code
stringlengths 1
1.05M
| repo_name
stringlengths 6
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stringlengths 3
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stringclasses 222
values | license
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from scripts.commons.Script import Script
script = Script(cpp_builder_unum=1) # Initialize: load config file, parse arguments, build cpp modules
a = script.args
if a.P: # penalty shootout
from agent.Agent_Penalty import Agent
else: # normal agent
from agent.Agent import Agent
# Args: Server IP, Agent Port, Monitor Port, Uniform No., Team name, Enable Log, Enable Draw, Wait for Server, is magmaFatProxy
if a.D: # debug mode
player = Agent(a.i, a.p, a.m, a.u, a.t, True, True, False, a.F)
else:
player = Agent(a.i, a.p, None, a.u, a.t, False, False, False, a.F)
while True:
player.think_and_send()
player.scom.receive()
|
2302_81918214/robocup3D
|
FCPCodebase-main/Run_Player.py
|
Python
|
agpl-3.0
| 648
|
def main():
from scripts.commons.Script import Script
script = Script() #Initialize: load config file, parse arguments, build cpp modules (warns the user about inconsistencies before choosing a test script)
# Allows using local version of StableBaselines3 (e.g. https://github.com/m-abr/Adaptive-Symmetry-Learning)
# place the 'stable-baselines3' folder in the parent directory of this project
import sys
from os.path import dirname, abspath, join
sys.path.insert( 0, join( dirname(dirname( abspath(__file__) )), "stable-baselines3") )
from scripts.commons.UI import UI
from os.path import isfile, join, realpath, dirname
from os import listdir, getcwd
from importlib import import_module
_cwd = realpath( join(getcwd(), dirname(__file__)))
gyms_path = _cwd + "/scripts/gyms/"
utils_path = _cwd + "/scripts/utils/"
exclusions = ["__init__.py"]
utils = sorted([f[:-3] for f in listdir(utils_path) if isfile(join(utils_path, f)) and f.endswith(".py") and f not in exclusions], key=lambda x: (x != "Server", x))
gyms = sorted([f[:-3] for f in listdir(gyms_path ) if isfile(join(gyms_path , f)) and f.endswith(".py") and f not in exclusions])
while True:
_, col_idx, col = UI.print_table( [utils, gyms], ["Demos & Tests & Utils","Gyms"], cols_per_title=[2,1], numbering=[True]*2, prompt='Choose script (ctrl+c to exit): ' )
is_gym = False
if col == 0:
chosen = ("scripts.utils." , utils[col_idx])
elif col == 1:
chosen = ("scripts.gyms." , gyms[col_idx])
is_gym = True
cls_name = chosen[1]
mod = import_module(chosen[0]+chosen[1])
'''
An imported script should not automatically execute the main code because:
- Multiprocessing methods, such as 'forkserver' and 'spawn', will execute the main code in every child
- The script can only be called once unless it is reloaded
'''
if not is_gym:
'''
Utils receive a script support object with user-defined arguments and useful methods
Each util must implement an execute() method, which may or may not return
'''
from world.commons.Draw import Draw
from agent.Base_Agent import Base_Agent
obj = getattr(mod,cls_name)(script)
try:
obj.execute() # Util may return normally or through KeyboardInterrupt
except KeyboardInterrupt:
print("\nctrl+c pressed, returning...\n")
Draw.clear_all() # clear all drawings
Base_Agent.terminate_all() # close all server sockets + monitor socket
script.players = [] # clear list of players created through batch commands
del obj
else:
'''
Gyms must implement a class Train() which is initialized with user-defined arguments and implements:
train() - method to run the optimization and save a new model
test(folder_dir, folder_name, model_file) - method to load an existing model and test it
'''
from scripts.commons.Train_Base import Train_Base
print("\nBefore using GYMS, make sure all server parameters are set correctly")
print("(sync mode should be 'On', real time should be 'Off', cheats should be 'On', ...)")
print("To change these parameters go to the previous menu, and select Server\n")
print("Also, GYMS start their own servers, so don't run any server manually")
while True:
try:
idx = UI.print_table([["Train","Test","Retrain"]], numbering=[True], prompt='Choose option (ctrl+c to return): ')[0]
except KeyboardInterrupt:
print()
break
if idx == 0:
mod.Train(script).train(dict())
else:
model_info = Train_Base.prompt_user_for_model()
if model_info is not None and idx == 1:
mod.Train(script).test(model_info)
elif model_info is not None:
mod.Train(script).train(model_info)
# allow child processes to bypass this file
if __name__ == "__main__":
try:
main()
except KeyboardInterrupt:
print("\nctrl+c pressed, exiting...")
exit()
|
2302_81918214/robocup3D
|
FCPCodebase-main/Run_Utils.py
|
Python
|
agpl-3.0
| 4,496
|
from agent.Base_Agent import Base_Agent
from math_ops.Math_Ops import Math_Ops as M
import math
import numpy as np
class Agent(Base_Agent):
def __init__(self, host:str, agent_port:int, monitor_port:int, unum:int,
team_name:str, enable_log, enable_draw, wait_for_server=True, is_fat_proxy=False) -> None:
# define robot type
robot_type = (0,1,1,1,2,3,3,3,4,4,4)[unum-1]
# Initialize base agent
# Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw, play mode correction, Wait for Server, Hear Callback
super().__init__(host, agent_port, monitor_port, unum, robot_type, team_name, enable_log, enable_draw, True, wait_for_server, None)
self.enable_draw = enable_draw
self.state = 0 # 0-Normal, 1-Getting up, 2-Kicking
self.kick_direction = 0
self.kick_distance = 0
self.fat_proxy_cmd = "" if is_fat_proxy else None
self.fat_proxy_walk = np.zeros(3) # filtered walk parameters for fat proxy
# DWJ-----------------------------------------------------------------------------------------------------------------
self.ball_3s_speed_pos = (0, 0, 0)
# DWJ -------------------------------------------------------------------------------------------------------------------
self.init_pos = ([-14.5,0],[-9,-5],[-9,0],[-9,5],[-5,-5],[-5,0],[-5,5],[-1,-6],[-1,-2.5],[-1,2.5],[-1,6])[unum-1] # initial formation
def beam(self, avoid_center_circle=False):
r = self.world.robot
pos = self.init_pos[:] # copy position list
self.state = 0
# Avoid center circle by moving the player back
if avoid_center_circle and np.linalg.norm(self.init_pos) < 2.5:
pos[0] = -2.3
if np.linalg.norm(pos - r.loc_head_position[:2]) > 0.1 or self.behavior.is_ready("Get_Up"):
self.scom.commit_beam(pos, M.vector_angle((-pos[0],-pos[1]))) # beam to initial position, face coordinate (0,0)
else:
if self.fat_proxy_cmd is None: # normal behavior
self.behavior.execute("Zero_Bent_Knees_Auto_Head")
else: # fat proxy behavior
self.fat_proxy_cmd += "(proxy dash 0 0 0)"
self.fat_proxy_walk = np.zeros(3) # reset fat proxy walk
# 移动
def move(self, target_2d=(0,0), orientation=None, is_orientation_absolute=True,
avoid_obstacles=True, priority_unums=[], is_aggressive=False, timeout=3000):
'''
Walk to target position
Parameters
----------
target_2d : array_like
2D target in absolute coordinates
orientation : float
absolute or relative orientation of torso, in degrees
set to None to go towards the target (is_orientation_absolute is ignored)
is_orientation_absolute : bool
True if orientation is relative to the field, False if relative to the robot's torso
avoid_obstacles : bool
True to avoid obstacles using path planning (maybe reduce timeout arg if this function is called multiple times per simulation cycle)
priority_unums : list
list of teammates to avoid (since their role is more important)
is_aggressive : bool
if True, safety margins are reduced for opponents
timeout : float
restrict path planning to a maximum duration (in microseconds)
'''
r = self.world.robot
if self.fat_proxy_cmd is not None: # fat proxy behavior
self.fat_proxy_move(target_2d, orientation, is_orientation_absolute) # ignore obstacles
return
if avoid_obstacles:
target_2d, _, distance_to_final_target = self.path_manager.get_path_to_target(
target_2d, priority_unums=priority_unums, is_aggressive=is_aggressive, timeout=timeout)
else:
distance_to_final_target = np.linalg.norm(target_2d - r.loc_head_position[:2])
self.behavior.execute("Walk", target_2d, True, orientation, is_orientation_absolute, distance_to_final_target) # Args: target, is_target_abs, ori, is_ori_abs, distance
def kick(self, kick_direction=None, kick_distance=None, abort=False, enable_pass_command=False):
'''
Walk to ball and kick
Parameters
----------
kick_direction : float
kick direction, in degrees, relative to the field
kick_distance : float
kick distance in meters
abort : bool
True to abort.
The method returns True upon successful abortion, which is immediate while the robot is aligning itself.
However, if the abortion is requested during the kick, it is delayed until the kick is completed.
avoid_pass_command : bool
When False, the pass command will be used when at least one opponent is near the ball
Returns
-------
finished : bool
Returns True if the behavior finished or was successfully aborted.
'''
if self.min_opponent_ball_dist < 1.45 and enable_pass_command:
self.scom.commit_pass_command()
self.kick_direction = self.kick_direction if kick_direction is None else kick_direction
self.kick_distance = self.kick_distance if kick_distance is None else kick_distance
if self.fat_proxy_cmd is None: # normal behavior
return self.behavior.execute("Basic_Kick", self.kick_direction, abort) # Basic_Kick has no kick distance control
else: # fat proxy behavior
return self.fat_proxy_kick()
def think_and_send(self):
# ========================= 基础数据准备 =========================
# 获取世界状态、机器人对象、当前时间等核心数据
w = self.world # 世界状态对象
r = self.world.robot # 当前机器人对象
my_head_pos_2d = r.loc_head_position[:2] # 机器人头部二维坐标（x,y）
my_ori = r.imu_torso_orientation # 机器人躯干朝向角度
ball_2d = w.ball_abs_pos[:2] # 足球绝对二维坐标
ball_vec = ball_2d - my_head_pos_2d # 球相对于头部的向量
ball_dir = M.vector_angle(ball_vec) # 球相对于头部的方向角（弧度）
ball_dist = np.linalg.norm(ball_vec) # 球与头部的直线距离
ball_sq_dist = ball_dist * ball_dist # 平方距离（用于快速比较）
ball_speed = np.linalg.norm(w.get_ball_abs_vel(6)[:2]) # 球速（取最近6ms速度）
behavior = self.behavior # 行为控制模块
goal_dir = M.target_abs_angle(ball_2d, (15.05,0)) # 球到对方球门的绝对角度
path_draw_options = self.path_manager.draw_options # 路径绘制参数
PM = w.play_mode # 当前比赛模式
PM_GROUP = w.play_mode_group # 比赛模式分组
# ========================= 预处理计算 =========================
# 预测球速降低到0.5m/s时的未来位置（用于策略决策）
slow_ball_pos = w.get_predicted_ball_pos(0.5)
# 计算队友到球的平方距离（排除无效队友）
teammates_ball_sq_dist = [
np.sum((p.state_abs_pos[:2] - slow_ball_pos) ** 2) # 计算平方距离
if p.state_last_update != 0 # 排除无状态更新的队友
and (w.time_local_ms - p.state_last_update <= 360 or p.is_self) # 状态有效性检查（360ms内）
and not p.state_fallen # 排除跌倒的队友
else 1000 # 无效情况设为最大距离
for p in w.teammates
]
# 同理计算对手到球距离（逻辑与队友计算相同）
opponents_ball_sq_dist = [...] # 此处省略重复逻辑
# 获取最小距离和对应球员编号
min_teammate_ball_sq_dist = min(teammates_ball_sq_dist)
self.min_teammate_ball_dist = math.sqrt(min_teammate_ball_sq_dist) # 转化为实际距离
self.min_opponent_ball_dist = math.sqrt(min(opponents_ball_sq_dist))
active_player_unum = teammates_ball_sq_dist.index(min_teammate_ball_sq_dist) + 1 # 获取最近队友编号
# ========================= 决策逻辑 =========================
if PM == w.M_GAME_OVER: # 比赛结束状态
pass # 无动作
elif PM_GROUP == w.MG_ACTIVE_BEAM: # 主动定位球状态
self.beam() # 执行定位球动作
elif PM_GROUP == w.MG_PASSIVE_BEAM:
self.beam(True) # 被动定位球（避开中圈）
elif self.state == 1 or (behavior.is_ready("Get_Up") and self.fat_proxy_cmd is None):
self.state = 0 if behavior.execute("Get_Up") else 1 # 摔倒后起身逻辑
elif PM == w.M_OUR_KICKOFF: # 我方开球
if r.unum == 9: # 特定球员执行开球
self.kick(120, 3) # 执行强力踢球（角度120度，力量3）
else:
self.move(self.init_pos, orientation=ball_dir) # 其他球员保持原位
elif active_player_unum != r.unum: # 非当前活跃球员
if r.unum == 1: # 守门员逻辑
if ball_dist < 0.5 and ball_speed < 0.1:
self.scom.commit_pass_command()
pass
else:
self.move(self.init_pos, orientation=ball_dir) # 保持门线位置
else: # 普通球员站位逻辑
# 动态调整阵型位置（基于球的位置）
new_x = max(0.5, (ball_2d[0]+15)/15) * (self.init_pos[0]+15) - 15
if self.min_teammate_ball_dist < self.min_opponent_ball_dist:
new_x = min(new_x + 3.5, 13) # 团队控球时前压
self.move((new_x, self.init_pos[1]), orientation=ball_dir, priority_unums=[active_player_unum])
else: # 当前是活跃球员
# 启用路径绘制（用于调试）
path_draw_options(enable_obstacles=True, enable_path=True, use_team_drawing_channel=True)
# 进攻逻辑（比赛进行中且球在前场时允许传球）
enable_pass_command = (PM == w.M_PLAY_ON and ball_2d[0]<6)
if PM == w.M_OUR_CORNER_KICK: # 角球战术
self.kick(-np.sign(ball_2d[1])*95, 5.5) # 斜传禁区
elif self.min_opponent_ball_dist + 0.5 < self.min_teammate_ball_dist: # 防守逻辑
if self.state == 2: # 中断当前动作
self.state = 0 if self.kick(abort=True) else 2
else: # 卡位防守（站在球与球门连线之间）
self.move(slow_ball_pos + M.normalize_vec((-16,0) - slow_ball_pos) * 0.2, is_aggressive=True)
else: # 常规进攻
self.state = 0 if self.kick(goal_dir, 9, False, enable_pass_command) else 2 # 朝球门方向射门
path_draw_options(enable_obstacles=False, enable_path=False) # 关闭调试绘制
# ========================= 指令传输 =========================
self.radio.broadcast() # 发送无线通信
if self.fat_proxy_cmd is None: # 正常指令模式
self.scom.commit_and_send(r.get_command()) # 发送动作指令
else: # 紧急指令模式
self.scom.commit_and_send(self.fat_proxy_cmd.encode())
self.fat_proxy_cmd = ""
# ========================= 调试辅助 =========================
if self.enable_draw: # 可视化调试信息
d = w.draw
if active_player_unum == r.unum: # 仅活跃球员显示预测轨迹
d.point(slow_ball_pos, 3, d.Color.pink, "status", False) # 显示预测球位
d.point(w.ball_2d_pred_pos[-1], 5, d.Color.pink, "status", False) # 最终预测位置
d.annotation((*my_head_pos_2d, 0.6), "I've got it!", d.Color.yellow, "status") # 状态提示
else:
d.clear("status") # 非活跃球员清空绘图
#--------------------------------------- Fat proxy auxiliary methods
def fat_proxy_kick(self):
w = self.world
r = self.world.robot
ball_2d = w.ball_abs_pos[:2]
my_head_pos_2d = r.loc_head_position[:2]
if np.linalg.norm(ball_2d - my_head_pos_2d) < 0.25:
# fat proxy kick arguments: power [0,10]; relative horizontal angle [-180,180]; vertical angle [0,70]
self.fat_proxy_cmd += f"(proxy kick 10 {M.normalize_deg( self.kick_direction - r.imu_torso_orientation ):.2f} 20)"
self.fat_proxy_walk = np.zeros(3) # reset fat proxy walk
return True
else:
self.fat_proxy_move(ball_2d-(-0.1,0), None, True) # ignore obstacles
return False
def fat_proxy_move(self, target_2d, orientation, is_orientation_absolute):
r = self.world.robot
target_dist = np.linalg.norm(target_2d - r.loc_head_position[:2])
target_dir = M.target_rel_angle(r.loc_head_position[:2], r.imu_torso_orientation, target_2d)
if target_dist > 0.1 and abs(target_dir) < 8:
self.fat_proxy_cmd += (f"(proxy dash {100} {0} {0})")
return
if target_dist < 0.1:
if is_orientation_absolute:
orientation = M.normalize_deg( orientation - r.imu_torso_orientation )
target_dir = np.clip(orientation, -60, 60)
self.fat_proxy_cmd += (f"(proxy dash {0} {0} {target_dir:.1f})")
else:
self.fat_proxy_cmd += (f"(proxy dash {20} {0} {target_dir:.1f})")
|
2302_81918214/robocup3D
|
FCPCodebase-main/agent/Agent.py
|
Python
|
agpl-3.0
| 13,899
|
from agent.Base_Agent import Base_Agent
from math_ops.Math_Ops import Math_Ops as M
import numpy as np
import random
class Agent(Base_Agent):
def __init__(self, host:str, agent_port:int, monitor_port:int, unum:int,
team_name:str, enable_log, enable_draw, wait_for_server=True, is_fat_proxy=False) -> None:
# define robot type
robot_type = 0 if unum == 1 else 4 # assume the goalkeeper uses uniform number 1 and the kicker uses any other number
# Initialize base agent
# Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw, play mode correction, Wait for Server, Hear Callback
super().__init__(host, agent_port, monitor_port, unum, robot_type, team_name, enable_log, enable_draw, False, wait_for_server, None)
self.enable_draw = enable_draw
self.state = 0 # 0-Normal, 1-Getting up, 2-Dive Left, 3-Dive Right, 4-Wait
self.kick_dir = 0 # kick direction
self.reset_kick = True # when True, a new random kick direction is generated
def think_and_send(self):
w = self.world
r = self.world.robot
my_head_pos_2d = r.loc_head_position[:2]
my_ori = r.imu_torso_orientation
ball_2d = w.ball_abs_pos[:2]
ball_vec = ball_2d - my_head_pos_2d
ball_dir = M.vector_angle(ball_vec)
ball_dist = np.linalg.norm(ball_vec)
ball_speed = np.linalg.norm(w.get_ball_abs_vel(6)[:2])
behavior = self.behavior
PM = w.play_mode
#--------------------------------------- 1. Decide action
if PM in [w.M_BEFORE_KICKOFF, w.M_THEIR_GOAL, w.M_OUR_GOAL]: # beam to initial position and wait
self.state = 0
self.reset_kick = True
pos = (-14,0) if r.unum == 1 else (4.9,0)
if np.linalg.norm(pos - r.loc_head_position[:2]) > 0.1 or behavior.is_ready("Get_Up"):
self.scom.commit_beam(pos, 0) # beam to initial position
else:
behavior.execute("Zero_Bent_Knees") # wait
elif self.state == 2: # dive left
self.state = 4 if behavior.execute("Dive_Left") else 2 # change state to wait after skill has finished
elif self.state == 3: # dive right
self.state = 4 if behavior.execute("Dive_Right") else 3 # change state to wait after skill has finished
elif self.state == 4: # wait (after diving or during opposing kick)
pass
elif self.state == 1 or behavior.is_ready("Get_Up"): # if getting up or fallen
self.state = 0 if behavior.execute("Get_Up") else 1 # return to normal state if get up behavior has finished
elif PM == w.M_OUR_KICKOFF and r.unum == 1 or PM == w.M_THEIR_KICKOFF and r.unum != 1:
self.state = 4 # wait until next beam
elif r.unum == 1: # goalkeeper
y_coordinate = np.clip(ball_2d[1], -1.1, 1.1)
behavior.execute("Walk", (-14,y_coordinate), True, 0, True, None) # Args: target, is_target_abs, ori, is_ori_abs, distance
if ball_2d[0] < -10:
self.state = 2 if ball_2d[1] > 0 else 3 # dive to defend
else: # kicker
if PM == w.M_OUR_KICKOFF and ball_2d[0] > 5: # check ball position to make sure I see it
if self.reset_kick:
self.kick_dir = random.choice([-7.5,7.5])
self.reset_kick = False
behavior.execute("Basic_Kick", self.kick_dir)
else:
behavior.execute("Zero_Bent_Knees") # wait
#--------------------------------------- 2. Broadcast
self.radio.broadcast()
#--------------------------------------- 3. Send to server
self.scom.commit_and_send( r.get_command() )
#---------------------- annotations for debugging
if self.enable_draw:
d = w.draw
if r.unum == 1:
d.annotation((*my_head_pos_2d, 0.8), "Goalkeeper" , d.Color.yellow, "status")
else:
d.annotation((*my_head_pos_2d, 0.8), "Kicker" , d.Color.yellow, "status")
if PM == w.M_OUR_KICKOFF: # draw arrow to indicate kick direction
d.arrow(ball_2d, ball_2d + 5*M.vector_from_angle(self.kick_dir), 0.4, 3, d.Color.cyan_light, "Target")
|
2302_81918214/robocup3D
|
FCPCodebase-main/agent/Agent_Penalty.py
|
Python
|
agpl-3.0
| 4,371
|
from abc import abstractmethod
from behaviors.Behavior import Behavior
from communication.Radio import Radio
from communication.Server_Comm import Server_Comm
from communication.World_Parser import World_Parser
from logs.Logger import Logger
from math_ops.Inverse_Kinematics import Inverse_Kinematics
from world.commons.Path_Manager import Path_Manager
from world.World import World
class Base_Agent():
all_agents = []
def __init__(self, host:str, agent_port:int, monitor_port:int, unum:int, robot_type:int, team_name:str, enable_log:bool=True,
enable_draw:bool=True, apply_play_mode_correction:bool=True, wait_for_server:bool=True, hear_callback=None) -> None:
self.radio = None # hear_message may be called during Server_Comm instantiation
self.logger = Logger(enable_log, f"{team_name}_{unum}")
self.world = World(robot_type, team_name, unum, apply_play_mode_correction, enable_draw, self.logger, host)
self.world_parser = World_Parser(self.world, self.hear_message if hear_callback is None else hear_callback)
self.scom = Server_Comm(host,agent_port,monitor_port,unum,robot_type,team_name,self.world_parser,self.world,Base_Agent.all_agents,wait_for_server)
self.inv_kinematics = Inverse_Kinematics(self.world.robot)
self.behavior = Behavior(self)
self.path_manager = Path_Manager(self.world)
self.radio = Radio(self.world, self.scom.commit_announcement)
self.behavior.create_behaviors()
Base_Agent.all_agents.append(self)
@abstractmethod
def think_and_send(self):
pass
def hear_message(self, msg:bytearray, direction, timestamp:float) -> None:
if direction != "self" and self.radio is not None:
self.radio.receive(msg)
def terminate(self):
# close shared monitor socket if this is the last agent on this thread
self.scom.close(close_monitor_socket=(len(Base_Agent.all_agents)==1))
Base_Agent.all_agents.remove(self)
@staticmethod
def terminate_all():
for o in Base_Agent.all_agents:
o.scom.close(True) # close shared monitor socket, if it exists
Base_Agent.all_agents = []
|
2302_81918214/robocup3D
|
FCPCodebase-main/agent/Base_Agent.py
|
Python
|
agpl-3.0
| 2,203
|
import numpy as np
class Behavior():
def __init__(self, base_agent) -> None:
from agent.Base_Agent import Base_Agent # for type hinting
self.base_agent : Base_Agent = base_agent
self.world = self.base_agent.world
self.state_behavior_name = None
self.state_behavior_init_ms = 0
self.previous_behavior = None
self.previous_behavior_duration = None
#Initialize standard behaviors
from behaviors.Poses import Poses
from behaviors.Slot_Engine import Slot_Engine
from behaviors.Head import Head
self.poses = Poses(self.world)
self.slot_engine = Slot_Engine(self.world)
self.head = Head(self.world)
def create_behaviors(self):
'''
Behaviors dictionary:
creation: key: ( description, auto_head, lambda reset[,a,b,c,..]: self.execute(...), lambda: self.is_ready(...) )
usage: key: ( description, auto_head, execute_func(reset, *args), is_ready_func )
'''
self.behaviors = self.poses.get_behaviors_callbacks()
self.behaviors.update(self.slot_engine.get_behaviors_callbacks())
self.behaviors.update(self.get_custom_callbacks())
def get_custom_callbacks(self):
'''
Searching custom behaviors could be implemented automatically
However, for code distribution, loading code dynamically is not ideal (unless we load byte code or some other import solution)
Currently, adding custom behaviors is a manual process:
1. Add import statement below
2. Add class to 'classes' list
'''
# Declaration of behaviors
from behaviors.custom.Basic_Kick.Basic_Kick import Basic_Kick
from behaviors.custom.Dribble.Dribble import Dribble
from behaviors.custom.Fall.Fall import Fall
from behaviors.custom.Get_Up.Get_Up import Get_Up
from behaviors.custom.Step.Step import Step
from behaviors.custom.Walk.Walk import Walk
classes = [Basic_Kick,Dribble,Fall,Get_Up,Step,Walk]
'''---- End of manual declarations ----'''
# Prepare callbacks
self.objects = {cls.__name__ : cls(self.base_agent) for cls in classes}
return {name: (o.description,o.auto_head,
lambda reset,*args,o=o: o.execute(reset,*args), lambda *args,o=o: o.is_ready(*args)) for name, o in self.objects.items()}
def get_custom_behavior_object(self, name):
''' Get unique object from class "name" ("name" must represent a custom behavior) '''
assert name in self.objects, f"There is no custom behavior called {name}"
return self.objects[name]
def get_all_behaviors(self):
''' Get name and description of all behaviors '''
return [ key for key in self.behaviors ], [ val[0] for val in self.behaviors.values() ]
def get_current(self):
''' Get name and duration (in seconds) of current behavior '''
duration = (self.world.time_local_ms - self.state_behavior_init_ms) / 1000.0
return self.state_behavior_name, duration
def get_previous(self):
''' Get name and duration (in seconds) of previous behavior '''
return self.previous_behavior, self.previous_behavior_duration
def force_reset(self):
''' Force reset next executed behavior '''
self.state_behavior_name = None
def execute(self, name, *args) -> bool:
'''
Execute one step of behavior `name` with arguments `*args`
- Automatically resets behavior on first call
- Call get_current() to get the current behavior (and its duration)
Returns
-------
finished : bool
True if behavior has finished
'''
assert name in self.behaviors, f"Behavior {name} does not exist!"
# Check if transitioning from other behavior
reset = bool(self.state_behavior_name != name)
if reset:
if self.state_behavior_name is not None:
self.previous_behavior = self.state_behavior_name # Previous behavior was interrupted (did not finish)
self.previous_behavior_duration = (self.world.time_local_ms - self.state_behavior_init_ms) / 1000.0
self.state_behavior_name = name
self.state_behavior_init_ms = self.world.time_local_ms
# Control head orientation if behavior allows it
if self.behaviors[name][1]:
self.head.execute()
# Execute behavior
if not self.behaviors[name][2](reset,*args):
return False
# The behavior has finished
self.previous_behavior = self.state_behavior_name # Store current behavior name
self.state_behavior_name = None
return True
def execute_sub_behavior(self, name, reset, *args):
'''
Execute one step of behavior `name` with arguments `*args`
Useful for custom behaviors that call other behaviors
- Behavior reset is performed manually
- Calling get_current() will return the main behavior (not the sub behavior)
- Poses ignore the reset argument
Returns
-------
finished : bool
True if behavior has finished
'''
assert name in self.behaviors, f"Behavior {name} does not exist!"
# Control head orientation if behavior allows it
if self.behaviors[name][1]:
self.head.execute()
# Execute behavior
return self.behaviors[name][2](reset,*args)
def execute_to_completion(self, name, *args):
'''
Execute steps and communicate with server until completion
- Slot behaviors indicate that the behavior has finished when sending the last command (which is promptly sent)
- Poses are finished when the server returns the desired robot state (so the last command is irrelevant)
- For custom behaviors, we assume the same logic, and so, the last command is ignored
Notes
-----
- Before exiting, the `Robot.joints_target_speed` array is reset to avoid polluting the next command
- For poses and custom behaviors that indicate a finished behavior on the 1st call, nothing is committed or sent
- Warning: this function may get stuck in an infinite loop if the behavior never ends
'''
r = self.world.robot
skip_last = name not in self.slot_engine.behaviors
while True:
done = self.execute(name, *args)
if done and skip_last: break # Exit here if last command is irrelevant
self.base_agent.scom.commit_and_send( r.get_command() )
self.base_agent.scom.receive()
if done: break # Exit here if last command is part of the behavior
# reset to avoid polluting the next command
r.joints_target_speed = np.zeros_like(r.joints_target_speed)
def is_ready(self, name, *args) -> bool:
''' Checks if behavior is ready to start under current game/robot conditions '''
assert name in self.behaviors, f"Behavior {name} does not exist!"
return self.behaviors[name][3](*args)
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/Behavior.py
|
Python
|
agpl-3.0
| 7,213
|
from math_ops.Math_Ops import Math_Ops as M
from world.World import World
import numpy as np
class Head():
FIELD_FLAGS = World.FLAGS_CORNERS_POS + World.FLAGS_POSTS_POS
HEAD_PITCH = -35
def __init__(self, world : World) -> None:
self.world = world
self.look_left = True
self.state = 0
def execute(self):
'''
Try to compute best head orientation if possible, otherwise look around
state:
0 - Adjust position - ball is in FOV and robot can self-locate
1..TIMEOUT-1 - Guided search - attempt to use recent visual/radio information to guide the search
TIMEOUT - Random search - look around (default mode after guided search fails by timeout)
'''
TIMEOUT = 30
w = self.world
r = w.robot
can_self_locate = r.loc_last_update > w.time_local_ms - w.VISUALSTEP_MS
#--------------------------------------- A. Ball is in FOV and robot can self-locate
if w.ball_last_seen > w.time_local_ms - w.VISUALSTEP_MS: # ball is in FOV
if can_self_locate:
best_dir = self.compute_best_direction(can_self_locate, use_ball_from_vision=True)
self.state = 0
elif self.state < TIMEOUT:
#--------------------------------------- B. Ball is in FOV but robot cannot currently self-locate
best_dir = self.compute_best_direction(can_self_locate, use_ball_from_vision=True)
self.state += 1 # change to guided search and increment time
elif self.state < TIMEOUT:
#--------------------------------------- C. Ball is not in FOV
best_dir = self.compute_best_direction(can_self_locate)
self.state += 1 # change to guided search and increment time
if self.state == TIMEOUT: # Random search
if w.ball_last_seen > w.time_local_ms - w.VISUALSTEP_MS: # Ball is in FOV (search 45 deg to both sides of the ball)
ball_dir = M.vector_angle(w.ball_rel_torso_cart_pos[:2])
targ = np.clip(ball_dir + (45 if self.look_left else -45), -119, 119)
else: # Ball is not in FOV (search 119 deg to both sides)
targ = 119 if self.look_left else -119
if r.set_joints_target_position_direct([0,1], np.array([targ,Head.HEAD_PITCH]), False) <= 0:
self.look_left = not self.look_left
else: # Adjust position or guided search
r.set_joints_target_position_direct([0,1], np.array([best_dir,Head.HEAD_PITCH]), False)
def compute_best_direction(self, can_self_locate, use_ball_from_vision=False):
FOV_MARGIN = 15 # safety margin, avoid margin horizontally
SAFE_RANGE = 120 - FOV_MARGIN*2
HALF_RANGE = SAFE_RANGE / 2
w = self.world
r = w.robot
if use_ball_from_vision:
ball_2d_dist = np.linalg.norm(w.ball_rel_torso_cart_pos[:2])
else:
ball_2d_dist = np.linalg.norm(w.ball_abs_pos[:2]-r.loc_head_position[:2])
if ball_2d_dist > 0.12:
if use_ball_from_vision:
ball_dir = M.vector_angle(w.ball_rel_torso_cart_pos[:2])
else:
ball_dir = M.target_rel_angle(r.loc_head_position, r.imu_torso_orientation, w.ball_abs_pos)
else: # ball is very close to robot
ball_dir = 0
flags_diff = dict()
# iterate flags
for f in Head.FIELD_FLAGS:
flag_dir = M.target_rel_angle(r.loc_head_position, r.imu_torso_orientation, f)
diff = M.normalize_deg( flag_dir - ball_dir )
if abs(diff) < HALF_RANGE and can_self_locate:
return ball_dir # return ball direction if robot can self-locate
flags_diff[f] = diff
closest_flag = min( flags_diff, key=lambda k: abs(flags_diff[k]) )
closest_diff = flags_diff[closest_flag]
if can_self_locate: # at this point, if it can self-locate, then abs(closest_diff) > HALF_RANGE
# return position that centers the ball as much as possible in the FOV, including the nearest flag if possible
final_diff = min( abs(closest_diff) - HALF_RANGE, SAFE_RANGE ) * np.sign(closest_diff)
else:
# position that centers the flag as much as possible, until it is seen, while keeping the ball in the FOV
final_diff = np.clip( closest_diff, -SAFE_RANGE, SAFE_RANGE )
# saturate instead of normalizing angle to avoid a complete neck rotation
return np.clip(ball_dir + final_diff, -119, 119)
return M.normalize_deg( ball_dir + final_diff )
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/Head.py
|
Python
|
agpl-3.0
| 4,730
|
'''
Pose - angles in degrees for the specified joints
Note: toes positions are ignored by robots that have no toes
Poses may control all joints or just a subgroup defined by the "indices" variable
'''
import numpy as np
from world.World import World
class Poses():
def __init__(self, world : World) -> None:
self.world = world
self.tolerance = 0.05 # angle error tolerance to consider that behavior is finished
'''
Instruction to add new pose:
1. add new entry to the following dictionary, using a unique behavior name
2. that's it
'''
self.poses = {
"Zero":(
"Neutral pose, including head", # description
False, # disable automatic head orientation
np.array([0,1,2,3,4,5,6,7,8,9,10,11,12,13, 14, 15,16,17,18,19,20,21,22,23]), # indices
np.array([0,0,0,0,0,0,0,0,0,0, 0, 0, 0, 0,-90,-90, 0, 0,90,90, 0, 0, 0, 0]) # values
),
"Zero_Legacy":(
"Neutral pose, including head, elbows cause collision (legacy)", # description
False, # disable automatic head orientation
np.array([0,1,2,3,4,5,6,7,8,9,10,11,12,13, 14, 15,16,17,18,19,20,21,22,23]), # indices
np.array([0,0,0,0,0,0,0,0,0,0, 0, 0, 0, 0,-90,-90, 0, 0, 0, 0, 0, 0, 0, 0]) # values
),
"Zero_Bent_Knees":(
"Neutral pose, including head, bent knees", # description
False, # disable automatic head orientation
np.array([0,1,2,3,4,5,6, 7, 8, 9,10,11,12,13, 14, 15,16,17,18,19,20,21,22,23]), # indices
np.array([0,0,0,0,0,0,30,30,-60,-60,30,30, 0, 0,-90,-90, 0, 0,90,90, 0, 0, 0, 0]) # values
),
"Zero_Bent_Knees_Auto_Head":(
"Neutral pose, automatic head, bent knees", # description
True, # enable automatic head orientation
np.array([2,3,4,5,6, 7, 8, 9,10,11,12,13, 14, 15,16,17,18,19,20,21,22,23]), # indices
np.array([0,0,0,0,30,30,-60,-60,30,30, 0, 0,-90,-90, 0, 0,90,90, 0, 0, 0, 0]) # values
),
"Fall_Back":(
"Incline feet to fall back", # description
True, # enable automatic head orientation
np.array([ 10, 11]), # indices
np.array([-20,-20]) # values
),
"Fall_Front":(
"Incline feet to fall forward", # description
True, # enable automatic head orientation
np.array([10,11]), # indices
np.array([45,45]) # values
),
"Fall_Left":(
"Incline legs to fall to left", # description
True, # enable automatic head orientation
np.array([ 4, 5]), # indices
np.array([-20,20]) # values
),
"Fall_Right":(
"Incline legs to fall to right", # description
True, # enable automatic head orientation
np.array([ 4, 5]), # indices
np.array([20,-20]) # values
),
}
# Remove toes if not robot 4
if world.robot.type != 4:
for key, val in self.poses.items():
idxs = np.where(val[2] >= 22)[0] # search for joint 22 & 23
if len(idxs) > 0:
self.poses[key] = (val[0], val[1], np.delete(val[2],idxs), np.delete(val[3],idxs)) # remove those joints
def get_behaviors_callbacks(self):
'''
Returns callbacks for each pose behavior (used internally)
Implementation note:
--------------------
Using dummy default parameters because lambda expression will remember the scope and var name.
In the loop, the scope does not change, nor does the var name.
However, default parameters are evaluated when the lambda is defined.
'''
return {key: (val[0], val[1], lambda reset, key=key: self.execute(key), lambda: True) for key, val in self.poses.items()}
def execute(self,name) -> bool:
_, _, indices, values = self.poses[name]
remaining_steps = self.world.robot.set_joints_target_position_direct(indices,values,True,tolerance=self.tolerance)
return bool(remaining_steps == -1)
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/Poses.py
|
Python
|
agpl-3.0
| 4,396
|
from math_ops.Math_Ops import Math_Ops as M
from os import listdir
from os.path import isfile, join
from world.World import World
import numpy as np
import xml.etree.ElementTree as xmlp
class Slot_Engine():
def __init__(self, world : World) -> None:
self.world = world
self.state_slot_number = 0
self.state_slot_start_time = 0
self.state_slot_start_angles = None
self.state_init_zero = True
# ------------- Parse slot behaviors
dir = M.get_active_directory("/behaviors/slot/")
common_dir = f"{dir}common/"
files = [(f,join(common_dir, f)) for f in listdir(common_dir) if isfile(join(common_dir, f)) and f.endswith(".xml")]
robot_dir = f"{dir}r{world.robot.type}"
files += [(f,join(robot_dir, f)) for f in listdir(robot_dir) if isfile(join(robot_dir, f)) and f.endswith(".xml")]
self.behaviors = dict()
self.descriptions = dict()
self.auto_head_flags = dict()
for fname, file in files:
robot_xml_root = xmlp.parse(file).getroot()
slots = []
bname = fname[:-4] # remove extension ".xml"
for xml_slot in robot_xml_root:
assert xml_slot.tag == 'slot', f"Unexpected XML element in slot behavior {fname}: '{xml_slot.tag}'"
indices, angles = [],[]
for action in xml_slot:
indices.append( int(action.attrib['id']) )
angles.append( float(action.attrib['angle']) )
delta_ms = float(xml_slot.attrib['delta']) * 1000
assert delta_ms > 0, f"Invalid delta <=0 found in Slot Behavior {fname}"
slots.append((delta_ms, indices, angles))
assert bname not in self.behaviors, f"Found at least 2 slot behaviors with same name: {fname}"
self.descriptions[bname] = robot_xml_root.attrib["description"] if "description" in robot_xml_root.attrib else bname
self.auto_head_flags[bname] = (robot_xml_root.attrib["auto_head"] == "1")
self.behaviors[bname] = slots
def get_behaviors_callbacks(self):
'''
Returns callbacks for each slot behavior (used internally)
Implementation note:
--------------------
Using dummy default parameters because lambda expression will remember the scope and var name.
In the loop, the scope does not change, nor does the var name.
However, default parameters are evaluated when the lambda is defined.
'''
return {key: (self.descriptions[key],self.auto_head_flags[key],
lambda reset,key=key: self.execute(key,reset), lambda key=key: self.is_ready(key)) for key in self.behaviors}
def is_ready(self,name) -> bool:
return True
def reset(self, name):
''' Initialize/Reset slot behavior '''
self.state_slot_number = 0
self.state_slot_start_time_ms = self.world.time_local_ms
self.state_slot_start_angles = np.copy(self.world.robot.joints_position)
assert name in self.behaviors, f"Requested slot behavior does not exist: {name}"
def execute(self,name,reset) -> bool:
''' Execute one step '''
if reset: self.reset(name)
elapsed_ms = self.world.time_local_ms - self.state_slot_start_time_ms
delta_ms, indices, angles = self.behaviors[name][self.state_slot_number]
# Check slot progression
if elapsed_ms >= delta_ms:
self.state_slot_start_angles[indices] = angles #update start angles based on last target
# Prevent 2 rare scenarios:
# 1 - this function is called after the behavior is finished & reset==False
# 2 - we are in the last slot, syncmode is not active, and we lost the last step
if self.state_slot_number+1 == len(self.behaviors[name]):
return True # So, the return indicates a finished behavior until a reset is sent via the arguments
self.state_slot_number += 1
elapsed_ms = 0
self.state_slot_start_time_ms = self.world.time_local_ms
delta_ms, indices, angles = self.behaviors[name][self.state_slot_number]
# Execute
progress = (elapsed_ms+20) / delta_ms
target = (angles - self.state_slot_start_angles[indices]) * progress + self.state_slot_start_angles[indices]
self.world.robot.set_joints_target_position_direct(indices,target,False)
# Return True if finished (this is the last step)
return bool(elapsed_ms+20 >= delta_ms and self.state_slot_number + 1 == len(self.behaviors[name])) # true if next step (now+20ms) is out of bounds
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/Slot_Engine.py
|
Python
|
agpl-3.0
| 4,737
|
from agent.Base_Agent import Base_Agent
from behaviors.custom.Step.Step_Generator import Step_Generator
from math_ops.Math_Ops import Math_Ops as M
class Basic_Kick():
def __init__(self, base_agent : Base_Agent) -> None:
self.behavior = base_agent.behavior
self.path_manager = base_agent.path_manager
self.world = base_agent.world
self.description = "Walk to ball and perform a basic kick"
self.auto_head = True
r_type = self.world.robot.type
self.bias_dir = [22,29,26,29,22][self.world.robot.type]
self.ball_x_limits = ((0.19,0.215), (0.2,0.22), (0.19,0.22), (0.2,0.215), (0.2,0.215))[r_type]
self.ball_y_limits = ((-0.115,-0.1), (-0.125,-0.095), (-0.12,-0.1), (-0.13,-0.105), (-0.09,-0.06))[r_type]
self.ball_x_center = (self.ball_x_limits[0] + self.ball_x_limits[1])/2
self.ball_y_center = (self.ball_y_limits[0] + self.ball_y_limits[1])/2
def execute(self,reset, direction, abort=False) -> bool: # You can add more arguments
'''
Parameters
----------
direction : float
kick direction relative to field, in degrees
abort : bool
True to abort.
The method returns True upon successful abortion, which is immediate while the robot is aligning itself.
However, if the abortion is requested during the kick, it is delayed until the kick is completed.
'''
w = self.world
r = self.world.robot
b = w.ball_rel_torso_cart_pos
t = w.time_local_ms
gait : Step_Generator = self.behavior.get_custom_behavior_object("Walk").env.step_generator
if reset:
self.phase = 0
self.reset_time = t
if self.phase == 0:
biased_dir = M.normalize_deg(direction + self.bias_dir) # add bias to rectify direction
ang_diff = abs(M.normalize_deg( biased_dir - r.loc_torso_orientation )) # the reset was learned with loc, not IMU
next_pos, next_ori, dist_to_final_target = self.path_manager.get_path_to_ball(
x_ori=biased_dir, x_dev=-self.ball_x_center, y_dev=-self.ball_y_center, torso_ori=biased_dir)
if (w.ball_last_seen > t - w.VISUALSTEP_MS and ang_diff < 5 and # ball is visible & aligned
self.ball_x_limits[0] < b[0] < self.ball_x_limits[1] and # ball is in kick area (x)
self.ball_y_limits[0] < b[1] < self.ball_y_limits[1] and # ball is in kick area (y)
t - w.ball_abs_pos_last_update < 100 and # ball absolute location is recent
dist_to_final_target < 0.03 and # if absolute ball position is updated
not gait.state_is_left_active and gait.state_current_ts == 2 and # walk gait phase is adequate
t - self.reset_time > 500): # to avoid kicking immediately without preparation & stability
self.phase += 1
return self.behavior.execute_sub_behavior("Kick_Motion", True)
else:
dist = max(0.07, dist_to_final_target)
reset_walk = reset and self.behavior.previous_behavior != "Walk" # reset walk if it wasn't the previous behavior
self.behavior.execute_sub_behavior("Walk", reset_walk, next_pos, True, next_ori, True, dist) # target, is_target_abs, ori, is_ori_abs, distance
return abort # abort only if self.phase == 0
else: # define kick parameters and execute
return self.behavior.execute_sub_behavior("Kick_Motion", False)
def is_ready(self) -> any: # You can add more arguments
''' Returns True if this behavior is ready to start/continue under current game/robot conditions '''
return True
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/custom/Basic_Kick/Basic_Kick.py
|
Python
|
agpl-3.0
| 3,874
|
from agent.Base_Agent import Base_Agent
from behaviors.custom.Dribble.Env import Env
from math_ops.Math_Ops import Math_Ops as M
from math_ops.Neural_Network import run_mlp
import numpy as np
import pickle
class Dribble():
def __init__(self, base_agent : Base_Agent) -> None:
self.behavior = base_agent.behavior
self.path_manager = base_agent.path_manager
self.world = base_agent.world
self.description = "RL dribble"
self.auto_head = True
self.env = Env(base_agent, 0.9 if self.world.robot.type == 3 else 1.2)
with open(M.get_active_directory([
"/behaviors/custom/Dribble/dribble_R0.pkl",
"/behaviors/custom/Dribble/dribble_R1.pkl",
"/behaviors/custom/Dribble/dribble_R2.pkl",
"/behaviors/custom/Dribble/dribble_R3.pkl",
"/behaviors/custom/Dribble/dribble_R4.pkl"
][self.world.robot.type]), 'rb') as f:
self.model = pickle.load(f)
def define_approach_orientation(self):
w = self.world
b = w.ball_abs_pos[:2]
me = w.robot.loc_head_position[:2]
self.approach_orientation = None
MARGIN = 0.8 # safety margin (if ball is near the field limits by this amount, the approach orientation is considered)
M90 = 90/MARGIN # auxiliary variable
DEV = 25 # when standing on top of sidelines or endlines, the approach direction deviates from that line by this amount
MDEV = (90+DEV)/MARGIN # auxiliary variable
a1 = -180 # angle range start (counterclockwise rotation)
a2 = 180 # angle range end (counterclockwise rotation)
if b[1] < -10 + MARGIN:
if b[0] < -15 + MARGIN:
a1 = DEV - M90 * (b[1]+10)
a2 = 90 - DEV + M90 * (b[0]+15)
elif b[0] > 15 - MARGIN:
a1 = 90 + DEV - M90 * (15-b[0])
a2 = 180 - DEV + M90 * (b[1]+10)
else:
a1 = DEV - MDEV * (b[1]+10)
a2 = 180 - DEV + MDEV * (b[1]+10)
elif b[1] > 10 - MARGIN:
if b[0] < -15 + MARGIN:
a1 = -90 + DEV - M90 * (b[0]+15)
a2 = -DEV + M90 * (10-b[1])
elif b[0] > 15 - MARGIN:
a1 = 180 + DEV - M90 * (10-b[1])
a2 = 270 - DEV + M90 * (15-b[0])
else:
a1 = -180 + DEV - MDEV * (10-b[1])
a2 = -DEV + MDEV * (10-b[1])
elif b[0] < -15 + MARGIN:
a1 = -90 + DEV - MDEV * (b[0]+15)
a2 = 90 - DEV + MDEV * (b[0]+15)
elif b[0] > 15 - MARGIN and abs(b[1]) > 1.2:
a1 = 90 + DEV - MDEV * (15-b[0])
a2 = 270 - DEV + MDEV * (15-b[0])
cad = M.vector_angle(b - me) # current approach direction
a1 = M.normalize_deg(a1)
a2 = M.normalize_deg(a2)
if a1<a2:
if a1 <= cad <= a2:
return # current approach orientation is within accepted range
else:
if a1 <= cad or cad <= a2:
return # current approach orientation is within accepted range
a1_diff = abs(M.normalize_deg(a1 - cad))
a2_diff = abs(M.normalize_deg(a2 - cad))
self.approach_orientation = a1 if a1_diff < a2_diff else a2 # fixed normalized orientation
def execute(self, reset, orientation, is_orientation_absolute, speed=1, stop=False):
'''
Parameters
----------
orientation : float
absolute or relative orientation of torso (relative to imu_torso_orientation), in degrees
set to None to dribble towards the opponent's goal (is_orientation_absolute is ignored)
is_orientation_absolute : bool
True if orientation is relative to the field, False if relative to the robot's torso
speed : float
speed from 0 to 1 (scale is not linear)
stop : bool
return True immediately if walking, wind down if dribbling, and return True when possible
'''
w = self.world
r = self.world.robot
me = r.loc_head_position[:2]
b = w.ball_abs_pos[:2]
b_rel = w.ball_rel_torso_cart_pos[:2]
b_dist = np.linalg.norm(b-me)
behavior = self.behavior
reset_dribble = False
lost_ball = (w.ball_last_seen <= w.time_local_ms - w.VISUALSTEP_MS) or np.linalg.norm(b_rel)>0.4
if reset:
self.phase = 0
if behavior.previous_behavior == "Push_RL" and 0<b_rel[0]<0.25 and abs(b_rel[1])<0.07:
self.phase = 1
reset_dribble = True
if self.phase == 0: # walk to ball
reset_walk = reset and behavior.previous_behavior != "Walk" and behavior.previous_behavior != "Push_RL" # reset walk if it wasn't the previous behavior
#------------------------ 1. Decide if a better approach orientation is needed (when ball is nearly out of bounds)
if reset or b_dist > 0.4: # stop defining orientation after getting near the ball to avoid noise
self.define_approach_orientation()
#------------------------ 2A. A better approach orientation is needed (ball is almost out of bounds)
if self.approach_orientation is not None:
next_pos, next_ori, dist_to_final_target = self.path_manager.get_path_to_ball(
x_ori=self.approach_orientation, x_dev=-0.24, torso_ori=self.approach_orientation, safety_margin=0.4)
if b_rel[0]<0.26 and b_rel[0]>0.18 and abs(b_rel[1])<0.04 and w.ball_is_visible: # ready to start dribble
self.phase += 1
reset_dribble = True
else:
dist = max(0.08, dist_to_final_target*0.7)
behavior.execute_sub_behavior("Walk", reset_walk, next_pos, True, next_ori, True, dist) # target, is_target_abs, ori, is_ori_abs, distance
#------------------------ 2B. A better approach orientation is not needed but the robot cannot see the ball
elif w.time_local_ms - w.ball_last_seen > 200: # walk to absolute target if ball was not seen
abs_ori = M.vector_angle( b - me )
behavior.execute_sub_behavior("Walk", reset_walk, b, True, abs_ori, True, None) # target, is_target_abs, ori, is_ori_abs, distance
#------------------------ 2C. A better approach orientation is not needed and the robot can see the ball
else: # walk to relative target
if 0.18<b_rel[0]<0.25 and abs(b_rel[1])<0.05 and w.ball_is_visible: # ready to start dribble
self.phase += 1
reset_dribble = True
else:
rel_target = b_rel+(-0.23,0) # relative target is a circle (center: ball, radius:0.23m)
rel_ori = M.vector_angle(b_rel) # relative orientation of ball, NOT the target!
dist = max(0.08, np.linalg.norm(rel_target)*0.7) # slow approach
behavior.execute_sub_behavior("Walk", reset_walk, rel_target, False, rel_ori, False, dist) # target, is_target_abs, ori, is_ori_abs, distance
if stop:
return True
if self.phase == 1 and (stop or (b_dist > 0.5 and lost_ball)): # go back to walking
self.phase += 1
elif self.phase == 1: # dribble
#------------------------ 1. Define dribble parameters
self.env.dribble_speed = speed
# Relative orientation values are decreased to avoid overshoot
if orientation is None:
if b[0] < 0: # dribble to the sides
if b[1] > 0:
dribble_target = (15,5)
else:
dribble_target = (15,-5)
else:
dribble_target = None # go to goal
self.env.dribble_rel_orientation = self.path_manager.get_dribble_path(optional_2d_target=dribble_target)[1]
elif is_orientation_absolute:
self.env.dribble_rel_orientation = M.normalize_deg( orientation - r.imu_torso_orientation )
else:
self.env.dribble_rel_orientation = float(orientation) # copy if numpy float
#------------------------ 2. Execute behavior
obs = self.env.observe(reset_dribble)
action = run_mlp(obs, self.model)
self.env.execute(action)
# wind down dribbling, and then reset phase
if self.phase > 1:
WIND_DOWN_STEPS = 60
#------------------------ 1. Define dribble wind down parameters
self.env.dribble_speed = 1 - self.phase/WIND_DOWN_STEPS
self.env.dribble_rel_orientation = 0
#------------------------ 2. Execute behavior
obs = self.env.observe(reset_dribble, virtual_ball=True)
action = run_mlp(obs, self.model)
self.env.execute(action)
#------------------------ 3. Reset behavior
self.phase += 1
if self.phase >= WIND_DOWN_STEPS - 5:
self.phase = 0
return True
return False
def is_ready(self):
''' Returns True if this behavior is ready to start/continue under current game/robot conditions '''
return True
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/custom/Dribble/Dribble.py
|
Python
|
agpl-3.0
| 9,453
|
from agent.Base_Agent import Base_Agent
from behaviors.custom.Step.Step_Generator import Step_Generator
from math_ops.Math_Ops import Math_Ops as M
import math
import numpy as np
class Env():
def __init__(self, base_agent : Base_Agent, step_width) -> None:
self.world = base_agent.world
self.ik = base_agent.inv_kinematics
# State space
self.obs = np.zeros(76, np.float32)
# Step behavior defaults
self.STEP_DUR = 8
self.STEP_Z_SPAN = 0.02
self.STEP_Z_MAX = 0.70
# IK
r = self.world.robot
nao_specs = self.ik.NAO_SPECS
self.leg_length = nao_specs[1] + nao_specs[3] # upper leg height + lower leg height
feet_y_dev = nao_specs[0] * step_width # wider step
sample_time = r.STEPTIME
max_ankle_z = nao_specs[5]
self.step_generator = Step_Generator(feet_y_dev, sample_time, max_ankle_z)
self.DEFAULT_ARMS = np.array([-90,-90,8,8,90,90,70,70],np.float32)
self.dribble_rel_orientation = None # relative to imu_torso_orientation (in degrees)
self.dribble_speed = 1
def observe(self, init=False, virtual_ball=False):
w = self.world
r = self.world.robot
if init: # reset variables
self.step_counter = 0
self.act = np.zeros(16, np.float32) # memory variable
# index observation naive normalization
self.obs[0] = min(self.step_counter,12*8) /100 # simple counter: 0,1,2,3...
self.obs[1] = r.loc_head_z *3 # z coordinate (torso)
self.obs[2] = r.loc_head_z_vel /2 # z velocity (torso)
self.obs[3] = r.imu_torso_roll /15 # absolute torso roll in deg
self.obs[4] = r.imu_torso_pitch /15 # absolute torso pitch in deg
self.obs[5:8] = r.gyro /100 # gyroscope
self.obs[8:11] = r.acc /10 # accelerometer
self.obs[11:17] = r.frp.get('lf', np.zeros(6)) * (10,10,10,0.01,0.01,0.01) # left foot: relative point of origin (p) and force vector (f) -> (px,py,pz,fx,fy,fz)*
self.obs[17:23] = r.frp.get('rf', np.zeros(6)) * (10,10,10,0.01,0.01,0.01) # right foot: relative point of origin (p) and force vector (f) -> (px,py,pz,fx,fy,fz)*
# *if foot is not touching the ground, then (px=0,py=0,pz=0,fx=0,fy=0,fz=0)
self.obs[23:43] = r.joints_position[2:22] /100 # position of all joints except head & toes (for robot type 4)
self.obs[43:63] = r.joints_speed[2:22] /6.1395 # speed of all joints except head & toes (for robot type 4)
'''
Expected observations for walking state:
Time step R 0 1 2 3 4 5 6 7 0
Progress 1 0 .14 .28 .43 .57 .71 .86 1 0
Left leg active T F F F F F F F F T
'''
if init: # the walking parameters refer to the last parameters in effect (after a reset, they are pointless)
self.obs[63] = 1 # step progress
self.obs[64] = 1 # 1 if left leg is active
self.obs[65] = 0 # 1 if right leg is active
self.obs[66] = 0
else:
self.obs[63] = self.step_generator.external_progress # step progress
self.obs[64] = float(self.step_generator.state_is_left_active) # 1 if left leg is active
self.obs[65] = float(not self.step_generator.state_is_left_active) # 1 if right leg is active
self.obs[66] = math.sin(self.step_generator.state_current_ts / self.step_generator.ts_per_step * math.pi)
# Ball
ball_rel_hip_center = self.ik.torso_to_hip_transform(w.ball_rel_torso_cart_pos)
ball_dist_hip_center = np.linalg.norm( ball_rel_hip_center )
if init:
self.obs[67:70] = (0,0,0) # Initial velocity is 0
elif w.ball_is_visible:
self.obs[67:70] = (ball_rel_hip_center - self.obs[70:73]) * 10 # Ball velocity, relative to ankle's midpoint
self.obs[70:73] = ball_rel_hip_center # Ball position, relative to hip
self.obs[73] = ball_dist_hip_center * 2
if virtual_ball: # simulate the ball between the robot's feet
self.obs[67:74] = (0,0,0,0.05,0,-0.175,0.36)
'''
Create internal target with a smoother variation
'''
MAX_ROTATION_DIFF = 20 # max difference (degrees) per visual step
MAX_ROTATION_DIST = 80
if init:
self.internal_rel_orientation = 0
self.internal_target_vel = 0
self.gym_last_internal_abs_ori = r.imu_torso_orientation # for training purposes (reward)
#---------------------------------------------------------------- compute internal target
if w.vision_is_up_to_date:
previous_internal_rel_orientation = np.copy(self.internal_rel_orientation)
internal_ori_diff = np.clip( M.normalize_deg( self.dribble_rel_orientation - self.internal_rel_orientation ), -MAX_ROTATION_DIFF, MAX_ROTATION_DIFF)
self.internal_rel_orientation = np.clip(M.normalize_deg( self.internal_rel_orientation + internal_ori_diff ), -MAX_ROTATION_DIST, MAX_ROTATION_DIST)
# Observations
self.internal_target_vel = self.internal_rel_orientation - previous_internal_rel_orientation
self.gym_last_internal_abs_ori = self.internal_rel_orientation + r.imu_torso_orientation
#----------------------------------------------------------------- observations
self.obs[74] = self.internal_rel_orientation / MAX_ROTATION_DIST
self.obs[75] = self.internal_target_vel / MAX_ROTATION_DIFF
return self.obs
def execute_ik(self, l_pos, l_rot, r_pos, r_rot):
r = self.world.robot
# Apply IK to each leg + Set joint targets
# Left leg
indices, self.values_l, error_codes = self.ik.leg(l_pos, l_rot, True, dynamic_pose=False)
r.set_joints_target_position_direct(indices, self.values_l, harmonize=False)
# Right leg
indices, self.values_r, error_codes = self.ik.leg(r_pos, r_rot, False, dynamic_pose=False)
r.set_joints_target_position_direct(indices, self.values_r, harmonize=False)
def execute(self, action):
r = self.world.robot
# Actions:
# 0,1,2 left ankle pos
# 3,4,5 right ankle pos
# 6,7,8 left foot rotation
# 9,10,11 right foot rotation
# 12,13 left/right arm pitch
# 14,15 left/right arm roll
# exponential moving average
self.act = 0.85 * self.act + 0.15 * action * 0.7 * 0.95 * self.dribble_speed
# execute Step behavior to extract the target positions of each leg (we will override these targets)
lfy,lfz,rfy,rfz = self.step_generator.get_target_positions(self.step_counter == 0, self.STEP_DUR, self.STEP_Z_SPAN, self.leg_length * self.STEP_Z_MAX)
# Leg IK
a = self.act
l_ankle_pos = (a[0]*0.025-0.01, a[1]*0.01 + lfy, a[2]*0.01 + lfz)
r_ankle_pos = (a[3]*0.025-0.01, a[4]*0.01 + rfy, a[5]*0.01 + rfz)
l_foot_rot = a[6:9] * (2,2,3)
r_foot_rot = a[9:12] * (2,2,3)
# Limit leg yaw/pitch (and add bias)
l_foot_rot[2] = max(0,l_foot_rot[2] + 18.3)
r_foot_rot[2] = min(0,r_foot_rot[2] - 18.3)
# Arms actions
arms = np.copy(self.DEFAULT_ARMS) # default arms pose
arms[0:4] += a[12:16]*4 # arms pitch+roll
# Set target positions
self.execute_ik(l_ankle_pos, l_foot_rot, r_ankle_pos, r_foot_rot) # legs
r.set_joints_target_position_direct( slice(14,22), arms, harmonize=False ) # arms
self.step_counter += 1
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/custom/Dribble/Env.py
|
Python
|
agpl-3.0
| 7,861
|
from agent.Base_Agent import Base_Agent
from math_ops.Math_Ops import Math_Ops as M
from math_ops.Neural_Network import run_mlp
import pickle, numpy as np
class Fall():
def __init__(self, base_agent : Base_Agent) -> None:
self.world = base_agent.world
self.description = "Fall example"
self.auto_head = False
with open(M.get_active_directory("/behaviors/custom/Fall/fall.pkl"), 'rb') as f:
self.model = pickle.load(f)
self.action_size = len(self.model[-1][0]) # extracted from size of Neural Network's last layer bias
self.obs = np.zeros(self.action_size+1, np.float32)
self.controllable_joints = min(self.world.robot.no_of_joints, self.action_size) # compatibility between different robot types
def observe(self):
r = self.world.robot
for i in range(self.action_size):
self.obs[i] = r.joints_position[i] / 100 # naive scale normalization
self.obs[self.action_size] = r.cheat_abs_pos[2] # head.z (alternative: r.loc_head_z)
def execute(self,reset) -> bool:
self.observe()
action = run_mlp(self.obs, self.model)
self.world.robot.set_joints_target_position_direct( # commit actions:
slice(self.controllable_joints), # act on trained joints
action*10, # scale actions up to motivate early exploration
harmonize=False # there is no point in harmonizing actions if the targets change at every step
)
return self.world.robot.loc_head_z < 0.15 # finished when head height < 0.15 m
def is_ready(self) -> any:
''' Returns True if this behavior is ready to start/continue under current game/robot conditions '''
return True
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/custom/Fall/Fall.py
|
Python
|
agpl-3.0
| 1,802
|
from agent.Base_Agent import Base_Agent
from collections import deque
import numpy as np
class Get_Up():
def __init__(self, base_agent : Base_Agent) -> None:
self.behavior = base_agent.behavior
self.world = base_agent.world
self.description = "Get Up using the most appropriate skills"
self.auto_head = False
self.MIN_HEIGHT = 0.3 # minimum value for the head's height
self.MAX_INCLIN = 50 # maximum torso inclination in degrees
self.STABILITY_THRESHOLD = 4
def reset(self):
self.state = 0
self.gyro_queue = deque(maxlen=self.STABILITY_THRESHOLD)
self.watchdog = 0 # when player has the shaking bug, it is never stable enough to get up
def execute(self,reset):
r = self.world.robot
execute_sub_behavior = self.behavior.execute_sub_behavior
if reset:
self.reset()
if self.state == 0: # State 0: go to pose "Zero"
self.watchdog += 1
self.gyro_queue.append( max(abs(r.gyro)) ) # log last STABILITY_THRESHOLD values
# advance to next state if behavior is complete & robot is stable
if (execute_sub_behavior("Zero",None) and len(self.gyro_queue) == self.STABILITY_THRESHOLD
and all(g < 10 for g in self.gyro_queue)) or self.watchdog > 100:
# determine how to get up
if r.acc[0] < -4 and abs(r.acc[1]) < 2 and abs(r.acc[2]) < 3:
execute_sub_behavior("Get_Up_Front", True) # reset behavior
self.state = 1
elif r.acc[0] > 4 and abs(r.acc[1]) < 2 and abs(r.acc[2]) < 3:
execute_sub_behavior("Get_Up_Back", True) # reset behavior
self.state = 2
elif r.acc[2] > 8: # fail-safe if vision is not up to date: if pose is 'Zero' and torso is upright, the robot is already up
return True
else:
execute_sub_behavior("Flip", True) # reset behavior
self.state = 3
elif self.state == 1:
if execute_sub_behavior("Get_Up_Front", False):
return True
elif self.state == 2:
if execute_sub_behavior("Get_Up_Back", False):
return True
elif self.state == 3:
if execute_sub_behavior("Flip", False):
self.reset()
return False
def is_ready(self):
''' Returns True if the Get Up behavior is ready (= robot is down) '''
r = self.world.robot
# check if z < 5 and acc magnitude > 8 and any visual indicator says we fell
return r.acc[2] < 5 and np.dot(r.acc,r.acc) > 64 and (r.loc_head_z < self.MIN_HEIGHT or r.imu_torso_inclination > self.MAX_INCLIN)
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/custom/Get_Up/Get_Up.py
|
Python
|
agpl-3.0
| 2,819
|
from agent.Base_Agent import Base_Agent
from behaviors.custom.Step.Step_Generator import Step_Generator
import numpy as np
class Step():
def __init__(self, base_agent : Base_Agent) -> None:
self.world = base_agent.world
self.ik = base_agent.inv_kinematics
self.description = "Step (Skill-Set-Primitive)"
self.auto_head = True
nao_specs = self.ik.NAO_SPECS
self.leg_length = nao_specs[1] + nao_specs[3] # upper leg height + lower leg height
feet_y_dev = nao_specs[0] * 1.2 # wider step
sample_time = self.world.robot.STEPTIME
max_ankle_z = nao_specs[5]
# Initialize step generator with constants
self.step_generator = Step_Generator(feet_y_dev, sample_time, max_ankle_z)
def execute(self,reset, ts_per_step=7, z_span=0.03, z_max=0.8):
lfy,lfz,rfy,rfz = self.step_generator.get_target_positions(reset, ts_per_step, z_span, self.leg_length * z_max)
#----------------- Apply IK to each leg + Set joint targets
# Left leg
indices, self.values_l, error_codes = self.ik.leg((0,lfy,lfz), (0,0,0), True, dynamic_pose=False)
for i in error_codes:
print(f"Joint {i} is out of range!" if i!=-1 else "Position is out of reach!")
self.world.robot.set_joints_target_position_direct(indices, self.values_l)
# Right leg
indices, self.values_r, error_codes = self.ik.leg((0,rfy,rfz), (0,0,0), False, dynamic_pose=False)
for i in error_codes:
print(f"Joint {i} is out of range!" if i!=-1 else "Position is out of reach!")
self.world.robot.set_joints_target_position_direct(indices, self.values_r)
# ----------------- Fixed arms
indices = [14,16,18,20]
values = np.array([-80,20,90,0])
self.world.robot.set_joints_target_position_direct(indices,values)
indices = [15,17,19,21]
values = np.array([-80,20,90,0])
self.world.robot.set_joints_target_position_direct(indices,values)
return False
def is_ready(self):
''' Returns True if Step Behavior is ready to start under current game/robot conditions '''
return True
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/custom/Step/Step.py
|
Python
|
agpl-3.0
| 2,218
|
import math
class Step_Generator():
GRAVITY = 9.81
Z0 = 0.2
def __init__(self, feet_y_dev, sample_time, max_ankle_z) -> None:
self.feet_y_dev = feet_y_dev
self.sample_time = sample_time
self.state_is_left_active = False
self.state_current_ts = 0
self.switch = False # switch legs
self.external_progress = 0 # non-overlaped progress
self.max_ankle_z = max_ankle_z
def get_target_positions(self, reset, ts_per_step, z_span, z_extension):
'''
Get target positions for each foot
Returns
-------
target : `tuple`
(Left leg y, Left leg z, Right leg y, Right leg z)
'''
assert type(ts_per_step)==int and ts_per_step > 0, "ts_per_step must be a positive integer!"
#-------------------------- Advance 1ts
if reset:
self.ts_per_step = ts_per_step # step duration in time steps
self.swing_height = z_span
self.max_leg_extension = z_extension # maximum distance between ankle to center of both hip joints
self.state_current_ts = 0
self.state_is_left_active = False
self.switch = False
elif self.switch:
self.state_current_ts = 0
self.state_is_left_active = not self.state_is_left_active # switch leg
self.switch = False
else:
self.state_current_ts += 1
#-------------------------- Compute COM.y
W = math.sqrt(self.Z0/self.GRAVITY)
step_time = self.ts_per_step * self.sample_time
time_delta = self.state_current_ts * self.sample_time
y0 = self.feet_y_dev # absolute initial y value
y_swing = y0 + y0 * ( math.sinh((step_time - time_delta)/W) + math.sinh(time_delta/W) ) / math.sinh(-step_time/W)
#-------------------------- Cap maximum extension and swing height
z0 = min(-self.max_leg_extension, self.max_ankle_z) # capped initial z value
zh = min(self.swing_height, self.max_ankle_z - z0) # capped swing height
#-------------------------- Compute Z Swing
progress = self.state_current_ts / self.ts_per_step
self.external_progress = self.state_current_ts / (self.ts_per_step-1)
active_z_swing = zh * math.sin(math.pi * progress)
#-------------------------- Accept new parameters after final step
if self.state_current_ts + 1 >= self.ts_per_step:
self.ts_per_step = ts_per_step # step duration in time steps
self.swing_height = z_span
self.max_leg_extension = z_extension # maximum distance between ankle to center of both hip joints
self.switch = True
#-------------------------- Distinguish active leg
if self.state_is_left_active:
return y0+y_swing, active_z_swing+z0, -y0+y_swing, z0
else:
return y0-y_swing, z0, -y0-y_swing, active_z_swing+z0
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/custom/Step/Step_Generator.py
|
Python
|
agpl-3.0
| 2,984
|
from agent.Base_Agent import Base_Agent
from behaviors.custom.Step.Step_Generator import Step_Generator
from math_ops.Math_Ops import Math_Ops as M
import math
import numpy as np
class Env():
def __init__(self, base_agent : Base_Agent) -> None:
self.world = base_agent.world
self.ik = base_agent.inv_kinematics
# State space
self.obs = np.zeros(63, np.float32)
# Step behavior defaults
self.STEP_DUR = 8
self.STEP_Z_SPAN = 0.02
self.STEP_Z_MAX = 0.70
# IK
nao_specs = self.ik.NAO_SPECS
self.leg_length = nao_specs[1] + nao_specs[3] # upper leg height + lower leg height
feet_y_dev = nao_specs[0] * 1.12 # wider step
sample_time = self.world.robot.STEPTIME
max_ankle_z = nao_specs[5]
self.step_generator = Step_Generator(feet_y_dev, sample_time, max_ankle_z)
self.DEFAULT_ARMS = np.array([-90,-90,8,8,90,90,70,70],np.float32)
self.walk_rel_orientation = None
self.walk_rel_target = None
self.walk_distance = None
def observe(self, init=False):
r = self.world.robot
if init: # reset variables
self.act = np.zeros(16, np.float32) # memory variable
self.step_counter = 0
# index observation naive normalization
self.obs[0] = min(self.step_counter,15*8) /100 # simple counter: 0,1,2,3...
self.obs[1] = r.loc_head_z *3 # z coordinate (torso)
self.obs[2] = r.loc_head_z_vel /2 # z velocity (torso)
self.obs[3] = r.imu_torso_roll /15 # absolute torso roll in deg
self.obs[4] = r.imu_torso_pitch /15 # absolute torso pitch in deg
self.obs[5:8] = r.gyro /100 # gyroscope
self.obs[8:11] = r.acc /10 # accelerometer
self.obs[11:17] = r.frp.get('lf', np.zeros(6)) * (10,10,10,0.01,0.01,0.01) # left foot: relative point of origin (p) and force vector (f) -> (px,py,pz,fx,fy,fz)*
self.obs[17:23] = r.frp.get('rf', np.zeros(6)) * (10,10,10,0.01,0.01,0.01) # right foot: relative point of origin (p) and force vector (f) -> (px,py,pz,fx,fy,fz)*
# *if foot is not touching the ground, then (px=0,py=0,pz=0,fx=0,fy=0,fz=0)
# Joints: Forward kinematics for ankles + feet rotation + arms (pitch + roll)
rel_lankle = self.ik.get_body_part_pos_relative_to_hip("lankle") # ankle position relative to center of both hip joints
rel_rankle = self.ik.get_body_part_pos_relative_to_hip("rankle") # ankle position relative to center of both hip joints
lf = r.head_to_body_part_transform("torso", r.body_parts['lfoot'].transform ) # foot transform relative to torso
rf = r.head_to_body_part_transform("torso", r.body_parts['rfoot'].transform ) # foot transform relative to torso
lf_rot_rel_torso = np.array( [lf.get_roll_deg(), lf.get_pitch_deg(), lf.get_yaw_deg()] ) # foot rotation relative to torso
rf_rot_rel_torso = np.array( [rf.get_roll_deg(), rf.get_pitch_deg(), rf.get_yaw_deg()] ) # foot rotation relative to torso
# pose
self.obs[23:26] = rel_lankle * (8,8,5)
self.obs[26:29] = rel_rankle * (8,8,5)
self.obs[29:32] = lf_rot_rel_torso / 20
self.obs[32:35] = rf_rot_rel_torso / 20
self.obs[35:39] = r.joints_position[14:18] /100 # arms (pitch + roll)
# velocity
self.obs[39:55] = r.joints_target_last_speed[2:18] # predictions == last action
'''
Expected observations for walking state:
Time step R 0 1 2 3 4 5 6 7 0
Progress 1 0 .14 .28 .43 .57 .71 .86 1 0
Left leg active T F F F F F F F F T
'''
if init: # the walking parameters refer to the last parameters in effect (after a reset, they are pointless)
self.obs[55] = 1 # step progress
self.obs[56] = 1 # 1 if left leg is active
self.obs[57] = 0 # 1 if right leg is active
else:
self.obs[55] = self.step_generator.external_progress # step progress
self.obs[56] = float(self.step_generator.state_is_left_active) # 1 if left leg is active
self.obs[57] = float(not self.step_generator.state_is_left_active) # 1 if right leg is active
'''
Create internal target with a smoother variation
'''
MAX_LINEAR_DIST = 0.5
MAX_LINEAR_DIFF = 0.014 # max difference (meters) per step
MAX_ROTATION_DIFF = 1.6 # max difference (degrees) per step
MAX_ROTATION_DIST = 45
if init:
self.internal_rel_orientation = 0
self.internal_target = np.zeros(2)
previous_internal_target = np.copy(self.internal_target)
#---------------------------------------------------------------- compute internal linear target
rel_raw_target_size = np.linalg.norm(self.walk_rel_target)
if rel_raw_target_size == 0:
rel_target = self.walk_rel_target
else:
rel_target = self.walk_rel_target / rel_raw_target_size * min(self.walk_distance, MAX_LINEAR_DIST)
internal_diff = rel_target - self.internal_target
internal_diff_size = np.linalg.norm(internal_diff)
if internal_diff_size > MAX_LINEAR_DIFF:
self.internal_target += internal_diff * (MAX_LINEAR_DIFF / internal_diff_size)
else:
self.internal_target[:] = rel_target
#---------------------------------------------------------------- compute internal rotation target
internal_ori_diff = np.clip( M.normalize_deg( self.walk_rel_orientation - self.internal_rel_orientation ), -MAX_ROTATION_DIFF, MAX_ROTATION_DIFF)
self.internal_rel_orientation = np.clip(M.normalize_deg( self.internal_rel_orientation + internal_ori_diff ), -MAX_ROTATION_DIST, MAX_ROTATION_DIST)
#----------------------------------------------------------------- observations
internal_target_vel = self.internal_target - previous_internal_target
self.obs[58] = self.internal_target[0] / MAX_LINEAR_DIST
self.obs[59] = self.internal_target[1] / MAX_LINEAR_DIST
self.obs[60] = self.internal_rel_orientation / MAX_ROTATION_DIST
self.obs[61] = internal_target_vel[0] / MAX_LINEAR_DIFF
self.obs[62] = internal_target_vel[0] / MAX_LINEAR_DIFF
return self.obs
def execute_ik(self, l_pos, l_rot, r_pos, r_rot):
r = self.world.robot
# Apply IK to each leg + Set joint targets
# Left leg
indices, self.values_l, error_codes = self.ik.leg(l_pos, l_rot, True, dynamic_pose=False)
r.set_joints_target_position_direct(indices, self.values_l, harmonize=False)
# Right leg
indices, self.values_r, error_codes = self.ik.leg(r_pos, r_rot, False, dynamic_pose=False)
r.set_joints_target_position_direct(indices, self.values_r, harmonize=False)
def execute(self, action):
r = self.world.robot
# Actions:
# 0,1,2 left ankle pos
# 3,4,5 right ankle pos
# 6,7,8 left foot rotation
# 9,10,11 right foot rotation
# 12,13 left/right arm pitch
# 14,15 left/right arm roll
internal_dist = np.linalg.norm( self.internal_target )
action_mult = 1 if internal_dist > 0.2 else (0.7/0.2) * internal_dist + 0.3
# exponential moving average
self.act = 0.8 * self.act + 0.2 * action * action_mult * 0.7
# execute Step behavior to extract the target positions of each leg (we will override these targets)
lfy,lfz,rfy,rfz = self.step_generator.get_target_positions(self.step_counter == 0, self.STEP_DUR, self.STEP_Z_SPAN, self.leg_length * self.STEP_Z_MAX)
# Leg IK
a = self.act
l_ankle_pos = (a[0]*0.02, max(0.01, a[1]*0.02 + lfy), a[2]*0.01 + lfz) # limit y to avoid self collision
r_ankle_pos = (a[3]*0.02, min(a[4]*0.02 + rfy, -0.01), a[5]*0.01 + rfz) # limit y to avoid self collision
l_foot_rot = a[6:9] * (3,3,5)
r_foot_rot = a[9:12] * (3,3,5)
# Limit leg yaw/pitch
l_foot_rot[2] = max(0,l_foot_rot[2] + 7)
r_foot_rot[2] = min(0,r_foot_rot[2] - 7)
# Arms actions
arms = np.copy(self.DEFAULT_ARMS) # default arms pose
arm_swing = math.sin(self.step_generator.state_current_ts / self.STEP_DUR * math.pi) * 6
inv = 1 if self.step_generator.state_is_left_active else -1
arms[0:4] += a[12:16]*4 + (-arm_swing*inv,arm_swing*inv,0,0) # arms pitch+roll
# Set target positions
self.execute_ik(l_ankle_pos, l_foot_rot, r_ankle_pos, r_foot_rot) # legs
r.set_joints_target_position_direct( slice(14,22), arms, harmonize=False ) # arms
self.step_counter += 1
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/custom/Walk/Env.py
|
Python
|
agpl-3.0
| 9,035
|
from agent.Base_Agent import Base_Agent
from behaviors.custom.Walk.Env import Env
from math_ops.Math_Ops import Math_Ops as M
from math_ops.Neural_Network import run_mlp
import numpy as np
import pickle
class Walk():
def __init__(self, base_agent : Base_Agent) -> None:
self.world = base_agent.world
self.description = "Omnidirectional RL walk"
self.auto_head = True
self.env = Env(base_agent)
self.last_executed = 0
with open(M.get_active_directory([
"/behaviors/custom/Walk/walk_R0.pkl",
"/behaviors/custom/Walk/walk_R1_R3.pkl",
"/behaviors/custom/Walk/walk_R2.pkl",
"/behaviors/custom/Walk/walk_R1_R3.pkl",
"/behaviors/custom/Walk/walk_R4.pkl"
][self.world.robot.type]), 'rb') as f:
self.model = pickle.load(f)
def execute(self, reset, target_2d, is_target_absolute, orientation, is_orientation_absolute, distance):
'''
Parameters
----------
target_2d : array_like
2D target in absolute or relative coordinates (use is_target_absolute to specify)
is_target_absolute : bool
True if target_2d is in absolute coordinates, False if relative to robot's torso
orientation : float
absolute or relative orientation of torso, in degrees
set to None to go towards the target (is_orientation_absolute is ignored)
is_orientation_absolute : bool
True if orientation is relative to the field, False if relative to the robot's torso
distance : float
distance to final target [0,0.5] (influences walk speed when approaching the final target)
set to None to consider target_2d the final target
'''
r = self.world.robot
#------------------------ 0. Override reset (since some behaviors use this as a sub-behavior)
if reset and self.world.time_local_ms - self.last_executed == 20:
reset = False
self.last_executed = self.world.time_local_ms
#------------------------ 1. Define walk parameters
if is_target_absolute: # convert to target relative to (head position + torso orientation)
raw_target = target_2d - r.loc_head_position[:2]
self.env.walk_rel_target = M.rotate_2d_vec(raw_target, -r.imu_torso_orientation)
else:
self.env.walk_rel_target = target_2d
if distance is None:
self.env.walk_distance = np.linalg.norm(self.env.walk_rel_target)
else:
self.env.walk_distance = distance # MAX_LINEAR_DIST = 0.5
# Relative orientation values are decreased to avoid overshoot
if orientation is None:
self.env.walk_rel_orientation = M.vector_angle(self.env.walk_rel_target) * 0.3
elif is_orientation_absolute:
self.env.walk_rel_orientation = M.normalize_deg( orientation - r.imu_torso_orientation )
else:
self.env.walk_rel_orientation = orientation * 0.3
#------------------------ 2. Execute behavior
obs = self.env.observe(reset)
action = run_mlp(obs, self.model)
self.env.execute(action)
return False
def is_ready(self):
''' Returns True if Walk Behavior is ready to start under current game/robot conditions '''
return True
|
2302_81918214/robocup3D
|
FCPCodebase-main/behaviors/custom/Walk/Walk.py
|
Python
|
agpl-3.0
| 3,399
|
#!/bin/bash
# Call this script from any directory
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
# cd to main folder
cd "${SCRIPT_DIR}/.."
rm -rf ./bundle/build
rm -rf ./bundle/dist
onefile="--onefile"
# bundle app, dependencies and data files into single executable
pyinstaller \
--add-data './world/commons/robots:world/commons/robots' \
--add-data './behaviors/slot/common:behaviors/slot/common' \
--add-data './behaviors/slot/r0:behaviors/slot/r0' \
--add-data './behaviors/slot/r1:behaviors/slot/r1' \
--add-data './behaviors/slot/r2:behaviors/slot/r2' \
--add-data './behaviors/slot/r3:behaviors/slot/r3' \
--add-data './behaviors/slot/r4:behaviors/slot/r4' \
--add-data './behaviors/custom/Dribble/*.pkl:behaviors/custom/Dribble' \
--add-data './behaviors/custom/Walk/*.pkl:behaviors/custom/Walk' \
--add-data './behaviors/custom/Fall/*.pkl:behaviors/custom/Fall' \
${onefile} --distpath ./bundle/dist/ --workpath ./bundle/build/ --noconfirm --name fcp Run_Player.py
# start.sh
cat > ./bundle/dist/start.sh << EOF
#!/bin/bash
export OMP_NUM_THREADS=1
host=\${1:-localhost}
port=\${2:-3100}
for i in {1..11}; do
./fcp -i \$host -p \$port -u \$i -t FCPortugal &
done
EOF
# start_penalty.sh
cat > ./bundle/dist/start_penalty.sh << EOF
#!/bin/bash
export OMP_NUM_THREADS=1
host=\${1:-localhost}
port=\${2:-3100}
./fcp -i \$host -p \$port -u 1 -t FCPortugal -P 1 &
./fcp -i \$host -p \$port -u 11 -t FCPortugal -P 1 &
EOF
# start_fat_proxy.sh
cat > ./bundle/dist/start_fat_proxy.sh << EOF
#!/bin/bash
export OMP_NUM_THREADS=1
host=\${1:-localhost}
port=\${2:-3100}
for i in {1..11}; do
./fcp -i \$host -p \$port -u \$i -t FCPortugal -F 1 &
done
EOF
# kill.sh
cat > ./bundle/dist/kill.sh << EOF
#!/bin/bash
pkill -9 -e fcp
EOF
# execution permission
chmod a+x ./bundle/dist/start.sh
chmod a+x ./bundle/dist/start_penalty.sh
chmod a+x ./bundle/dist/start_fat_proxy.sh
chmod a+x ./bundle/dist/kill.sh
|
2302_81918214/robocup3D
|
FCPCodebase-main/bundle/bundle.sh
|
Shell
|
agpl-3.0
| 1,957
|
from typing import List
from world.commons.Other_Robot import Other_Robot
from world.World import World
import numpy as np
class Radio():
'''
map limits are hardcoded:
teammates/opponents positions (x,y) in ([-16,16],[-11,11])
ball position (x,y) in ([-15,15],[-10,10])
known server limitations:
claimed: all ascii from 0x20 to 0x7E except ' ', '(', ')'
bugs:
- ' or " clip the message
- '\' at the end or near another '\'
- ';' at beginning of message
'''
# map limits are hardcoded:
# lines, columns, half lines index, half cols index, (lines-1)/x_span, (cols-1)/y_span, combinations, combinations*2states,
TP = 321,221,160,110,10, 10,70941,141882 # teammate position
OP = 201,111,100,55, 6.25,5, 22311,44622 # opponent position
BP = 301,201,150,100,10, 10,60501 # ball position
SYMB = "!#$%&*+,-./0123456789:<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[]^_`abcdefghijklmnopqrstuvwxyz{|}~;"
SLEN = len(SYMB)
SYMB_TO_IDX = {ord(s):i for i,s in enumerate(SYMB)}
def __init__(self, world : World, commit_announcement) -> None:
self.world = world
self.commit_announcement = commit_announcement
r = world.robot
t = world.teammates
o = world.opponents
self.groups = ( # player team/unum, group has ball?, self in group?
[(t[9],t[10],o[6],o[7],o[8],o[9],o[10]), True ], # 2 teammates, 5 opponents, ball
[(t[0],t[1], t[2],t[3],t[4],t[5],t[6] ), False], # 7 teammates
[(t[7],t[8], o[0],o[1],o[2],o[3],o[4],o[5]), False] # 2 teammates, 6 opponents
)
for g in self.groups: # add 'self in group?'
g.append(any(i.is_self for i in g[0]))
def get_player_combination(self, pos, is_unknown, is_down, info):
''' Returns combination (0-based) and number of possible combinations '''
if is_unknown:
return info[7]+1, info[7]+2 # return unknown combination
x,y = pos[:2]
if x < -17 or x > 17 or y < -12 or y > 12:
return info[7], info[7]+2 # return out of bounds combination (if it exceeds 1m in any axis)
# convert to int to avoid overflow later
l = int(np.clip( round(info[4]*x+info[2]), 0, info[0]-1 )) # absorb out of bounds positions (up to 1m in each axis)
c = int(np.clip( round(info[5]*y+info[3]), 0, info[1]-1 ))
return (l*info[1]+c)+(info[6] if is_down else 0), info[7]+2 # return valid combination
def get_ball_combination(self, x, y):
''' Returns combination (0-based) and number of possible combinations '''
# if ball is out of bounds, we force it in
l = int(np.clip( round(Radio.BP[4]*x+Radio.BP[2]), 0, Radio.BP[0]-1 ))
c = int(np.clip( round(Radio.BP[5]*y+Radio.BP[3]), 0, Radio.BP[1]-1 ))
return l*Radio.BP[1]+c, Radio.BP[6] # return valid combination
def get_ball_position(self,comb):
l = comb // Radio.BP[1]
c = comb % Radio.BP[1]
return np.array([l/Radio.BP[4]-15, c/Radio.BP[5]-10, 0.042]) # assume ball is on ground
def get_player_position(self,comb, info):
if comb == info[7]: return -1 # player is out of bounds
if comb == info[7]+1: return -2 # player is in unknown location
is_down = comb >= info[6]
if is_down:
comb -= info[6]
l = comb // info[1]
c = comb % info[1]
return l/info[4]-16, c/info[5]-11, is_down
def check_broadcast_requirements(self):
'''
Check if broadcast group is valid
Returns
-------
ready : bool
True if all requirements are met
Sequence: g0,g1,g2, ig0,ig1,ig2, iig0,iig1,iig2 (whole cycle: 0.36s)
igx means 'incomplete group', where <=1 element can be MIA recently
iigx means 'very incomplete group', where <=2 elements can be MIA recently
Rationale: prevent incomplete messages from monopolizing the broadcast space
However:
- 1st round: when 0 group members are missing, that group will update 3 times every 0.36s
- 2nd round: when 1 group member is recently missing, that group will update 2 times every 0.36s
- 3rd round: when 2 group members are recently missing, that group will update 1 time every 0.36s
- when >2 group members are recently missing, that group will not be updated
Players that have never been seen or heard are not considered for the 'recently missing'.
If there is only 1 group member since the beginning, the respective group can be updated, except in the 1st round.
In this way, the 1st round cannot be monopolized by clueless agents, which is important during games with 22 players.
'''
w = self.world
r = w.robot
ago40ms = w.time_local_ms - 40
ago370ms = w.time_local_ms - 370 # maximum delay (up to 2 MIAs) is 360ms because radio has a delay of 20ms (otherwise max delay would be 340ms)
group : List[Other_Robot]
idx9 = int((w.time_server * 25)+0.1) % 9 # sequence of 9 phases
max_MIA = idx9 // 3 # maximum number of MIA players (based on server time)
group_idx = idx9 % 3 # group number (based on server time)
group, has_ball, is_self_included = self.groups[group_idx]
#============================================ 0. check if group is valid
if has_ball and w.ball_abs_pos_last_update < ago40ms: # Ball is included and not up to date
return False
if is_self_included and r.loc_last_update < ago40ms: # Oneself is included and unable to self-locate
return False
# Get players that have been previously seen or heard but not recently
MIAs = [not ot.is_self and ot.state_last_update < ago370ms and ot.state_last_update > 0 for ot in group]
self.MIAs = [ot.state_last_update == 0 or MIAs[i] for i,ot in enumerate(group)] # add players that have never been seen
if sum(MIAs) > max_MIA: # checking if number of recently missing members is not above threshold
return False
# never seen before players are always ignored except when:
# - this is the 0 MIAs round (see explanation above)
# - all are MIA
if (max_MIA == 0 and any(self.MIAs)) or all(self.MIAs):
return False
# Check for invalid members. Conditions:
# - Player is other and not MIA and:
# - last update was >40ms ago OR
# - last update did not include the head (head is important to provide state and accurate position)
if any(
(not ot.is_self and not self.MIAs[i] and
(ot.state_last_update < ago40ms or ot.state_last_update==0 or len(ot.state_abs_pos)<3)# (last update: has no head or is old)
) for i,ot in enumerate(group)
):
return False
return True
def broadcast(self):
'''
向队友广播环境信息的核心方法（每帧执行）
消息内容包含：所有移动实体（球/球员）的位置和状态
采用组合编码方式将多维信息压缩为短字符串
'''
# ===================== 前置条件检查 =====================
if not self.check_broadcast_requirements(): # 检查广播条件（如通信冷却时间、带宽限制等）
return # 不满足条件时直接返回
# ===================== 数据准备 =====================
w = self.world # 获取世界状态快照
ot : Other_Robot # 类型提示：其他机器人对象
# 基于服务器时间计算当前组别（25秒周期循环，3组轮换）
group_idx = int((w.time_server * 25)+0.1) % 3 # 将时间映射到[0,1,2]组索引
group, has_ball, _ = self.groups[group_idx] # 获取当前组的配置（球员列表，是否包含球，其他参数）
# ===================== 组合编码生成 =====================
combination = group_idx # 初始化组合值为组索引（基础值）
no_of_combinations = 3 # 当前组合的可能性总数（初始为3组可能性）
# ---- 球信息编码 ----
if has_ball: # 当前组需要编码球的信息
# 将球的二维坐标(x,y)转换为组合码（例如：离散化坐标或哈希值）
c, n = self.get_ball_combination(w.ball_abs_pos[0], w.ball_abs_pos[1])
# 累加组合值：c * 当前可能性数，相当于在组合数中开辟球信息段
combination += c * no_of_combinations
no_of_combinations *= n # 更新总可能性数（基数扩展）
# ---- 球员信息编码 ----
for i,ot in enumerate(group): # 遍历当前组的每个球员
# 生成单个球员的组合码，包含：
# - 位置坐标：ot.state_abs_pos
# - 未知状态：self.MIAs[i]（Missing In Action，是否失去追踪）
# - 倒地状态：ot.state_fallen
# - 队伍类型：Radio.TP（队友）或Radio.OP（对手）
c, n = self.get_player_combination(ot.state_abs_pos,
self.MIAs[i], ot.state_fallen,
Radio.TP if ot.is_teammate else Radio.OP)
# 累加组合值：类似位域拼接，每个球员占据独立段
combination += c * no_of_combinations
no_of_combinations *= n # 基数扩展（例如：每个球员增加89种可能性）
# 安全性断言：确保总可能性不超过编码符号表的容量（避免溢出）
# 88*89^19 ≈ 9.61e38 是Radio.SYMB符号表的最大容量（假设使用89个字符）
assert(no_of_combinations < 9.61e38)
# ===================== 消息编码 =====================
msg = '' # 初始化空消息
# 首字符特殊处理：跳过';'符号（规避服务器解析bug）
remainder = combination % (Radio.SLEN-1) # SLEN为符号表长度（例如89）
msg += Radio.SYMB[remainder] # 从符号表中取对应字符
combination = combination // (Radio.SLEN-1) # 更新剩余组合值
# 后续字符编码：使用完整符号表
while combination > 0:
remainder = combination % Radio.SLEN # 取当前余数
msg += Radio.SYMB[remainder] # 映射到符号字符
combination = combination // Radio.SLEN # 迭代处理更高位
# ===================== 消息提交 =====================
self.commit_announcement(msg.encode()) # 提交编码后的消息（转换为字节流）
def receive(self, msg:bytearray):
w = self.world
r = w.robot
ago40ms = w.time_local_ms - 40
ago110ms = w.time_local_ms - 110
msg_time = w.time_local_ms - 20 # message was sent in the last step
#============================================ 1. get combination
# read first symbol, which cannot be ';' due to server bug
combination = Radio.SYMB_TO_IDX[msg[0]]
total_combinations = Radio.SLEN-1
if len(msg)>1:
for m in msg[1:]:
combination += total_combinations * Radio.SYMB_TO_IDX[m]
total_combinations *= Radio.SLEN
#============================================ 2. get msg ID
message_no = combination % 3
combination //= 3
group, has_ball, _ = self.groups[message_no]
#============================================ 3. get data
if has_ball:
ball_comb = combination % Radio.BP[6]
combination //= Radio.BP[6]
players_combs = []
ot : Other_Robot
for ot in group:
info = Radio.TP if ot.is_teammate else Radio.OP
players_combs.append( combination % (info[7]+2) )
combination //= info[7]+2
#============================================ 4. update world
if has_ball and w.ball_abs_pos_last_update < ago40ms: # update ball if it was not seen
time_diff = (msg_time - w.ball_abs_pos_last_update) / 1000
ball = self.get_ball_position(ball_comb)
w.ball_abs_vel = (ball - w.ball_abs_pos) / time_diff
w.ball_abs_speed = np.linalg.norm(w.ball_abs_vel)
w.ball_abs_pos_last_update = msg_time # (error: 0-40 ms)
w.ball_abs_pos = ball
w.is_ball_abs_pos_from_vision = False
for c, ot in zip(players_combs, group):
# handle oneself case
if ot.is_self:
# the ball's position has a fair amount of noise, whether seen by us or other players
# but our self-locatization mechanism is usually much better than how others perceive us
if r.loc_last_update < ago110ms: # so we wait until we miss 2 visual steps
data = self.get_player_position(c, Radio.TP)
if type(data)==tuple:
x,y,is_down = data
r.loc_head_position[:2] = x,y # z is kept unchanged
r.loc_head_position_last_update = msg_time
r.radio_fallen_state = is_down
r.radio_last_update = msg_time
continue
# do not update if other robot was recently seen
if ot.state_last_update >= ago40ms:
continue
info = Radio.TP if ot.is_teammate else Radio.OP
data = self.get_player_position(c, info)
if type(data)==tuple:
x,y,is_down = data
p = np.array([x,y])
if ot.state_abs_pos is not None: # update the x & y components of the velocity
time_diff = (msg_time - ot.state_last_update) / 1000
velocity = np.append( (p - ot.state_abs_pos[:2]) / time_diff, 0) # v.z = 0
vel_diff = velocity - ot.state_filtered_velocity
if np.linalg.norm(vel_diff) < 4: # otherwise assume it was beamed
ot.state_filtered_velocity /= (ot.vel_decay,ot.vel_decay,1) # neutralize decay (except in the z-axis)
ot.state_filtered_velocity += ot.vel_filter * vel_diff
ot.state_fallen = is_down
ot.state_last_update = msg_time
ot.state_body_parts_abs_pos = {"head":p}
ot.state_abs_pos = p
ot.state_horizontal_dist = np.linalg.norm(p - r.loc_head_position[:2])
ot.state_ground_area = (p, 0.3 if is_down else 0.2) # not very precise, but we cannot see the robot
|
2302_81918214/robocup3D
|
FCPCodebase-main/communication/Radio.py
|
Python
|
agpl-3.0
| 15,117
|
from communication.World_Parser import World_Parser
from itertools import count
from select import select
from sys import exit
from world.World import World
import socket
import time
class Server_Comm():
monitor_socket = None
def __init__(self, host:str, agent_port:int, monitor_port:int, unum:int, robot_type:int, team_name:str,
world_parser:World_Parser, world:World, other_players, wait_for_server=True) -> None:
self.BUFFER_SIZE = 8192
self.rcv_buff = bytearray(self.BUFFER_SIZE)
self.send_buff = []
self.world_parser = world_parser
self.unum = unum
# During initialization, it's not clear whether we are on the left or right side
self._unofficial_beam_msg_left = "(agent (unum " + str(unum) + ") (team Left) (move "
self._unofficial_beam_msg_right = "(agent (unum " + str(unum) + ") (team Right) (move "
self.world = world
self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM )
if wait_for_server: print("Waiting for server at ", host, ":", agent_port, sep="",end=".",flush=True)
while True:
try:
self.socket.connect((host, agent_port))
print(end=" ")
break
except ConnectionRefusedError:
if not wait_for_server:
print("Server is down. Closing...")
exit()
time.sleep(1)
print(".",end="",flush=True)
print("Connected agent", unum, self.socket.getsockname())
self.send_immediate(b'(scene rsg/agent/nao/nao_hetero.rsg ' + str(robot_type).encode() + b')')
self._receive_async(other_players, True)
self.send_immediate(b'(init (unum '+ str(unum).encode() + b') (teamname '+ team_name.encode() + b'))')
self._receive_async(other_players, False)
# Repeat to guarantee that team side information is received
for _ in range(3):
# Eliminate advanced step by changing syn order (rcssserver3d protocol bug, usually seen for player 11)
self.send_immediate(b'(syn)') #if this syn is not needed, it will be discarded by the server
for p in other_players:
p.scom.send_immediate(b'(syn)')
for p in other_players:
p.scom.receive()
self.receive()
if world.team_side_is_left == None:
print("\nError: server did not return a team side! Check server terminal!")
exit()
# Monitor socket is shared by all agents on the same thread
if Server_Comm.monitor_socket is None and monitor_port is not None:
print("Connecting to server's monitor port at ", host, ":", monitor_port, sep="",end=".",flush=True)
Server_Comm.monitor_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM )
Server_Comm.monitor_socket.connect((host, monitor_port))
print("Done!")
def _receive_async(self, other_players, first_pass) -> None:
'''Private function that receives asynchronous information during the initialization'''
if not other_players:
self.receive()
return
self.socket.setblocking(0)
if first_pass: print("Async agent",self.unum,"initialization", end="", flush=True)
while True:
try:
print(".",end="",flush=True)
self.receive()
break
except:
pass
for p in other_players:
p.scom.send_immediate(b'(syn)')
for p in other_players:
p.scom.receive()
self.socket.setblocking(1)
if not first_pass: print("Done!")
def receive(self, update=True):
for i in count(): # parse all messages and perform value updates, but heavy computation is only done once at the end
try:
if self.socket.recv_into(self.rcv_buff, nbytes=4) != 4: raise ConnectionResetError()
msg_size = int.from_bytes(self.rcv_buff[:4], byteorder='big', signed=False)
if self.socket.recv_into(self.rcv_buff, nbytes=msg_size, flags=socket.MSG_WAITALL) != msg_size: raise ConnectionResetError()
except ConnectionResetError:
print("\nError: socket was closed by rcssserver3d!")
exit()
self.world_parser.parse(self.rcv_buff[:msg_size])
if len(select([self.socket],[],[], 0.0)[0]) == 0: break
if update:
if i==1: self.world.log( "Server_Comm.py: The agent lost 1 packet! Is syncmode enabled?")
if i>1: self.world.log(f"Server_Comm.py: The agent lost {i} consecutive packets! Is syncmode disabled?")
self.world.update()
if len(select([self.socket],[],[], 0.0)[0]) != 0:
self.world.log("Server_Comm.py: Received a new packet while on world.update()!")
self.receive()
def send_immediate(self, msg:bytes) -> None:
''' Commit and send immediately '''
try:
self.socket.send( (len(msg)).to_bytes(4,byteorder='big') + msg ) #Add message length in the first 4 bytes
except BrokenPipeError:
print("\nError: socket was closed by rcssserver3d!")
exit()
def send(self) -> None:
''' Send all committed messages '''
if len(select([self.socket],[],[], 0.0)[0]) == 0:
self.send_buff.append(b'(syn)')
self.send_immediate( b''.join(self.send_buff) )
else:
self.world.log("Server_Comm.py: Received a new packet while thinking!")
self.send_buff = [] #clear buffer
def commit(self, msg:bytes) -> None:
assert type(msg) == bytes, "Message must be of type Bytes!"
self.send_buff.append(msg)
def commit_and_send(self, msg:bytes = b'') -> None:
self.commit(msg)
self.send()
def clear_buffer(self) -> None:
self.send_buff = []
def commit_announcement(self, msg:bytes) -> None:
'''
Say something to every player on the field.
Maximum 20 characters, ascii between 0x20, 0x7E except ' ', '(', ')'
Accepted: letters+numbers+symbols: !"#$%&'*+,-./:;<=>?@[\]^_`{|}~
Message range: 50m (the field is 36m diagonally, so ignore this limitation)
A player can only hear a teammate's message every 2 steps (0.04s)
This ability exists independetly for messages from both teams
(i.e. our team cannot spam the other team to block their messages)
Messages from oneself are always heard
'''
assert len(msg) <= 20 and type(msg) == bytes
self.commit(b'(say ' + msg + b')')
def commit_pass_command(self) -> None:
'''
Issue a pass command:
Conditions:
- The current playmode is PlayOn
- The agent is near the ball (default 0.5m)
- No opponents are near the ball (default 1m)
- The ball is stationary (default <0.05m/s)
- A certain amount of time has passed between pass commands
'''
self.commit(b'(pass)')
def commit_beam(self, pos2d, rot) -> None:
'''
Official beam command that can be used in-game
This beam is affected by noise (unless it is disabled in the server configuration)
Parameters
----------
pos2d : array_like
Absolute 2D position (negative X is always our half of the field, no matter our side)
rot : `int`/`float`
Player angle in degrees (0 points forward)
'''
assert len(pos2d)==2, "The official beam command accepts only 2D positions!"
self.commit( f"(beam {pos2d[0]} {pos2d[1]} {rot})".encode() )
def unofficial_beam(self, pos3d, rot) -> None:
'''
Unofficial beam - it cannot be used in official matches
Parameters
----------
pos3d : array_like
Absolute 3D position (negative X is always our half of the field, no matter our side)
rot : `int`/`float`
Player angle in degrees (0 points forward)
'''
assert len(pos3d)==3, "The unofficial beam command accepts only 3D positions!"
# there is no need to normalize the angle, the server accepts any angle
if self.world.team_side_is_left:
msg = f"{self._unofficial_beam_msg_left }{ pos3d[0]} { pos3d[1]} {pos3d[2]} {rot-90}))".encode()
else:
msg = f"{self._unofficial_beam_msg_right}{-pos3d[0]} {-pos3d[1]} {pos3d[2]} {rot+90}))".encode()
self.monitor_socket.send( (len(msg)).to_bytes(4,byteorder='big') + msg )
def unofficial_kill_sim(self) -> None:
''' Unofficial kill simulator command '''
msg = b'(killsim)'
self.monitor_socket.send( (len(msg)).to_bytes(4,byteorder='big') + msg )
def unofficial_move_ball(self, pos3d, vel3d=(0,0,0)) -> None:
'''
Unofficial command to move ball
info: ball radius = 0.042m
Parameters
----------
pos3d : array_like
Absolute 3D position (negative X is always our half of the field, no matter our side)
vel3d : array_like
Absolute 3D velocity (negative X is always our half of the field, no matter our side)
'''
assert len(pos3d)==3 and len(vel3d)==3, "To move the ball we need a 3D position and velocity"
if self.world.team_side_is_left:
msg = f"(ball (pos { pos3d[0]} { pos3d[1]} {pos3d[2]}) (vel { vel3d[0]} { vel3d[1]} {vel3d[2]}))".encode()
else:
msg = f"(ball (pos {-pos3d[0]} {-pos3d[1]} {pos3d[2]}) (vel {-vel3d[0]} {-vel3d[1]} {vel3d[2]}))".encode()
self.monitor_socket.send( (len(msg)).to_bytes(4,byteorder='big') + msg )
def unofficial_set_game_time(self, time_in_s : float) -> None:
'''
Unofficial command to set the game time
e.g. unofficial_set_game_time(68.78)
Parameters
----------
time_in_s : float
Game time in seconds
'''
msg = f"(time {time_in_s})".encode()
self.monitor_socket.send( (len(msg)).to_bytes(4,byteorder='big') + msg )
def unofficial_set_play_mode(self, play_mode : str) -> None:
'''
Unofficial command to set the play mode
e.g. unofficial_set_play_mode("PlayOn")
Parameters
----------
play_mode : str
Play mode
'''
msg = f"(playMode {play_mode})".encode()
self.monitor_socket.send( (len(msg)).to_bytes(4,byteorder='big') + msg )
def unofficial_kill_player(self, unum : int, team_side_is_left : bool) -> None:
'''
Unofficial command to kill specific player
Parameters
----------
unum : int
Uniform number
team_side_is_left : bool
True if player to kill belongs to left team
'''
msg = f"(kill (unum {unum}) (team {'Left' if team_side_is_left else 'Right'}))".encode()
self.monitor_socket.send( (len(msg)).to_bytes(4,byteorder='big') + msg )
def close(self, close_monitor_socket = False):
''' Close agent socket, and optionally the monitor socket (shared by players running on the same thread) '''
self.socket.close()
if close_monitor_socket and Server_Comm.monitor_socket is not None:
Server_Comm.monitor_socket.close()
Server_Comm.monitor_socket = None
|
2302_81918214/robocup3D
|
FCPCodebase-main/communication/Server_Comm.py
|
Python
|
agpl-3.0
| 11,585
|
from math_ops.Math_Ops import Math_Ops as M
from world.Robot import Robot
from world.World import World
import math
import numpy as np
class World_Parser():
def __init__(self, world:World, hear_callback) -> None:
self.LOG_PREFIX = "World_Parser.py: "
self.world = world
self.hear_callback = hear_callback
self.exp = None
self.depth = None
self.LEFT_SIDE_FLAGS = {b'F2L':(-15,-10,0),
b'F1L':(-15,+10,0),
b'F2R':(+15,-10,0),
b'F1R':(+15,+10,0),
b'G2L':(-15,-1.05,0.8),
b'G1L':(-15,+1.05,0.8),
b'G2R':(+15,-1.05,0.8),
b'G1R':(+15,+1.05,0.8)} #mapping between flag names and their corrected location, when playing on the left side
self.RIGHT_SIDE_FLAGS = {b'F2L':(+15,+10,0),
b'F1L':(+15,-10,0),
b'F2R':(-15,+10,0),
b'F1R':(-15,-10,0),
b'G2L':(+15,+1.05,0.8),
b'G1L':(+15,-1.05,0.8),
b'G2R':(-15,+1.05,0.8),
b'G1R':(-15,-1.05,0.8)}
self.play_mode_to_id = None
self.LEFT_PLAY_MODE_TO_ID = {"KickOff_Left":World.M_OUR_KICKOFF, "KickIn_Left":World.M_OUR_KICK_IN, "corner_kick_left":World.M_OUR_CORNER_KICK,
"goal_kick_left":World.M_OUR_GOAL_KICK, "free_kick_left":World.M_OUR_FREE_KICK, "pass_left":World.M_OUR_PASS,
"direct_free_kick_left": World.M_OUR_DIR_FREE_KICK, "Goal_Left": World.M_OUR_GOAL, "offside_left": World.M_OUR_OFFSIDE,
"KickOff_Right":World.M_THEIR_KICKOFF, "KickIn_Right":World.M_THEIR_KICK_IN, "corner_kick_right":World.M_THEIR_CORNER_KICK,
"goal_kick_right":World.M_THEIR_GOAL_KICK, "free_kick_right":World.M_THEIR_FREE_KICK, "pass_right":World.M_THEIR_PASS,
"direct_free_kick_right": World.M_THEIR_DIR_FREE_KICK, "Goal_Right": World.M_THEIR_GOAL, "offside_right": World.M_THEIR_OFFSIDE,
"BeforeKickOff": World.M_BEFORE_KICKOFF, "GameOver": World.M_GAME_OVER, "PlayOn": World.M_PLAY_ON }
self.RIGHT_PLAY_MODE_TO_ID = {"KickOff_Left":World.M_THEIR_KICKOFF, "KickIn_Left":World.M_THEIR_KICK_IN, "corner_kick_left":World.M_THEIR_CORNER_KICK,
"goal_kick_left":World.M_THEIR_GOAL_KICK, "free_kick_left":World.M_THEIR_FREE_KICK, "pass_left":World.M_THEIR_PASS,
"direct_free_kick_left": World.M_THEIR_DIR_FREE_KICK, "Goal_Left": World.M_THEIR_GOAL, "offside_left": World.M_THEIR_OFFSIDE,
"KickOff_Right":World.M_OUR_KICKOFF, "KickIn_Right":World.M_OUR_KICK_IN, "corner_kick_right":World.M_OUR_CORNER_KICK,
"goal_kick_right":World.M_OUR_GOAL_KICK, "free_kick_right":World.M_OUR_FREE_KICK, "pass_right":World.M_OUR_PASS,
"direct_free_kick_right": World.M_OUR_DIR_FREE_KICK, "Goal_Right": World.M_OUR_GOAL, "offside_right": World.M_OUR_OFFSIDE,
"BeforeKickOff": World.M_BEFORE_KICKOFF, "GameOver": World.M_GAME_OVER, "PlayOn": World.M_PLAY_ON }
def find_non_digit(self,start):
while True:
if (self.exp[start] < ord('0') or self.exp[start] > ord('9')) and self.exp[start] != ord('.'): return start
start+=1
def find_char(self,start,char):
while True:
if self.exp[start] == char : return start
start+=1
def read_float(self, start):
if self.exp[start:start+3] == b'nan': return float('nan'), start+3 #handle nan values (they exist)
end = self.find_non_digit(start+1) #we assume the first one is a digit or minus sign
try:
retval = float(self.exp[start:end])
except:
self.world.log(f"{self.LOG_PREFIX}String to float conversion failed: {self.exp[start:end]} at msg[{start},{end}], \nMsg: {self.exp.decode()}")
retval = 0
return retval, end
def read_int(self, start):
end = self.find_non_digit(start+1) #we assume the first one is a digit or minus sign
return int(self.exp[start:end]), end
def read_bytes(self, start):
end = start
while True:
if self.exp[end] == ord(' ') or self.exp[end] == ord(')'): break
end+=1
return self.exp[start:end], end
def read_str(self, start):
b, end = self.read_bytes(start)
return b.decode(), end
def get_next_tag(self, start):
min_depth = self.depth
while True:
if self.exp[start] == ord(")") : #monitor xml element depth
self.depth -= 1
if min_depth > self.depth: min_depth = self.depth
elif self.exp[start] == ord("(") : break
start+=1
if start >= len(self.exp): return None, start, 0
self.depth += 1
start += 1
end = self.find_char(start, ord(" "))
return self.exp[start:end], end, min_depth
def parse(self, exp):
self.exp = exp #used by other member functions
self.depth = 0 #xml element depth
self.world.step += 1
self.world.line_count = 0
self.world.robot.frp = dict()
self.world.flags_posts = dict()
self.world.flags_corners = dict()
self.world.vision_is_up_to_date = False
self.world.ball_is_visible = False
self.world.robot.feet_toes_are_touching = dict.fromkeys(self.world.robot.feet_toes_are_touching, False)
self.world.time_local_ms += World.STEPTIME_MS
for p in self.world.teammates: p.is_visible = False
for p in self.world.opponents: p.is_visible = False
tag, end, _ = self.get_next_tag(0)
while end < len(exp):
if tag==b'time':
while True:
tag, end, min_depth = self.get_next_tag(end)
if min_depth == 0: break
if tag==b'now':
#last_time = self.world.time_server
self.world.time_server, end = self.read_float(end+1)
#Test server time reliability
#increment = self.world.time_server - last_time
#if increment < 0.019: print ("down",last_time,self.world.time_server)
#if increment > 0.021: print ("up",last_time,self.world.time_server)
else:
self.world.log(f"{self.LOG_PREFIX}Unknown tag inside 'time': {tag} at {end}, \nMsg: {exp.decode()}")
elif tag==b'GS':
while True:
tag, end, min_depth = self.get_next_tag(end)
if min_depth == 0: break
if tag==b'unum':
_, end = self.read_int(end+1) #We already know our unum
elif tag==b'team':
aux, end = self.read_str(end+1)
is_left = bool(aux == "left")
if self.world.team_side_is_left != is_left:
self.world.team_side_is_left = is_left
self.play_mode_to_id = self.LEFT_PLAY_MODE_TO_ID if is_left else self.RIGHT_PLAY_MODE_TO_ID
self.world.draw.set_team_side(not is_left)
self.world.team_draw.set_team_side(not is_left)
elif tag==b'sl':
if self.world.team_side_is_left:
self.world.goals_scored, end = self.read_int(end+1)
else:
self.world.goals_conceded, end = self.read_int(end+1)
elif tag==b'sr':
if self.world.team_side_is_left:
self.world.goals_conceded, end = self.read_int(end+1)
else:
self.world.goals_scored, end = self.read_int(end+1)
elif tag==b't':
self.world.time_game, end = self.read_float(end+1)
elif tag==b'pm':
aux, end = self.read_str(end+1)
if self.play_mode_to_id is not None:
self.world.play_mode = self.play_mode_to_id[aux]
else:
self.world.log(f"{self.LOG_PREFIX}Unknown tag inside 'GS': {tag} at {end}, \nMsg: {exp.decode()}")
elif tag==b'GYR':
while True:
tag, end, min_depth = self.get_next_tag(end)
if min_depth == 0: break
'''
The gyroscope measures the robot's torso angular velocity (rotation rate vector)
The angular velocity's orientation is given by the right-hand rule.
Original reference frame:
X:left(-)/right(+) Y:back(-)/front(+) Z:down(-)/up(+)
New reference frame:
X:back(-)/front(+) Y:right(-)/left(+) Z:down(-)/up(+)
'''
if tag==b'n':
pass
elif tag==b'rt':
self.world.robot.gyro[1], end = self.read_float(end+1)
self.world.robot.gyro[0], end = self.read_float(end+1)
self.world.robot.gyro[2], end = self.read_float(end+1)
self.world.robot.gyro[1] *= -1
else:
self.world.log(f"{self.LOG_PREFIX}Unknown tag inside 'GYR': {tag} at {end}, \nMsg: {exp.decode()}")
elif tag==b'ACC':
while True:
tag, end, min_depth = self.get_next_tag(end)
if min_depth == 0: break
'''
The accelerometer measures the acceleration relative to freefall. It will read zero during any type of free fall.
When at rest relative to the Earth's surface, it will indicate an upwards acceleration of 9.81m/s^2 (in SimSpark).
Original reference frame:
X:left(-)/right(+) Y:back(-)/front(+) Z:down(-)/up(+)
New reference frame:
X:back(-)/front(+) Y:right(-)/left(+) Z:down(-)/up(+)
'''
if tag==b'n':
pass
elif tag==b'a':
self.world.robot.acc[1], end = self.read_float(end+1)
self.world.robot.acc[0], end = self.read_float(end+1)
self.world.robot.acc[2], end = self.read_float(end+1)
self.world.robot.acc[1] *= -1
else:
self.world.log(f"{self.LOG_PREFIX}Unknown tag inside 'ACC': {tag} at {end}, \nMsg: {exp.decode()}")
elif tag==b'HJ':
while True:
tag, end, min_depth = self.get_next_tag(end)
if min_depth == 0: break
if tag==b'n':
joint_name, end = self.read_str(end+1)
joint_index = Robot.MAP_PERCEPTOR_TO_INDEX[joint_name]
elif tag==b'ax':
joint_angle, end = self.read_float(end+1)
#Fix symmetry issues 2/4 (perceptors)
if joint_name in Robot.FIX_PERCEPTOR_SET: joint_angle = -joint_angle
old_angle = self.world.robot.joints_position[joint_index]
self.world.robot.joints_speed[joint_index] = (joint_angle - old_angle) / World.STEPTIME * math.pi / 180
self.world.robot.joints_position[joint_index] = joint_angle
else:
self.world.log(f"{self.LOG_PREFIX}Unknown tag inside 'HJ': {tag} at {end}, \nMsg: {exp.decode()}")
elif tag==b'FRP':
while True:
tag, end, min_depth = self.get_next_tag(end)
if min_depth == 0: break
'''
The reference frame is used for the contact point and force vector applied to that point
Note: The force vector is applied to the foot, so it usually points up
Original reference frame:
X:left(-)/right(+) Y:back(-)/front(+) Z:down(-)/up(+)
New reference frame:
X:back(-)/front(+) Y:right(-)/left(+) Z:down(-)/up(+)
'''
if tag==b'n':
foot_toe_id, end = self.read_str(end+1)
self.world.robot.frp[foot_toe_id] = foot_toe_ref = np.empty(6)
self.world.robot.feet_toes_last_touch[foot_toe_id] = self.world.time_local_ms
self.world.robot.feet_toes_are_touching[foot_toe_id] = True
elif tag==b'c':
foot_toe_ref[1], end = self.read_float(end+1)
foot_toe_ref[0], end = self.read_float(end+1)
foot_toe_ref[2], end = self.read_float(end+1)
foot_toe_ref[1] *= -1
elif tag==b'f':
foot_toe_ref[4], end = self.read_float(end+1)
foot_toe_ref[3], end = self.read_float(end+1)
foot_toe_ref[5], end = self.read_float(end+1)
foot_toe_ref[4] *= -1
else:
self.world.log(f"{self.LOG_PREFIX}Unknown tag inside 'FRP': {tag} at {end}, \nMsg: {exp.decode()}")
elif tag==b'See':
self.world.vision_is_up_to_date = True
self.world.vision_last_update = self.world.time_local_ms
while True:
tag, end, min_depth = self.get_next_tag(end)
if min_depth == 0: break
tag_bytes = bytes(tag) #since bytearray is not hashable, it cannot be used as key for dictionaries
if tag==b'G1R' or tag==b'G2R' or tag==b'G1L' or tag==b'G2L':
_, end, _ = self.get_next_tag(end)
c1, end = self.read_float(end+1)
c2, end = self.read_float(end+1)
c3, end = self.read_float(end+1)
aux = self.LEFT_SIDE_FLAGS[tag_bytes] if self.world.team_side_is_left else self.RIGHT_SIDE_FLAGS[tag_bytes]
self.world.flags_posts[aux] = (c1,c2,c3)
elif tag==b'F1R' or tag==b'F2R' or tag==b'F1L' or tag==b'F2L':
_, end, _ = self.get_next_tag(end)
c1, end = self.read_float(end+1)
c2, end = self.read_float(end+1)
c3, end = self.read_float(end+1)
aux = self.LEFT_SIDE_FLAGS[tag_bytes] if self.world.team_side_is_left else self.RIGHT_SIDE_FLAGS[tag_bytes]
self.world.flags_corners[aux] = (c1,c2,c3)
elif tag==b'B':
_, end, _ = self.get_next_tag(end)
self.world.ball_rel_head_sph_pos[0], end = self.read_float(end+1)
self.world.ball_rel_head_sph_pos[1], end = self.read_float(end+1)
self.world.ball_rel_head_sph_pos[2], end = self.read_float(end+1)
self.world.ball_rel_head_cart_pos = M.deg_sph2cart(self.world.ball_rel_head_sph_pos)
self.world.ball_is_visible = True
self.world.ball_last_seen = self.world.time_local_ms
elif tag==b'mypos':
self.world.robot.cheat_abs_pos[0], end = self.read_float(end+1)
self.world.robot.cheat_abs_pos[1], end = self.read_float(end+1)
self.world.robot.cheat_abs_pos[2], end = self.read_float(end+1)
elif tag==b'myorien':
self.world.robot.cheat_ori, end = self.read_float(end+1)
elif tag==b'ballpos':
c1, end = self.read_float(end+1)
c2, end = self.read_float(end+1)
c3, end = self.read_float(end+1)
self.world.ball_cheat_abs_vel[0] = (c1 - self.world.ball_cheat_abs_pos[0]) / World.VISUALSTEP
self.world.ball_cheat_abs_vel[1] = (c2 - self.world.ball_cheat_abs_pos[1]) / World.VISUALSTEP
self.world.ball_cheat_abs_vel[2] = (c3 - self.world.ball_cheat_abs_pos[2]) / World.VISUALSTEP
self.world.ball_cheat_abs_pos[0] = c1
self.world.ball_cheat_abs_pos[1] = c2
self.world.ball_cheat_abs_pos[2] = c3
elif tag==b'P':
while True:
previous_depth = self.depth
previous_end = end
tag, end, min_depth = self.get_next_tag(end)
if min_depth < 2: #if =1 we are still inside 'See', if =0 we are already outside 'See'
end = previous_end #The "P" tag is special because it's the only variable particle inside 'See'
self.depth = previous_depth
break # we restore the previous tag, and let 'See' handle it
if tag==b'team':
player_team, end = self.read_str(end+1)
is_teammate = bool(player_team == self.world.team_name)
if self.world.team_name_opponent is None and not is_teammate: #register opponent team name
self.world.team_name_opponent = player_team
elif tag==b'id':
player_id, end = self.read_int(end+1)
player = self.world.teammates[player_id-1] if is_teammate else self.world.opponents[player_id-1]
player.body_parts_cart_rel_pos = dict() #reset seen body parts
player.is_visible = True
elif tag==b'llowerarm' or tag==b'rlowerarm' or tag==b'lfoot' or tag==b'rfoot' or tag==b'head':
tag_str = tag.decode()
_, end, _ = self.get_next_tag(end)
c1, end = self.read_float(end+1)
c2, end = self.read_float(end+1)
c3, end = self.read_float(end+1)
if is_teammate:
self.world.teammates[player_id-1].body_parts_sph_rel_pos[tag_str] = (c1,c2,c3)
self.world.teammates[player_id-1].body_parts_cart_rel_pos[tag_str] = M.deg_sph2cart((c1,c2,c3))
else:
self.world.opponents[player_id-1].body_parts_sph_rel_pos[tag_str] = (c1,c2,c3)
self.world.opponents[player_id-1].body_parts_cart_rel_pos[tag_str] = M.deg_sph2cart((c1,c2,c3))
else:
self.world.log(f"{self.LOG_PREFIX}Unknown tag inside 'P': {tag} at {end}, \nMsg: {exp.decode()}")
elif tag==b'L':
l = self.world.lines[self.world.line_count]
_, end, _ = self.get_next_tag(end)
l[0], end = self.read_float(end+1)
l[1], end = self.read_float(end+1)
l[2], end = self.read_float(end+1)
_, end, _ = self.get_next_tag(end)
l[3], end = self.read_float(end+1)
l[4], end = self.read_float(end+1)
l[5], end = self.read_float(end+1)
if np.isnan(l).any():
self.world.log(f"{self.LOG_PREFIX}Received field line with NaNs {l}")
else:
self.world.line_count += 1 #accept field line if there are no NaNs
else:
self.world.log(f"{self.LOG_PREFIX}Unknown tag inside 'see': {tag} at {end}, \nMsg: {exp.decode()}")
elif tag==b'hear':
team_name, end = self.read_str(end+1)
if team_name == self.world.team_name: # discard message if it's not from our team
timestamp, end = self.read_float(end+1)
if self.exp[end+1] == ord('s'): # this message was sent by oneself
direction, end = "self", end+5
else: # this message was sent by teammate
direction, end = self.read_float(end+1)
msg, end = self.read_bytes(end+1)
self.hear_callback(msg, direction, timestamp)
tag, end, _ = self.get_next_tag(end)
else:
self.world.log(f"{self.LOG_PREFIX}Unknown root tag: {tag} at {end}, \nMsg: {exp.decode()}")
tag, end, min_depth = self.get_next_tag(end)
|
2302_81918214/robocup3D
|
FCPCodebase-main/communication/World_Parser.py
|
Python
|
agpl-3.0
| 22,136
|
src = $(wildcard *.cpp)
obj = $(src:.c=.o)
CFLAGS = -O3 -shared -std=c++11 -fPIC -Wall $(PYBIND_INCLUDES)
all: $(obj)
g++ $(CFLAGS) -o a_star.so $^
debug: $(filter-out lib_main.cpp,$(obj))
g++ -O0 -std=c++14 -Wall -g -o debug.bin debug_main.cc $^
.PHONY: clean
clean:
rm -f $(obj) all
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/a_star/Makefile
|
Makefile
|
agpl-3.0
| 292
|
#include "a_star.h"
#include "expansion_groups.h"
#include <cmath>
#include <algorithm>
#include <chrono>
#define SQRT2 1.414213562373095f
#define LINES 321
#define COLS 221
#define MAX_RADIUS 5 // obstacle max radius in meters
#define IN_GOAL_LINE 312 // target line when go_to_goal is 'true' (312 -> 15.2m)
using std::chrono::high_resolution_clock;
using std::chrono::duration_cast;
using std::chrono::microseconds;
/*
Map dimensions: 32m*22m
col 0 ... col 220
line 0
--- our goal --- line 1
| | line 2
| | line 3
|--------------| ...
| | line 317
| | line 318
-- their goal -- line 319
line 320
[(H)ard wall: -3, (S)oft wall: -2, (E)mpty: 0, 0 < Cost < inf]
*/
// build board cost statically
#define H27 -3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3,-3
#define S11 -2,-2,-2,-2,-2,-2,-2,-2,-2,-2,-2
#define S19 S11,-2,-2,-2,-2,-2,-2,-2,-2
#define S97 S11,S11,S11,S11,S11,S11,S11,S11,-2,-2,-2,-2,-2,-2,-2,-2,-2
#define S98 S11,S11,S11,S11,S11,S11,S11,S11,-2,-2,-2,-2,-2,-2,-2,-2,-2,-2
#define S221 S98,S98,S11,S11,-2,-2,-2
#define E19 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
#define E197 E19,E19,E19,E19,E19,E19,E19,E19,E19,E19,0,0,0,0,0,0,0
#define L0 S221 // Line 0: soft W
#define L0_1 L0,L0
#define L2 S97,H27,S97 // Line 2: soft W, (goal post, back net, goal post), soft W
#define L2_5 L2,L2,L2,L2
#define L6 S98,-3,-3,-3,S19,-3,-3,-3,S98 // Line 6: soft W, goal post, soft W, goal post, soft W
#define L6_10 L6,L6,L6,L6,L6
#define L11 S98,-2,-3,-3,S19,-3,-3,-2,S98 // Line 11:soft W, empty field, goal post, soft W, goal post,empty field, soft W
#define L12 S11,-2,E197,-2,S11 // Line 12:soft W, empty field, soft W
#define L12x33 L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12,L12
#define LIN12_308 L12x33,L12x33,L12x33,L12x33,L12x33,L12x33,L12x33,L12x33,L12x33
#define L309 S98,-2,-3,-3,E19,-3,-3,-2,S98 // Line 309: soft W, empty field, goal post, empty field, goal post,empty field, soft W
#define L310 S98,-3,-3,-3,E19,-3,-3,-3,S98 // Line 310: soft W, goal post, inside goal, goal post, soft W
#define L310_314 L310,L310,L310,L310,L310
using std::min;
using std::max;
#define MIN min_node
Node* min_node; // non-expanded node with lowest predicted total cost (f)
namespace open{
Node* insert(Node* new_node, Node* root) {
new_node->left = nullptr;
new_node->right = nullptr;
// Empty BST, return without saving min_node
if(root == nullptr){
new_node->up = nullptr;
MIN = new_node; // save min node for fast access
return new_node;
}
// If new_node is the new min node
if(new_node->f < MIN->f){
MIN->left = new_node;
new_node->up = MIN;
MIN = new_node;
return root;
}
Node* node = root;
float key = new_node->f;
while(true){
if (key < node->f)
if(node->left == nullptr){
node->left = new_node;
break;
}else{
node = node->left;
}
else{
if(node->right == nullptr){
node->right = new_node;
break;
}else{
node = node->right;
}
}
}
new_node->up = node;
return root;
}
// Remove min node
Node* pop(Node* root) {
// Minimum node can have right child, but not left child
if (MIN->right == nullptr){
if(MIN == root){ //------(A)------ min node is root and has no children
return nullptr; // BST is empty
}
MIN->up->left = nullptr; //------(B)------ min node has no children but has parent
MIN = MIN->up;
}else{
if(MIN == root){ //------(C)------ min node is root and has right child
MIN = MIN->right;
root = MIN;
root->up = nullptr;
while(MIN->left != nullptr){ // update new min node
MIN = MIN->left;
}
return root; // right child is now root
}
MIN->right->up = MIN->up; //------(D)------ min node has right child and parent
MIN->up->left = MIN->right;
MIN = MIN->right;
while(MIN->left != nullptr){ // update new min node
MIN = MIN->left;
}
}
return root;
}
// Remove specific node
Node* delete_node(Node* node, Node* root) {
if(node == MIN){ // remove min node
return pop(root);
}
if(node->left==nullptr and node->right==nullptr){ //------(A)------ node has no children (it can't be root, otherwise it would be min node)
// Redirect incoming connection
if(node->up->left == node){ node->up->left = nullptr; }
else{ node->up->right = nullptr; }
}else if(node->left==nullptr){ //------(B)------ node has right child (it can't be root, otherwise it would be min node)
// Redirect incoming connections
node->right->up = node->up;
if(node->up->left == node){ node->up->left = node->right; }
else{ node->up->right = node->right; }
}else if(node->right==nullptr){ //------(C)------ node has left child
if(node == root){
node->left->up = nullptr;
return node->left; // left child becomes root
}
// Redirect incoming connections (if not root)
node->left->up = node->up;
if(node->up->left == node){ node->up->left = node->left; }
else{ node->up->right = node->left; }
}else{ //------(D)------ node has 2 children
Node *successor = node->right;
if(successor->left == nullptr){ //----- if successor is the node's right child (successor has no left child)
//-------------- successor replaces node
// Outgoing connections (successor's right child is not changed)
successor->left = node->left;
successor->up = node->up; // if node is root this is also ok
// Incoming connections
node->left->up = successor;
if(node == root){ return successor; } // successor becomes root
// Incoming connections (if not root)
if(node->up->left == node){ node->up->left = successor; }
else{ node->up->right = successor; }
}else{ //------ if successor is deeper (successor has no left child, and is itself a left child)
do{
successor = successor->left;
}while(successor->left != nullptr);
//-------------- Remove successor by redirecting its incoming connections
if(successor->right==nullptr){ // no children
successor->up->left = nullptr;
}else{
successor->up->left = successor->right;
successor->right->up = successor->up;
}
//-------------- successor replaces node
// Outgoing connections
successor->left = node->left;
successor->right = node->right;
successor->up = node->up; // if node is root this is also ok
// Incoming connections
node->left->up = successor;
node->right->up = successor;
if(node == root){ return successor; } // successor becomes root
// Incoming connections (if not root)
if(node->up->left == node){ node->up->left = successor; }
else{ node->up->right = successor; }
}
}
return root;
}
// Inorder Traversal
// void inorder(Node* root, Node* board) {
// if (root != nullptr) {
// // Traverse left
// inorder(root->left, board);
// // Traverse root
// std::cout << (root-board)/COLS << " " << (root-board)%COLS << " -> ";
// // Traverse right
// inorder(root->right, board);
// }
// return;
// }
}
inline int x_to_line(float x){
return int(fmaxf(0.f, fminf(10*x+160, 320.f)) + 0.5f);
}
inline int y_to_col(float y){
return int(fmaxf(0.f, fminf(10*y+110, 220.f)) + 0.5f);
}
inline float diagonal_distance(bool go_to_goal, int line, int col, int end_l, int end_c){
// diagonal distance - adapted from http://theory.stanford.edu/~amitp/GameProgramming/Heuristics.html
int dl, dc;
if( go_to_goal ){
dl = abs(IN_GOAL_LINE - line);
if (col>119) { dc = col-119; }
else if (col<101) { dc = 101-col; }
else { dc = 0; }
}else{
dl = abs(line - end_l);
dc = abs(col - end_c);
}
return (dl + dc) - 0.585786437626905f * min(dl,dc);
}
inline Node* expand_child(Node* open_root, float cost, float wall_index, Node* curr_node, Node* board, int pos, int state,
bool go_to_goal, int line, int col, int end_l, int end_c, unsigned int* node_state, float extra ){
// child can be as inaccessible as current pos (but there is a cost penalty to avoid inaccessible paths)
if(cost <= wall_index){
cost = 100.f;
}
// g (min cost from start to n)
float g = curr_node->g + extra + std::fmaxf(0.f,cost); // current cost + child distance
Node* child = &board[pos];
// if child is already in the open set
if (state){
if (g >= child->g){
return open_root; // if not an improvement, we discard the new child
}else{
open_root = open::delete_node(child, open_root); // if it is an improvement: remove reference, update it, add it again in correct order
}
}else{
node_state[pos] = 1;
}
// f (prediction of min total cost passing through n)
float f = g + diagonal_distance(go_to_goal,line,col,end_l,end_c);
child->g = g;
child->f = f;
child->parent = curr_node;
return open::insert(child, open_root);
}
float final_path[2050];
int final_path_size;
inline void build_final_path(Node* const best_node, const Node* board, float status, const bool override_end=false, const float end_x=0, const float end_y=0){
// Node* pt = best_node;
// while( pt != nullptr ){
// int pos = pt - board;
// board_cost[pos] = -4;
// pt = pt->parent;
// }
// std::cout << "\n";
// for(int l=l_min; l<=l_max; l++){
// for(int c=c_min; c<=c_max; c++){
// //[(H)ard wall: -3, (S)oft wall: -2, (E)mpty: 0, 0 < Cost < inf]
// //if (board[l][c].closed) std::cout << "o";
// if (board_cost[l*COLS+c] == -3) std::cout << "h";
// else if (board_cost[l*COLS+c] == -2) std::cout << "s";
// else if (board_cost[l*COLS+c] == -4) std::cout << ".";
// else if (board_cost[l*COLS+c] == -1) std::cout << "g";
// else if (board_cost[l*COLS+c] == 0 and node_state[l*COLS+c]==2) std::cout << "o";
// else if (board_cost[l*COLS+c] == 0) std::cout << " ";
// //ele ainda nao sabe ler hard walls
// else std::cout << int(board_cost[l*COLS+c]+0.5f);
// }
// std::cout << "\n";
// }
// Using 'current_node' would suffice if A* reaches the objective (but 'best_node' works with impossible paths or timeout)
Node* ptr = best_node;
int counter=0;
do{
ptr = ptr->parent;
counter++;
}while( ptr != nullptr );
final_path_size = min(counter*2,2048);
ptr = best_node;
int i = final_path_size-1;
// if enabled, replace end point with correct coordinates instead of discrete version
if(override_end){
final_path[i--] = end_y;
final_path[i--] = end_x;
ptr = ptr->parent;
}
for(; i>0;){
final_path[i--] = ((ptr-board) % COLS)/10.f-11.f; // y
final_path[i--] = ((ptr-board) / COLS)/10.f-16.f; // x
ptr = ptr->parent;
}
// add status (& increment path size)
final_path[final_path_size++] = status; // 0-success, 1-timeout, 2-impossible, 3-no obstacles(this one is not done in this function)
// add cost (& increment path size)
final_path[final_path_size++] = best_node->g / 10.f; // min. A* cost from start to best_node
}
/**
* @brief Returns true if line segment 'ab' intersects either goal (considering the unreachable area)
* - This function assumes that 'a' and 'b' are two points outside the unreachable goal area
* - Therefore, 'ab' must enter and exit the goal unreachable area
* - To detect this, we consider only the intersection of 'ab' and the goal outer borders (back+sides)
* - The front should already be covered by independent goal posts checks
*/
inline bool does_intersect_any_goal(float a_x, float a_y, float b_x, float b_y){
float ab_x = b_x - a_x;
float ab_y = b_y - a_y;
float k;
if(ab_x != 0){ // Check if 'ab' and goal back is noncollinear (collinear intersections are ignored)
k = (15.75-a_x) / ab_x; // a_x + ab_x*k = 15.75
if (k >= 0 and k <= 1 and fabsf(a_y + ab_y * k) <= 1.25){ // collision (intersection_y = a_y + ab_y*k)
return true;
}
k = (-15.75-a_x) / ab_x; // a_x + ab_x*k = -15.75
if (k >= 0 and k <= 1 and fabsf(a_y + ab_y * k) <= 1.25){ // collision (intersection_y = a_y + ab_y*k)
return true;
}
}
if(ab_y != 0){ // Check if 'ab' and goal sides are noncollinear (collinear intersections are ignored)
k = (1.25-a_y) / ab_y; // a_y + ab_y*k = 1.25
if( k >= 0 and k <= 1){
float intersection_x_abs = fabsf(a_x + ab_x * k);
if( intersection_x_abs >= 15 and intersection_x_abs <= 15.75 ){ // check one side for both goals at same time
return true;
}
}
k = (-1.25-a_y) / ab_y; // a_y + ab_y*k = -1.25
if( k >= 0 and k <= 1){
float intersection_x_abs = fabsf(a_x + ab_x * k);
if( intersection_x_abs >= 15 and intersection_x_abs <= 15.75 ){ // check one side for both goals at same time
return true;
}
}
}
return false;
}
/**
* @brief Add space cushion near the midlines and endlines
*/
inline void add_space_cushion(float board_cost[]){
#define CUSHION_WIDTH 6
// opponent goal line
for(int i=0; i<CUSHION_WIDTH; i++){
int ii = (i+12)*COLS;
for(int j=12+i; j<209-i; j++){
board_cost[ii+j] = CUSHION_WIDTH-i;
}
}
// our goal line
for(int i=0; i<CUSHION_WIDTH; i++){
int ii = (308-i)*COLS;
for(int j=12+i; j<99; j++){
board_cost[ii+j] = CUSHION_WIDTH-i;
board_cost[ii+220-j] = CUSHION_WIDTH-i;
}
}
// sidelines
for(int i=0; i<CUSHION_WIDTH; i++){
for(int j=(13+i)*COLS; j<(308-i)*COLS; j+=COLS){
board_cost[j+i+12] = CUSHION_WIDTH-i;
board_cost[j-i+208] = CUSHION_WIDTH-i;
}
}
}
/**
* @brief This function checks if a straight path from start to end is obstructed by obstacles
* Returning 'false' means that we do not need A* to solve this problem
*
* Normal case (start != end):
* The path is obstructed if it intersects any hard or soft circumference
* Special case (start == end):
* The path is obstructed if start is inside any hard circumference
*/
bool is_path_obstructed(float start_x, float start_y, float end_x, float end_y, float given_obstacles[],
int given_obst_size, bool go_to_goal, int wall_index, float board_cost[]){
// Restrict start coordinates to map
start_x = max(-16.f, min(start_x, 16.f));
start_y = max(-11.f, min(start_y, 11.f));
int s_lin = x_to_line(start_x);
int e_lin = x_to_line(end_x);
int s_col = y_to_col(start_y);
int e_col = y_to_col(end_y);
float s_cost = board_cost[s_lin*COLS+s_col];
float e_cost = board_cost[e_lin*COLS+e_col];
// Path is obvious if start/end are is same or adjacent cells
bool is_near = abs(s_lin - e_lin) <= 1 and abs(s_col - e_col) <= 1;
// Let A* handle it if the start position is unreachable or (out of bounds is not allowed && is nearly out of bounds && not near end)
if(s_cost <= wall_index or (!is_near and s_cost > 0)){
return true;
}
if (go_to_goal){ // This is a safe target. If it generates a collision with any goal post, we use A* instead.
end_x = 15.2;
end_y = max(-0.8f, min(start_y, 0.8f));
}else{ // Restrict end coordinates to map
end_x = max(-16.f, min(end_x, 16.f));
end_y = max(-11.f, min(end_y, 11.f));
// Let A* handle it if the end position is unreachable or (is nearly out of bounds && out of bounds is not allowed && not near end)
if(e_cost <= wall_index or (!is_near and e_cost > 0)){
return true;
}
}
/**
* Check if path intersects either goal (considering the unreachable area)
* - at this point we know that 'start' and 'end' are reachable
* - Therefore, the path must enter and exit the goal unreachable area for an intersection to exist
* - To detect this, we consider only the intersection of the path and the goal outer borders (back+sides)
* - The front is covered next by goal posts checks
*/
if (does_intersect_any_goal(start_x, start_y, end_x, end_y)) {
return true;
}
/**
* ----------------------- List all obstacles: given obstacles + goal posts
* note that including the goal posts in the given obstacles is not a bad idea since the default
* goal posts we add here provide no space cushion (which may be needed if the robot is not precise)
* values explanation:
* - goal post location (tested in simulator, collision with robot, robot is sideways to the goal post and arms are close to body)
* - hard radius (tested in the same way, robot collides when closer than 0.15m from goal post border)
* post radius 0.02 + agent radius 0.15 = 0.17
* - largest radius (equivalent to hard radius, since there is no soft radius)
*/
float obst[given_obst_size*4/5+16] = {
15.02, 1.07, 0.17, 0.17,
15.02, -1.07, 0.17, 0.17,
-15.02, 1.07, 0.17, 0.17,
-15.02, -1.07, 0.17, 0.17
}; // x, y, hard radius, largest radius
int obst_size = 16;
for(int i=0; i<given_obst_size; i+=5){
obst[obst_size++] = given_obstacles[i]; // x
obst[obst_size++] = given_obstacles[i+1]; // y
obst[obst_size++] = fmaxf( 0, fminf(given_obstacles[i+2], MAX_RADIUS) ); // hard radius
obst[obst_size++] = fmaxf( 0, fminf(max(given_obstacles[i+2], given_obstacles[i+3]), MAX_RADIUS) ); // largest radius
}
//------------------------ Special case (start ~= end): the path is obstructed if start or end are inside any hard circumference
if( is_near ){
for(int ob=0; ob<obst_size; ob+=4){
float c_x = obst[ob]; // obstacle center
float c_y = obst[ob+1]; // obstacle center
float hard_radius = obst[ob+2]; // hard radius
float r_sq = hard_radius * hard_radius; // squared radius
float sc_x = c_x - start_x;
float sc_y = c_y - start_y;
float ec_x = c_x - end_x;
float ec_y = c_y - end_y;
if(sc_x*sc_x + sc_y*sc_y <= r_sq or ec_x*ec_x + ec_y*ec_y <= r_sq){ // check distance: center<->start center<->end
return true;
}
}
}else{
//-------------------- Normal case (start !~= end): the path is obstructed if it intersects any hard or soft circumference
// for each obstacle: check if circle intersects line segment (start-end)
for(int ob=0; ob<obst_size; ob+=4){
float c_x = obst[ob]; // obstacle center
float c_y = obst[ob+1]; // obstacle center
float largest_radius = obst[ob+3]; // largest radius
float r_sq = largest_radius * largest_radius; // squared radius
float sc_x = c_x - start_x;
float sc_y = c_y - start_y;
float se_x = end_x - start_x;
float se_y = end_y - start_y;
float sc_proj_scale = (sc_x*se_x + sc_y*se_y) / (se_x*se_x + se_y*se_y); // scale = projection length / target vector length
float sc_proj_x = se_x * sc_proj_scale; // projection of start->center onto start->end
float sc_proj_y = se_y * sc_proj_scale; // projection of start->center onto start->end
// check if projection falls on top of trajectory (start->projection = k * start->end)
float k = abs(se_x)>abs(se_y) ? sc_proj_x/se_x : sc_proj_y/se_y; // we use the largest dimension of start->end to avoid division by 0
if(k <= 0){
if(sc_x*sc_x + sc_y*sc_y <= r_sq){ // check distance: center<->start
return true;
}
}else if(k >= 1){
float ec_x = c_x - end_x;
float ec_y = c_y - end_y;
if(ec_x*ec_x + ec_y*ec_y <= r_sq){ // check distance: center<->end
return true;
}
}else{
float proj_c_x = c_x - (sc_proj_x + start_x);
float proj_c_y = c_y - (sc_proj_y + start_y);
if(proj_c_x*proj_c_x + proj_c_y*proj_c_y <= r_sq){ // check distance: center<->projection
return true;
}
}
}
}
float path_x = end_x - start_x;
float path_y = end_y - start_y;
final_path_size = 6;
final_path[0] = start_x;
final_path[1] = start_y;
final_path[2] = end_x;
final_path[3] = end_y;
final_path[4] = 3; // status: 3-no obstacles
final_path[5] = sqrtf(path_x*path_x+path_y*path_y) + max(0.f, e_cost/10.f); // min. A* cost from start to end (e_cost is added even if start==end to help debug cell costs)
return false; // no obstruction was found
}
// opponent players + active player + restricted areas (from referee)
// data:
// [start x][start y]
// [allow out of bounds?][go to goal?]
// [optional target x][optional target y]
// [timeout]
// [x][y][hard radius][soft radius][force]
void astar(float params[], int params_size){
auto t1 = high_resolution_clock::now();
const float s_x = params[0]; // start x
const float s_y = params[1]; // start y
const bool allow_out_of_bounds = params[2];
const int wall_index = allow_out_of_bounds ? -3 : -2; // (cost <= wall_index) means 'unreachable'
const bool go_to_goal = params[3];
const float opt_t_x = params[4]; // optional target x
const float opt_t_y = params[5]; // optional target y
const int timeout_us = params[6];
float* obstacles = ¶ms[7];
int obst_size = params_size-7; // size of obstacles array
//======================================================== Populate board 0: add field layout
float board_cost[LINES*COLS] = {L0_1,L2_5,L6_10,L11,LIN12_308,L309,L310_314,L2_5,L0_1};
if (!allow_out_of_bounds){ // add cost to getting near sideline or endline (except near goal)
add_space_cushion(board_cost);
}
//======================================================== Check if path is obstructed
if (!is_path_obstructed(s_x, s_y, opt_t_x, opt_t_y, obstacles, obst_size, go_to_goal, wall_index, board_cost)){
return; // return if path is not obstructed
}
//======================================================== Define board basics (start, end, limits)
// if the start point is out of field, it is brought in
const int start_l = x_to_line(s_x);
const int start_c = y_to_col(s_y);
const int start_pos = start_l * COLS + start_c;
// define objective (go to goal or a specific point)
int end_l, end_c;
if(!go_to_goal){
end_l = x_to_line(opt_t_x);
end_c = y_to_col(opt_t_y);
}else{
end_l = IN_GOAL_LINE;
}
// define board limits considering the initial and final positions (and obstacles in the next section, and goals after that)
int l_min = min(start_l, end_l);
int l_max = max(start_l, end_l);
int c_min, c_max;
if(go_to_goal){
c_min = min(start_c,119);
c_max = max(start_c,101);
}else{
c_min = min(start_c, end_c);
c_max = max(start_c, end_c);
}
if (!allow_out_of_bounds){ // workspace must contain a bit of empty field if out of bounds is not allowed
l_min = min(l_min, 306);
l_max = max(14, l_max);
c_min = min(c_min, 206);
c_max = max(14, c_max);
}
//======================================================== Initialize A*
Node* open_root = nullptr;
Node board[LINES*COLS];
unsigned int node_state[LINES*COLS] = {0}; //0-unknown, 1-open, 2-closed
//======================================================== Populate board 1: convert obstacles to cost
for(int ob=0; ob<obst_size; ob+=5){
int lin = x_to_line(obstacles[ob]);
int col = y_to_col(obstacles[ob+1]);
float hard_radius = fmaxf( 0, fminf(obstacles[ob+2], MAX_RADIUS) );
float soft_radius = fmaxf( 0, fminf(obstacles[ob+3], MAX_RADIUS) );
float force = obstacles[ob+4];
float f_per_m = force / soft_radius; // force per meter
int max_r = int( fmaxf(hard_radius, soft_radius)*10.f+1e-4 ); // add epsilon to avoid potential rounding error in expansion groups
l_min = min(l_min, lin - max_r - 1 );
l_max = max(l_max, lin + max_r + 1 );
c_min = min(c_min, col - max_r - 1 );
c_max = max(c_max, col + max_r + 1 );
//=============================================================== hard radius
int i=0;
for(; i<expansion_positions_no and expansion_pos_dist[i] <= hard_radius; i++){
int l = lin + expansion_pos_l[i];
int c = col + expansion_pos_c[i];
if(l>=0 and c>=0 and l<LINES and c<COLS){
board_cost[l*COLS+c] = -3;
}
}
//=============================================================== soft radius
for(; i<expansion_positions_no and expansion_pos_dist[i] <= soft_radius; i++){
int l = lin + expansion_pos_l[i];
int c = col + expansion_pos_c[i];
int p = l*COLS+c;
float cost = board_cost[p];
float fr = force-(f_per_m * expansion_pos_dist[i]);
if(l>=0 and c>=0 and l<LINES and c<COLS and cost > wall_index and cost < fr){
board_cost[p] = fr;
}
}
}
// adjust board limits if working area overlaps goal area (which includes walking margin)
if (c_max > 96 and c_min < 124){ // Otherwise it does not overlap any goal
if (l_max > 1 and l_min < 12 ){ // Overlaps our goal
l_max = max(12,l_max); // Extend working area to include our goal
l_min = min(l_min,1);
c_max = max(124,c_max);
c_min = min(c_min,96);
}
if (l_max > 308 and l_min < 319 ){ // Overlaps their goal
l_max = max(319,l_max); // Extend working area to include their goal
l_min = min(l_min,308);
c_max = max(124,c_max);
c_min = min(c_min,96);
}
}
//======================================================== Populate board 2: add objective
// Explanation: if objective is not accessible we do not add it to the map, although its reference still exists.
// Therefore, we know how far we are from that reference, but it cannot be reached.
// Even if we are on top of the objective, the idea is to get away, to the nearest valid position.
if(!go_to_goal){
float *end_cost = &board_cost[end_l*COLS+end_c];
if(*end_cost > wall_index){
*end_cost = -1;
}
}else{
for(int i=IN_GOAL_LINE*COLS+101; i<=IN_GOAL_LINE*COLS+119; i++){
if(board_cost[i] > wall_index){
board_cost[i] = -1;
}
}
}
// add board limits as an additional restriction to workspace
l_min = max(0, l_min);
l_max = min(l_max, 320);
c_min = max(0, c_min);
c_max = min(c_max, 220);
// add start node to open list (it will be closed right away, so there is not need to set it as open)
board[start_pos].g = 0; // This is needed to compute the cost of child nodes, but f is not needed because there are no comparisons with other nodes in the open BST
board[start_pos].parent = nullptr; //This is where the path ends
open_root = open::insert(&board[start_pos], open_root);
int measure_timeout=0;
Node* best_node = &board[start_pos]; // save best node based on distance to goal (useful if impossible/timeout to get best path)
float best_node_dist = std::numeric_limits<float>::max(); // infinite distance if start is itself unreachable
if(board_cost[start_pos] > wall_index){
best_node_dist = diagonal_distance(go_to_goal,start_l,start_c,end_l,end_c);
}
//======================================================== A* algorithm
while (open_root != nullptr){
// Get next best node (lowest predicted total cost (f))
Node* curr_node = min_node;
const int curr_pos = curr_node - board;
const int curr_line = curr_pos / COLS;
const int curr_col = curr_pos % COLS;
const float curr_cost = board_cost[curr_pos];
measure_timeout = (measure_timeout+1) & 31; // check timeout at every 32 iterations
// save best node based on distance to goal (useful if impossible/timeout)
if(curr_cost > wall_index){
float dd = diagonal_distance(go_to_goal,curr_line,curr_col,end_l,end_c);
if(best_node_dist > dd){
best_node = curr_node;
best_node_dist = dd;
}
}
open_root = open::pop(open_root);
node_state[curr_pos] = 2;
// Check if we reached objective
if( curr_cost == -1 ){
// replace end point with correct coordinates instead of discrete version if the optional target was defined (not going to goal)
build_final_path(best_node, board, 0, !go_to_goal, opt_t_x, opt_t_y);
return;
}
if( measure_timeout==0 and duration_cast<microseconds>(high_resolution_clock::now() - t1).count() > timeout_us ){
build_final_path(best_node, board, 1);
return;
}
// Expand child nodes
bool rcol_ok = curr_col < c_max;
bool lcol_ok = curr_col > c_min;
if(curr_line > l_min){
int line = curr_line - 1;
int col = curr_col - 1;
int pos = curr_pos - COLS - 1;
float cost = board_cost[pos];
int state = node_state[pos];
// check if not an obstacle and if node is not closed (child can be as inaccessible as current pos)
if (state!=2 and !(cost <= wall_index and cost < curr_cost) and lcol_ok){
open_root = expand_child(open_root,cost,wall_index,curr_node,board,pos,state,go_to_goal,line,col,end_l,end_c,node_state,SQRT2);
}
col++;
pos++;
cost = board_cost[pos];
state = node_state[pos];
// check if not an obstacle and if node is not closed (child can be as inaccessible as current pos)
if (state!=2 and !(cost <= wall_index and cost < curr_cost)){
open_root = expand_child(open_root,cost,wall_index,curr_node,board,pos,state,go_to_goal,line,col,end_l,end_c,node_state,1);
}
col++;
pos++;
cost = board_cost[pos];
state = node_state[pos];
// check if not an obstacle and if node is not closed (child can be as inaccessible as current pos)
if (state!=2 and !(cost <= wall_index and cost < curr_cost) and rcol_ok){
open_root = expand_child(open_root,cost,wall_index,curr_node,board,pos,state,go_to_goal,line,col,end_l,end_c,node_state,SQRT2);
}
}
if(curr_line < l_max){
int line = curr_line + 1;
int col = curr_col - 1;
int pos = curr_pos + COLS - 1;
float cost = board_cost[pos];
int state = node_state[pos];
// check if not an obstacle and if node is not closed (child can be as inaccessible as current pos)
if (state!=2 and !(cost <= wall_index and cost < curr_cost) and lcol_ok){
open_root = expand_child(open_root,cost,wall_index,curr_node,board,pos,state,go_to_goal,line,col,end_l,end_c,node_state,SQRT2);
}
col++;
pos++;
cost = board_cost[pos];
state = node_state[pos];
// check if not an obstacle and if node is not closed (child can be as inaccessible as current pos)
if (state!=2 and !(cost <= wall_index and cost < curr_cost)){
open_root = expand_child(open_root,cost,wall_index,curr_node,board,pos,state,go_to_goal,line,col,end_l,end_c,node_state,1);
}
col++;
pos++;
cost = board_cost[pos];
state = node_state[pos];
// check if not an obstacle and if node is not closed (child can be as inaccessible as current pos)
if (state!=2 and !(cost <= wall_index and cost < curr_cost) and rcol_ok){
open_root = expand_child(open_root,cost,wall_index,curr_node,board,pos,state,go_to_goal,line,col,end_l,end_c,node_state,SQRT2);
}
}
{
int col = curr_col - 1;
int pos = curr_pos - 1;
float cost = board_cost[pos];
int state = node_state[pos];
// check if not an obstacle and if node is not closed (child can be as inaccessible as current pos)
if (state!=2 and !(cost <= wall_index and cost < curr_cost) and lcol_ok){
open_root = expand_child(open_root,cost,wall_index,curr_node,board,pos,state,go_to_goal,curr_line,col,end_l,end_c,node_state,1);
}
col+=2;
pos+=2;
cost = board_cost[pos];
state = node_state[pos];
// check if not an obstacle and if node is not closed (child can be as inaccessible as current pos)
if (state!=2 and !(cost <= wall_index and cost < curr_cost) and rcol_ok){
open_root = expand_child(open_root,cost,wall_index,curr_node,board,pos,state,go_to_goal,curr_line,col,end_l,end_c,node_state,1);
}
}
}
build_final_path(best_node, board, 2);
return;
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/a_star/a_star.cpp
|
C++
|
agpl-3.0
| 36,022
|
#pragma once
/**
* FILENAME: a_star.h
* DESCRIPTION: custom A* pathfinding implementation, optimized for the soccer environment
* AUTHOR: Miguel Abreu (m.abreu@fe.up.pt)
* DATE: 2022
*/
struct Node{
//------------- BST parameters
Node* left;
Node* right;
Node* up;
//------------- A* parameters
Node* parent;
float g;
float f;
};
extern void astar(float params[], int params_size);
extern float final_path[2050];
extern int final_path_size;
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/a_star/a_star.h
|
C
|
agpl-3.0
| 503
|
#include "a_star.h"
#include <chrono>
#include <iostream>
using std::chrono::high_resolution_clock;
using std::chrono::duration_cast;
using std::chrono::microseconds;
std::chrono::_V2::system_clock::time_point t1,t2;
float params[] = {
15.78,-0.07, //start
1,1, //out of bounds? go to goal?
0,0, //target (if not go to goal)
500000, // timeout
-10,0,1,5,5,
-10,1,1,7,10,
-10,-7,0,5,1
};
int params_size = sizeof(params)/sizeof(params[0]);
int main(){
t1 = high_resolution_clock::now();
astar(params, params_size);
t2 = high_resolution_clock::now();
std::cout << duration_cast<microseconds>(t2 - t1).count() << "us (includes initialization)\n";
t1 = high_resolution_clock::now();
astar(params, params_size);
t2 = high_resolution_clock::now();
std::cout << duration_cast<microseconds>(t2 - t1).count() << "us\n";
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/a_star/debug_main.cc
|
C++
|
agpl-3.0
| 885
|
const int expansion_positions_no = 7845;
const float expansion_pos_dist[7845] = 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246,4.98196748283246,4.98196748283246,4.98196748283246,4.98196748283246,4.98196748283246,4.98196748283246,4.98196748283246,4.98196748283246,4.98196748283246,4.992995093127972,4.992995093127972,4.992995093127972,4.992995093127972,4.992995093127972,4.992995093127972,4.992995093127972,4.992995093127972,4.99799959983992,4.99799959983992,4.99799959983992,4.99799959983992,4.99799959983992,4.99799959983992,4.99799959983992,4.99799959983992,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0};
const int expansion_pos_l[7845] = 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const int expansion_pos_c[7845] = 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|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/a_star/expansion_groups.h
|
C
|
agpl-3.0
| 191,723
|
from os import getcwd
from os.path import join, dirname
from math import sqrt
MAX_RADIUS = 5
LINES = 321
COLS = 221
expansion_groups = dict()
CWD = join(getcwd(), dirname(__file__))
for l in range(MAX_RADIUS*10+1):
for c in range(MAX_RADIUS*10+1):
dist = sqrt(l*l+c*c)*0.1
if dist <= MAX_RADIUS:
if not dist in expansion_groups:
expansion_groups[dist] = []
expansion_groups[dist].append((l,c))
if c>0: expansion_groups[dist].append((l,-c))
if l>0: expansion_groups[dist].append((-l,c))
if c>0 and l>0: expansion_groups[dist].append((-l,-c))
ascending_dist = sorted(expansion_groups)
groups_no = len(expansion_groups)
#============================================= prepare text to file
no_of_pos = 0
for g in expansion_groups.values():
no_of_pos += len(g)
file = []
file.append(f"const int expansion_positions_no = {no_of_pos};\n")
file.append(f"const float expansion_pos_dist[{no_of_pos}] = {{")
for dist in ascending_dist:
for p in expansion_groups[dist]:
file.extend([f"{dist}",","])
file.pop()
file.append(f"}};\n\nconst int expansion_pos_l[{no_of_pos}] = {{")
for dist in ascending_dist:
for p in expansion_groups[dist]:
file.extend([f"{p[0]}",","])
file.pop()
file.append(f"}};\n\nconst int expansion_pos_c[{no_of_pos}] = {{")
for dist in ascending_dist:
for p in expansion_groups[dist]:
file.extend([f"{p[1]}",","])
file.pop()
file.append("};\n")
#============================================= write to file
with open(join(CWD, "expansion_groups.h"), 'w') as f:
f.write(''.join(file))
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/a_star/expansion_groups.py
|
Python
|
agpl-3.0
| 1,646
|
#include "a_star.h"
#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
namespace py = pybind11;
using namespace std;
py::array_t<float> compute( py::array_t<float> parameters ){
// ================================================= 1. Parse data
py::buffer_info parameters_buf = parameters.request();
int params_len = parameters_buf.shape[0];
// ================================================= 2. Compute path
astar( (float*)parameters_buf.ptr, params_len );
// ================================================= 3. Prepare data to return
py::array_t<float> retval = py::array_t<float>(final_path_size); //allocate
py::buffer_info buff = retval.request();
float *ptr = (float *) buff.ptr;
for(int i=0; i<final_path_size; i++){
ptr[i] = final_path[i];
}
return retval;
}
using namespace pybind11::literals; // to add informative argument names as -> "argname"_a
PYBIND11_MODULE(a_star, m) { // the python module name, m is the interface to create bindings
m.doc() = "Custom A-star implementation"; // optional module docstring
// optional arguments names
m.def("compute", &compute, "Compute the best path", "parameters"_a);
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/a_star/lib_main.cpp
|
C++
|
agpl-3.0
| 1,231
|
src = $(wildcard *.cpp)
obj = $(src:.c=.o)
CFLAGS = -O3 -shared -std=c++11 -fPIC -Wall $(PYBIND_INCLUDES)
all: $(obj)
g++ $(CFLAGS) -o ball_predictor.so $^
debug: $(filter-out lib_main.cpp,$(obj))
g++ -O0 -std=c++14 -Wall -g -o debug.bin debug_main.cc $^
.PHONY: clean
clean:
rm -f $(obj) all
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/ball_predictor/Makefile
|
Makefile
|
agpl-3.0
| 300
|
#include <cmath>
#include "ball_predictor.h"
float ball_pos_pred[600]; // ball position (x,y) prediction for 300*0.02s = 6s
float ball_vel_pred[600]; // ball velocity (x,y) prediction for 300*0.02s = 6s
float ball_spd_pred[300]; // ball linear speed (s) prediction for 300*0.02s = 6s
int pos_pred_len=0;
/**
* @brief Get intersection with moving ball (intersection point and distance)
* @param x robot position (x)
* @param y robot position (y)
* @param max_robot_sp_per_step maximum speed per step
* @param ball_pos imported ball positions (possibly modified version of ball_pos_pred)
* @param ball_pos_len length of ball_pos
* @param ret_x returned position (x) of intersection point
* @param ret_y returned position (y) of intersection point
* @param ret_d returned distance between robot and intersection point
*/
void get_intersection_with_ball(float x, float y, float max_robot_sp_per_step, float ball_pos[], float ball_pos_len,
float &ret_x, float &ret_y, float &ret_d){
float robot_max_displacement = 0.2; // robot has an immediate reach radius of 0.2m
int j=0;
while(1){
float vec_x = ball_pos[j++] - x;
float vec_y = ball_pos[j++] - y;
float b_dist_sq = vec_x*vec_x + vec_y*vec_y; // squared ball distance
// If robot has reached the ball, or the ball has stopped but the robot is still not there
if (b_dist_sq <= robot_max_displacement*robot_max_displacement or j>=ball_pos_len){
float d = sqrtf(b_dist_sq);
ret_d = d;
ret_x = ball_pos[j-2];
ret_y = ball_pos[j-1];
break;
}
robot_max_displacement += max_robot_sp_per_step;
}
}
/**
* @brief Predict ball position/velocity until the ball stops or gets out of bounds (up to 6s)
* Adequate when the ball is rolling on the ground
* @param bx ball position (x)
* @param by ball position (y)
* @param vx ball velocity (x)
* @param vy ball velocity (y)
*/
void predict_rolling_ball_pos_vel_spd(double bx, double by, double vx, double vy){
// acceleration = Rolling Drag Force * mass (constant = 0.026 kg)
// acceleration = k1 * velocity^2 + k2 * velocity
const double k1 = -0.01;
const double k2 = -1;
const double k1_x = (vx < 0) ? -k1 : k1; // invert k1 if vx is negative, because vx^2 absorbs the sign
const double k1_y = (vy < 0) ? -k1 : k1; // invert k1 if vy is negative, because vy^2 absorbs the sign
ball_pos_pred[0] = bx; // current ball position
ball_pos_pred[1] = by;
ball_vel_pred[0] = vx; // current ball velocity
ball_vel_pred[1] = vy;
ball_spd_pred[0] = sqrt(vx*vx+vy*vy);
int counter = 2;
while(counter < 600){
// acceleration
double acc_x = vx*vx*k1_x + vx*k2;
double acc_y = vy*vy*k1_y + vy*k2;
// second equation of motion: displacement = v0*t + 0.5*a*t^2
double dx = vx*0.02 + acc_x*0.0002; // 0.5*0.02^2 = 0.0002
double dy = vy*0.02 + acc_y*0.0002; // 0.5*0.02^2 = 0.0002
// position
bx += dx;
by += dy;
// abort when displacement is low or ball is out of bounds
if ((fabs(dx) < 0.0005 and fabs(dy) < 0.0005) or fabs(bx) > 15 or fabs(by) > 10){
break;
}
// velocity
vx += acc_x*0.02;
vy += acc_y*0.02;
// store as 32b
ball_spd_pred[counter/2] = sqrt(vx*vx+vy*vy);
ball_vel_pred[counter] = vx;
ball_pos_pred[counter++] = bx;
ball_vel_pred[counter] = vy;
ball_pos_pred[counter++] = by;
}
pos_pred_len = counter;
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/ball_predictor/ball_predictor.cpp
|
C++
|
agpl-3.0
| 3,659
|
#pragma once
extern float ball_pos_pred[600]; // ball position (x,y) prediction for 300*0.02s = 6s
extern float ball_vel_pred[600]; // ball velocoty (x,y) prediction for 300*0.02s = 6s
extern float ball_spd_pred[300]; // ball linear speed (s) prediction for 300*0.02s = 6s
extern int pos_pred_len;
extern void get_intersection_with_ball(float x, float y, float max_robot_sp_per_step, float ball_pos[], float ball_pos_len,
float &ret_x, float &ret_y, float &ret_d);
extern void predict_rolling_ball_pos_vel_spd(double bx, double by, double vx, double vy);
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/ball_predictor/ball_predictor.h
|
C
|
agpl-3.0
| 603
|
#include "ball_predictor.h"
#include <chrono>
#include <iostream>
#include <iomanip>
using std::cout;
using std::chrono::high_resolution_clock;
using std::chrono::duration_cast;
using std::chrono::microseconds;
std::chrono::_V2::system_clock::time_point t1,t2;
int main(){
// ================================================= 1. Generate data
float px = 3;
float py = 4;
float vx = -5;
float vy = -1;
// ================================================= 2. Compute prediction
t1 = high_resolution_clock::now();
predict_rolling_ball_pos_vel_spd(px, py, vx, vy);
t2 = high_resolution_clock::now();
cout << std::fixed << std::setprecision(8);
for(int i=0; i<pos_pred_len; i+=2){
cout << i/2 << " pos:" << ball_pos_pred[i] << "," << ball_pos_pred[i+1] <<
" vel:" << ball_vel_pred[i] << "," << ball_vel_pred[i+1] <<
" spd:" << ball_spd_pred[i/2] << "\n";
}
cout << "\n\n" << duration_cast<microseconds>(t2 - t1).count() << "us for prediction\n";
// ================================================= 3. Generate data
float robot_x = -1;
float robot_y = 1;
float max_speed_per_step = 0.7*0.02;
float ret_x, ret_y, ret_d;
// ================================================= 4. Compute intersection
t1 = high_resolution_clock::now();
get_intersection_with_ball(robot_x, robot_y, max_speed_per_step, ball_pos_pred, pos_pred_len, ret_x, ret_y, ret_d);
t2 = high_resolution_clock::now();
cout << duration_cast<microseconds>(t2 - t1).count() << "us for intersection\n\n";
cout << "Intersection: " << ret_x << "," << ret_y << " dist: " << ret_d << "\n\n";
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/ball_predictor/debug_main.cc
|
C++
|
agpl-3.0
| 1,715
|
#include "ball_predictor.h"
#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
namespace py = pybind11;
using namespace std;
/**
* @brief Predict rolling ball position, velocity, linear speed
*
* @param parameters
* ball_x, ball_y, ball_vel_x, ball_vel_y
* @return ball_pos_pred, ball_vel_pred, ball_spd_pred
*/
py::array_t<float> predict_rolling_ball( py::array_t<float> parameters ){
// ================================================= 1. Parse data
py::buffer_info parameters_buf = parameters.request();
float* parameters_ptr = (float*)parameters_buf.ptr;
float px = parameters_ptr[0];
float py = parameters_ptr[1];
float vx = parameters_ptr[2];
float vy = parameters_ptr[3];
// ================================================= 2. Compute path
predict_rolling_ball_pos_vel_spd(px, py, vx, vy);
// ================================================= 3. Prepare data to return
py::array_t<float> retval = py::array_t<float>(pos_pred_len+pos_pred_len+pos_pred_len/2); //allocate
py::buffer_info buff = retval.request();
float *ptr = (float *) buff.ptr;
for(int i=0; i<pos_pred_len; i++){
ptr[i] = ball_pos_pred[i];
}
ptr+=pos_pred_len;
for(int i=0; i<pos_pred_len; i++){
ptr[i] = ball_vel_pred[i];
}
ptr+=pos_pred_len;
for(int i=0; i<pos_pred_len/2; i++){
ptr[i] = ball_spd_pred[i];
}
return retval;
}
/**
* @brief Get point of intersection with moving ball
*
* @param parameters
* robot_x, robot_y, robot_max_speed_per_step
* @return intersection_x, intersection_y, intersection_distance
*/
py::array_t<float> get_intersection( py::array_t<float> parameters ){
// ================================================= 1. Parse data
py::buffer_info parameters_buf = parameters.request();
float* parameters_ptr = (float*)parameters_buf.ptr;
int params_len = parameters_buf.shape[0];
float x = parameters_ptr[0];
float y = parameters_ptr[1];
float max_sp = parameters_ptr[2];
float* ball_pos = parameters_ptr + 3;
float ret_x, ret_y, ret_d;
// ================================================= 2. Compute path
get_intersection_with_ball(x, y, max_sp, ball_pos, params_len-3, ret_x, ret_y, ret_d);
// ================================================= 3. Prepare data to return
py::array_t<float> retval = py::array_t<float>(3); //allocate
py::buffer_info buff = retval.request();
float *ptr = (float *) buff.ptr;
ptr[0] = ret_x;
ptr[1] = ret_y;
ptr[2] = ret_d;
return retval;
}
using namespace pybind11::literals; // to add informative argument names as -> "argname"_a
PYBIND11_MODULE(ball_predictor, m) { // the python module name, m is the interface to create bindings
m.doc() = "Ball predictor"; // optional module docstring
// optional arguments names
m.def("predict_rolling_ball", &predict_rolling_ball, "Predict rolling ball", "parameters"_a);
m.def("get_intersection", &get_intersection, "Get point of intersection with moving ball", "parameters"_a);
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/ball_predictor/lib_main.cpp
|
C++
|
agpl-3.0
| 3,148
|
#include "Field.h"
#include "RobovizLogger.h"
#include "World.h"
static World& world = SWorld::getInstance();
//=================================================================================================
//=========================================================================== constexpr definitions
//=================================================================================================
decltype(Field::cRingLineLength) constexpr Field::cRingLineLength;
decltype(Field::cPenaltyBoxDistX) constexpr Field::cPenaltyBoxDistX;
decltype(Field::cHalfPenaltyWidth) constexpr Field::cHalfPenaltyWidth;
decltype(Field::cHalfGoalWidth) constexpr Field::cHalfGoalWidth;
decltype(Field::cHalfFielfLength) constexpr Field::cHalfFielfLength;
decltype(Field::cGoalWidth) constexpr Field::cGoalWidth;
decltype(Field::cGoalDepth) constexpr Field::cGoalDepth;
decltype(Field::cGoalHeight) constexpr Field::cGoalHeight;
decltype(Field::cFieldLength) constexpr Field::cFieldLength;
decltype(Field::cFieldWidth) constexpr Field::cFieldWidth;
decltype(Field::cPenaltyLength) constexpr Field::cPenaltyLength;
decltype(Field::cPenaltyWidth) constexpr Field::cPenaltyWidth;
decltype(Field::cFieldLineSegments::list) constexpr Field::cFieldLineSegments::list;
decltype(Field::cFieldPoints::list) constexpr Field::cFieldPoints::list;
//non-constexpr definitions
decltype(Field::list_8_landmarks::list) Field::list_8_landmarks::list;
//=================================================================================================
//=============================================================================== Drawing utilities
//=================================================================================================
/**
* Draw estimates of all visible lines, markers, self position and ball
* */
void Field::draw_visible(const Matrix4D& headToFieldT, bool is_right_side) const{
if(is_right_side){
return draw_visible_switch(headToFieldT);
}
string draw_name = "localization";
RobovizLogger* roboviz = RobovizLogger::Instance();
roboviz->init(); // only initialized when draw_visible is called (only happens once)
//--------------------------------- Print all lines, whether they were identified or not
for(const Line6f& l : list_segments) {
Vector3f s = headToFieldT * l.startc;
Vector3f e = headToFieldT * l.endc;
roboviz->drawLine(s.x, s.y, s.z, e.x, e.y, e.z, 1, 0.8,0,0, &draw_name);
}
//--------------------------------- Print identified line segments, with their fixed abs coordinates
for(const auto& s : list_known_segments){
Vector3f mid = Vector3f::determineMidpoint(s.point[0].absPos.get_vector(), s.point[1].absPos.get_vector());
string line_name(s.fieldSegment->name);
roboviz->drawAnnotation(&line_name,mid.x,mid.y,mid.z, 0,1,0,&draw_name);
roboviz->drawLine(s.point[0].absPos.x, s.point[0].absPos.y, s.point[0].absPos.z,
s.point[1].absPos.x, s.point[1].absPos.y, s.point[1].absPos.z, 3, 0,0.8,0, &draw_name);
}
for(const auto& m : list_known_markers){
string line_name(m.fieldPt->name);
roboviz->drawAnnotation(&line_name,m.absPos.x, m.absPos.y, m.absPos.z+1, 1,0,0,&draw_name);
roboviz->drawLine(m.absPos.x, m.absPos.y, m.absPos.z,
m.absPos.x, m.absPos.y, m.absPos.z+0.5, 1, 0.8,0.8,0.8, &draw_name);
}
for(const auto& m : list_unknown_markers){
string line_name = "!";
roboviz->drawAnnotation(&line_name,m.absPos.x, m.absPos.y, m.absPos.z+1, 1,0,0,&draw_name);
roboviz->drawLine(m.absPos.x, m.absPos.y, m.absPos.z,
m.absPos.x, m.absPos.y, m.absPos.z+0.5, 1, 0.8,0.8,0.8, &draw_name);
}
//--------------------------------- Draw player and ball arrows
Vector3f me = headToFieldT.toVector3f();
roboviz->drawLine(me.x, me.y, me.z, me.x, me.y, me.z+0.5, 2,1,0,0,&draw_name);
roboviz->drawLine(me.x, me.y, me.z, me.x-0.2, me.y, me.z+0.2, 2,1,0,0,&draw_name);
roboviz->drawLine(me.x, me.y, me.z, me.x+0.2, me.y, me.z+0.2, 2,1,0,0,&draw_name);
//There is no need to draw the ball position here (but it works well)
/*static Vector3f last_known_ball_pos = Vector3f();
if(world.ball_seen){
last_known_ball_pos = headToFieldT * world.ball_rel_pos_cart;
}
Vector3f &b = last_known_ball_pos;
roboviz->drawLine(b.x, b.y, b.z, b.x, b.y, b.z+0.5, 2,1,1,0,&draw_name);
roboviz->drawLine(b.x, b.y, b.z, b.x-0.2, b.y, b.z+0.2, 2,1,1,0,&draw_name);
roboviz->drawLine(b.x, b.y, b.z, b.x+0.2, b.y, b.z+0.2, 2,1,1,0,&draw_name);*/
roboviz->swapBuffers(&draw_name);
}
/**
* Draw estimates of all visible lines, markers, self position and ball, but switch field sides
* */
void Field::draw_visible_switch(const Matrix4D& headToFieldT) const{
string draw_name = "localization";
RobovizLogger* roboviz = RobovizLogger::Instance();
roboviz->init(); // only initialized when draw_visible is called (only happens once)
//--------------------------------- Print all lines, whether they were identified or not
for(const Line6f& l : list_segments) {
Vector3f s = headToFieldT * l.startc;
Vector3f e = headToFieldT * l.endc;
roboviz->drawLine(-s.x, -s.y, s.z, -e.x, -e.y, e.z, 1, 0.8,0,0, &draw_name);
}
//--------------------------------- Print identified line segments, with their fixed abs coordinates
for(const auto& s : list_known_segments){
Vector3f mid = Vector3f::determineMidpoint(s.point[0].absPos.get_vector(), s.point[1].absPos.get_vector());
string line_name(s.fieldSegment->name);
roboviz->drawAnnotation(&line_name,-mid.x,-mid.y,mid.z, 0,1,0,&draw_name);
roboviz->drawLine(-s.point[0].absPos.x, -s.point[0].absPos.y, s.point[0].absPos.z,
-s.point[1].absPos.x, -s.point[1].absPos.y, s.point[1].absPos.z, 3, 0,0.8,0, &draw_name);
}
for(const auto& m : list_known_markers){
string line_name(m.fieldPt->name);
roboviz->drawAnnotation(&line_name,-m.absPos.x, -m.absPos.y, m.absPos.z+1, 1,0,0,&draw_name);
roboviz->drawLine(-m.absPos.x, -m.absPos.y, m.absPos.z,
-m.absPos.x, -m.absPos.y, m.absPos.z+0.5, 1, 0.8,0.8,0.8, &draw_name);
}
for(const auto& m : list_unknown_markers){
string line_name = "!";
roboviz->drawAnnotation(&line_name,-m.absPos.x, -m.absPos.y, m.absPos.z+1, 1,0,0,&draw_name);
roboviz->drawLine(-m.absPos.x, -m.absPos.y, m.absPos.z,
-m.absPos.x, -m.absPos.y, m.absPos.z+0.5, 1, 0.8,0.8,0.8, &draw_name);
}
//--------------------------------- Draw player and ball arrows
Vector3f me = headToFieldT.toVector3f();
roboviz->drawLine(-me.x, -me.y, me.z, -me.x, -me.y, me.z+0.5, 2,1,0,0,&draw_name);
roboviz->drawLine(-me.x, -me.y, me.z, -me.x+0.2, -me.y, me.z+0.2, 2,1,0,0,&draw_name);
roboviz->drawLine(-me.x, -me.y, me.z, -me.x-0.2, -me.y, me.z+0.2, 2,1,0,0,&draw_name);
//There is no need to draw the ball position here (but it works well)
/*static Vector3f last_known_ball_pos = Vector3f();
if(world.ball_seen){
last_known_ball_pos = headToFieldT * world.ball_rel_pos_cart;
}
Vector3f &b = last_known_ball_pos;
roboviz->drawLine(b.x, b.y, b.z, b.x, b.y, b.z+0.5, 2,1,1,0,&draw_name);
roboviz->drawLine(b.x, b.y, b.z, b.x-0.2, b.y, b.z+0.2, 2,1,1,0,&draw_name);
roboviz->drawLine(b.x, b.y, b.z, b.x+0.2, b.y, b.z+0.2, 2,1,1,0,&draw_name);*/
roboviz->swapBuffers(&draw_name);
}
//=================================================================================================
//==================================================================== Refresh / Identify / Collect
//=================================================================================================
/**
* Gather markers with known absolute z: line endpoints + foot contact points + [toe contact points]
**/
void Field::gather_ground_markers(){
/**
* Add NAO's feet ground contact points to zmarks
* Dependency: the agent's feet must be touching the ground
* Flaws:
* - if the feet are touching other players or the ball (may be solved if it is problematic)
* - robot 4 may be touching the ground with its toes and they are currently ignored
**/
for(int i=0; i<2; i++){
if( world.foot_touch[i] ){ //if this foot is touching the ground
//Vector3f contactAux = world.foot_contact_pt[i]; //contact point using strange coordinate system
//Vector3f contactpt = Vector3f(contactAux.y,-contactAux.x,contactAux.z); // fix coordinate system for both feet
Vector3f relPos = world.foot_contact_rel_pos[i];
list_feet_contact_points.emplace_back( sVector3d({0,0,0}), relPos.toPolar(), relPos);
list_ground_markers.emplace_back( sVector3d({0,0,0}), relPos.toPolar(), relPos);
}
}
//Deactivated since it did not produce better results, even when both feet are floating (it was only better when the robot fell)
/*const Types::BodyParts toePart[2] = {Types::ilLToe, Types::ilRToe};
for(int i=0; i<2; i++){
if( agent::model->getToeTouch(feetSide[i]) ){ //if this foot is touching the ground
Vector3f contactAux = agent::model->getToeContact(feetSide[i]); //contact point using strange coordinate system
Vector3f contactpt = Vector3f(contactAux.y,-contactAux.x,contactAux.z); // fix coordinate system for both feet
Vector3f relPos = agent::model->getRelPositionOfBodyPoint(agent::model->getBodyPart(toePart[i]),contactpt);
if(agent::model->getFootTouch(Types::iLeft) == false && agent::model->getFootTouch(Types::iRight) == false){
zmarks.emplace_back( 0,0,0,relPos );
}
}
}*/
//Add all line endings to ground markers
for(const Line6f& l : list_segments){
list_ground_markers.emplace_back( sVector3d({0,0,0}), l.startp, l.startc);
list_ground_markers.emplace_back( sVector3d({0,0,0}), l.endp, l.endc);
}
non_collinear_ground_markers = list_ground_markers.size(); //Excludes corner flags
//Add corner flags
for(const auto& c : list_landmarks_corners){
list_ground_markers.emplace_back( sVector3d({0,0,0}), c.relPosPolar, c.relPosCart);
}
/**
* All the polar coordinates' errors are dependent on the distance
* Var[distance error] = Var[ed*d/100] + Var[er] (ed-error distance, er-error rounding)
* Var[distance error] = (d/100)^2 * Var[ed] + Var[er]
* Their importance will be given by Inverse-variance weighting
*
* Application:
* repetition = max(int(k*(1/var)),1), where k=1/1500
* repetitions for 1 meter: 71
* repetitions for 2 meters: 55
* repetitions for >=19 meters: 1
*/
for(const auto& g : list_ground_markers){
float var = pow(g.relPosPolar.x / 100.f,2) * var_distance + var_round_hundredth;
float w = 1.f/(1500.f*var); //weight = (1/var)*k where k is a constant to transform the large weights into number of repetitions
int repetitions = max(int(w),1);
list_weighted_ground_markers.insert(list_weighted_ground_markers.end(), repetitions, g);
}
}
/**
* Update markers after visual step
* Marks attributes: abs(x,y,z), rel(r,h,v)
* Possible markers:
* - 8 landmarks
* - line endings:
* - 8 @ field corners
* - 12 @ penalty box corners
* - 20 @ center ring
* - 2 @ halfway line
* - 8 noisy estimates from corner-originated lines
* */
void Field::update(){
//no need to reserve space since these vectors will expand mostly in the first cycles
list_segments.clear();
list_landmarks.clear();
list_landmarks_corners.clear();
list_landmarks_goalposts.clear();
list_feet_contact_points.clear();
list_known_markers.clear();
list_unknown_markers.clear();
list_known_segments.clear();
list_ground_markers.clear();
list_weighted_ground_markers.clear();
//----------------------------------------- Pre-processing: prepare landmark lists
for(int i=0; i<8; i++){
sFixedMarker *l8;
const sFieldPoint *fp;
World::sLMark *l = &world.landmark[i];
if (l->pos.x == -15 && l->pos.y == -10) {l8 = &list_8_landmarks::_corner_mm; fp = &cFieldPoints::corner_mm;}
else if(l->pos.x == -15 && l->pos.y == +10) {l8 = &list_8_landmarks::_corner_mp; fp = &cFieldPoints::corner_mp;}
else if(l->pos.x == +15 && l->pos.y == -10) {l8 = &list_8_landmarks::_corner_pm; fp = &cFieldPoints::corner_pm;}
else if(l->pos.x == +15 && l->pos.y == +10) {l8 = &list_8_landmarks::_corner_pp; fp = &cFieldPoints::corner_pp;}
else if(l->pos.x == -15 && l->pos.y < 0) {l8 = &list_8_landmarks::_goal_mm; fp = &cFieldPoints::goal_mm; }
else if(l->pos.x == -15 && l->pos.y > 0) {l8 = &list_8_landmarks::_goal_mp; fp = &cFieldPoints::goal_mp; }
else if(l->pos.x == +15 && l->pos.y < 0) {l8 = &list_8_landmarks::_goal_pm; fp = &cFieldPoints::goal_pm; }
else if(l->pos.x == +15 && l->pos.y > 0) {l8 = &list_8_landmarks::_goal_pp; fp = &cFieldPoints::goal_pp; }
else{ return; }
if(l->seen){
l8->set_relPos(l->rel_pos);
l8->visible = true;
sMarker seen_mark(fp, l->rel_pos);
list_landmarks.push_back(seen_mark);
list_known_markers.push_back(seen_mark);
if (l->isCorner){ list_landmarks_corners.push_back( seen_mark); }
else { list_landmarks_goalposts.push_back(seen_mark); }
}else{
l8->visible = false;
}
}
//----------------------------------------- Pre-processing: prepare lines and landmarks' coordinates sign
for(const auto& l : world.lines_polar) {
list_segments.emplace_back(l.start, l.end);
}
//----------------------------------------- Gather markers with known absolute z: line endpoints + foot contact points
gather_ground_markers();
}
void Field::update_from_transformation(const Matrix4D& tmatrix){
/**
* Identify segments based on transformation matrix
*
* Identification starts from longest to shortest line
* The visible line segment is identified if there is only 1 close field line
* If there is more than 1 close field line and all but 1 were already taken, it is still identified
*/
//----------------------------------------------- get lines ordered from largest to shortest
vector<Line6f*> lines_descending_length;
for(auto& l : list_segments){
lines_descending_length.push_back(&l);
}
//Sort from largest to smallest radius
sort(lines_descending_length.begin(),lines_descending_length.end(),
[](const Line6f* a, const Line6f* b) { return (a->length > b->length); });
//----------------------------------------------- identify lines
for(const Line6f* l : lines_descending_length){
Vector3f l_abs[2] = {tmatrix * l->startc, tmatrix * l->endc};
float l_angle = atan2f(l_abs[1].y - l_abs[0].y, l_abs[1].x - l_abs[0].x);
const float min_err = 0.3; //maximum allowed distance (startdist + enddist < 0.3m)
const sFieldSegment* best_line = nullptr;
for(const auto& s : cFieldLineSegments::list){ //find distance to closest field line
//Skip field line if seen line is substantially larger
if( l->length > (s.length + 0.7) ){ continue; }
//Skip field line if orientation does not match
float line_angle_difference = normalize_line_angle_rad(l_angle - s.angle);
if(line_angle_difference > 0.26) continue; //tolerance 15deg
//Skip field line if it was already identified
bool already_identified = false;
for(const auto& k : list_known_segments){
if(k.fieldSegment == &s){ already_identified=true; break; }
}
if(already_identified) continue;
//Error is the sum of the distance of a single line segment to both endpoints of seen line
float err = fieldLineSegmentDistToCart2DPoint(s,l_abs[0].to2d());
if(err < min_err) err += fieldLineSegmentDistToCart2DPoint(s,l_abs[1].to2d());
if(err < min_err){
if(best_line == nullptr){ best_line = &s; } //Save the field line for now (others may emerge)
else{
best_line = nullptr; //Two close field lines, none of which was taken yet, so abort
break;
}
}
}
if(best_line != nullptr){
//-------------- Fix the seen line's start<->end order to match the corresponding field line
int l_index[2] = {0,1}; //line index of [0]start and [1]end points
if(normalize_vector_angle_rad(l_angle - best_line->angle) > 1.57079633f){ // they point in opposite directions
l_index[0] = 1;
l_index[1] = 0;
}
const Vector3f *lAbs[2] = {&l_abs[l_index[0]], &l_abs[l_index[1]]};
const Vector3f *lRelP[2] = {&l->get_polar_pt(l_index[0]), &l->get_polar_pt(l_index[1])};
const Vector3f *lRelC[2] = {&l->get_cart_pt(l_index[0]), &l->get_cart_pt(l_index[1])};
//-------------- Fix the absolute coordinates with field information
bool isInFoV[2] = {false,false};
//1st: recognize endpoints as field points (& save known markers)
/**
* //---------------------------------------------- General solution
* All points reasonably within the FoV are identified
* Noise applied horizontally sigma=0.1225, Pr[-0.5<x<0.5]=0.99996
* Noise applied vertically sigma=0.1480, Pr[-0.5<x<0.5]=0.99928
*
* Warning: the server limits the focal distance to 10cm (which is enforced in the x cartesian coordinate)
* current solution: lRelC[i].x > 0.2
*
* Warning 2: sometimes the error of phi and theta is larger than expected, producing points like
* (rel polar: 0.57,-36.36,-54.41) (rel cart: 0.267,-0.1967,-0.4635)
* which goes with the theory that the FoV is actually defined as a cone (a bit unrealistic though)
* current solution: cone_angle < hor_FoV-5
*/
for( int i=0; i<2; i++){
float cone_angle = acosf(lRelC[i]->x / lRelP[i]->x); //angle between vector and (1,0,0)
const float max_cone_angle = (cHalfHorizontalFoV-5)*M_PI/180;
if(cone_angle < max_cone_angle && lRelC[i]->x > 0.2){
list_known_markers.emplace_back(best_line->point[i], *lRelP[i], *lRelC[i]);
isInFoV[i] = true;
}
}
//2nd: use real coordinates if point was recognized, otherwise push it to a valid position (& save segment and unknown markers)
const Line6f field_line(best_line->point[0]->get_vector(), best_line->point[1]->get_vector(), best_line->length);
sVector3d l_pt_d[2]; //final line segment points (double precision floating-points)
for( int i=0; i<2; i++){
if(isInFoV[i]){
l_pt_d[i] = best_line->point[i]->pt; //set recognized point's abs coordinates
}else{
Vector3f p = field_line.segmentPointClosestToCartPoint(*lAbs[i]); //push point to closest valid position
l_pt_d[i].set(p); //set unknown point's estimated coordinates
list_unknown_markers.emplace_back(best_line, l_pt_d[i], *lRelP[i], *lRelC[i]);
}
}
//-------------- Save identified line segment
list_known_segments.emplace_back(sMarker(l_pt_d[0],*lRelP[0],*lRelC[0]),sMarker(l_pt_d[1],*lRelP[1],*lRelC[1]), l->length, best_line);
}
}
}
void Field::update_unknown_markers(const Matrix4D& tmatrix){
for(auto& u : list_unknown_markers){
//Transform marker to world frame
Vector3f raw_abs_pos = tmatrix * u.relPosCart;
//Push marker to existing field segment
const Line6f field_seg( u.fieldSeg->point[0]->get_vector(), u.fieldSeg->point[1]->get_vector(), u.fieldSeg->length);
Vector3f fixed_abs_pos = field_seg.segmentPointClosestToCartPoint(raw_abs_pos); //push point to closest valid position
u.absPos = sVector3d({fixed_abs_pos.x, fixed_abs_pos.y, fixed_abs_pos.z});
}
}
//=================================================================================================
//================================================================================== Math utilities
//=================================================================================================
/**
* Field lines are on the ground (z=0), so the method is simplified
*/
float Field::fieldLineSegmentDistToCartPoint(const sFieldSegment& fLine, const Vector3f& cp){
//Line segment vector (start -> end)
float vx = fLine.point[1]->pt.x - fLine.point[0]->pt.x;
float vy = fLine.point[1]->pt.y - fLine.point[0]->pt.y;
Vector3f w1(cp.x - fLine.point[0]->pt.x, cp.y - fLine.point[0]->pt.y, cp.z); // vector: (segment start -> point)
if (w1.x * vx + w1.y * vy <= 0)
return w1.length();// if angle between vectors is >=90deg, we return the distance to segment start
Vector3f w2(cp.x - fLine.point[1]->pt.x, cp.y - fLine.point[1]->pt.y, cp.z); // vector: (segment end -> point)
if (w2.x * vx + w2.y * vy >= 0)
return w2.length(); //if angle between vectors is <=90deg, we return the distance to segment end
Vector3f v_cross_w1(vy * w1.z, - vx * w1.z, vx * w1.y - vy * w1.x);
return v_cross_w1.length() / fLine.length; //distance line to point (area of parallelogram divided by base gives height)
}
/**
* Field lines are on the ground (z=0), so the method is simplified
*/
float Field::fieldLineSegmentDistToCart2DPoint(const sFieldSegment& fLine, const Vector& cp){
const Vector segment_start(fLine.point[0]->pt.x, fLine.point[0]->pt.y);
const Vector segment_end( fLine.point[1]->pt.x, fLine.point[1]->pt.y );
//Line segment vector (start -> end)
Vector v(segment_end-segment_start);
Vector w1(cp - segment_start);// vector: (segment start -> point)
if(w1.innerProduct(v) <= 0)
return w1.length();// if angle between vectors is >=90deg, we return the distance to segment start
Vector w2(cp - segment_end); // vector: (segment end -> point)
if(w2.innerProduct(v) >= 0)
return w2.length(); //if angle between vectors is <=90deg, we return the distance to segment end
return fabsf(v.crossProduct(w1)) / fLine.length; //distance line to point (area of parallelogram divided by base gives height)
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/Field.cpp
|
C++
|
agpl-3.0
| 22,994
|
/**
* FILENAME: Field
* DESCRIPTION: Field map
* AUTHOR: Miguel Abreu (m.abreu@fe.up.pt)
* DATE: 2021
*/
#pragma once
#include "Vector3f.h"
#include "Singleton.h"
#include "Matrix4D.h"
#include "Line6f.h"
#include <vector>
#include <array>
using namespace std;
class Field {
friend class Singleton<Field>;
private:
Field(){};
void gather_ground_markers();
public:
//=================================================================================================
//====================================================================================== Structures
//=================================================================================================
struct sVector3d {
double x,y,z;
//sVector3d(const Vector3f& v) : x(v.x), y(v.y), z(v.z) {}
Vector3f get_vector() const {
return Vector3f(x,y,z);
}
void set(const sVector3d &pt){
x=pt.x; y=pt.y; z=pt.z;
}
void set(const Vector3f &pt){
x=pt.x; y=pt.y; z=pt.z;
}
float dist(const Vector3f &other) const{
float dx = x-other.x;
float dy = y-other.y;
float dz = z-other.z;
return sqrtf(dx*dx+dy*dy+dz*dz);
}
};
struct sFieldPoint {
const sVector3d pt;
const char name[10];
Vector3f get_vector() const {
return Vector3f(pt.x,pt.y,pt.z);
}
};
struct sFieldSegment {
const sFieldPoint * const point[2];
const double length;
const double angle;
const char name[8];
};
struct sMarker {
/**
* Estimated absolute position based on the transformation matrix and field knowledge
*/
sVector3d absPos;
/**
* Pointer to corresponding field point (if reasonably inside the FoV)
* The coordinates are the same as "absPos" but it provides other features:
* - Name of the field point
* - Knowledge that this marker corresponds to a field point (nullptr otherwise)
* - The address of the fieldPt may be compared with field segment endpoints
*/
const sFieldPoint *fieldPt = nullptr;
/**
* Pointer to corresponding field segment
* This variable is currently set only for unknown markers
* (i.e. those which are known to belong to a field line, but whose field point is unknown)
*/
const sFieldSegment *fieldSeg = nullptr;
Vector3f relPosPolar;
Vector3f relPosCart;
/**
* Default constructor
*/
sMarker() : absPos({0,0,0}), relPosPolar(Vector3f()), relPosCart(Vector3f()) {};
/**
* Constructor with absolute position and relative polar coordinates (the cartesian version is computed)
*/
sMarker(const sVector3d& absPos_, const Vector3f& relPosPolar_)
: absPos(absPos_), relPosPolar(relPosPolar_), relPosCart(relPosPolar_.toCartesian()) {};
/**
* Constructor with field point and relative polar coordinates (the cartesian version is computed)
*/
sMarker(const sFieldPoint* fieldPt_, const Vector3f& relPosPolar_)
: absPos(fieldPt_->pt), fieldPt(fieldPt_), relPosPolar(relPosPolar_), relPosCart(relPosPolar_.toCartesian()) {};
/**
* Constructor with float absolute position and relative polar coordinates (the cartesian version is computed)
*/
sMarker(const Vector3f& absPos_, const Vector3f& relPosPolar_)
: absPos(sVector3d({absPos_.x,absPos_.y,absPos_.z})), relPosPolar(relPosPolar_), relPosCart(relPosPolar_.toCartesian()) {};
/**
* Constructor with absolute position, relative polar & cartesian coordinates
*/
sMarker(const sVector3d& absPos_, const Vector3f& relPosPolar_, const Vector3f& relPosCart_)
: absPos(absPos_), relPosPolar(relPosPolar_), relPosCart(relPosCart_) {};
/**
* Constructor with field segment, absolute position, relative polar & cartesian coordinates (e.g. unknown marker)
*/
sMarker(const sFieldSegment* fieldSeg_, const sVector3d& absPos_, const Vector3f& relPosPolar_, const Vector3f& relPosCart_)
: fieldSeg(fieldSeg_), absPos(absPos_), relPosPolar(relPosPolar_), relPosCart(relPosCart_) {};
/**
* Constructor with field point, relative polar & cartesian coordinates
*/
sMarker(const sFieldPoint* fieldPt_, const Vector3f& relPosPolar_, const Vector3f& relPosCart_)
: absPos(fieldPt_->pt), fieldPt(fieldPt_), relPosPolar(relPosPolar_), relPosCart(relPosCart_) {};
};
struct sSegment {
/**
* Order of start and end is the same as the corresponding fieldSegment
* [0]-start, [1]-end
*/
sMarker point[2];
float length; //visible segment length
const sFieldSegment* fieldSegment; //Corresponding field segment if we had full visibility
/**
* Constructor
*/
sSegment(const sMarker& start, const sMarker& end, float length_, const sFieldSegment* fieldSegment_)
: point{start,end}, length(length_), fieldSegment(fieldSegment_) {};
};
struct sFixedMarker {
bool visible;
Vector3f relPosPolar;
Vector3f relPosCart;
/**
* Default constructor
*/
sFixedMarker() : relPosPolar(Vector3f()), relPosCart(Vector3f()), visible(false) {};
void set_relPos(Vector3f relPosPolar_){
relPosPolar = relPosPolar_;
relPosCart = relPosPolar_.toCartesian();
}
};
//=================================================================================================
//================================================================================= Field constants
//=================================================================================================
/**
* Constant field dimensions
*/
static constexpr double cFieldLength = 30.0, cFieldWidth = 20.0, cPenaltyLength = 1.8, cPenaltyWidth = 6.0;
static constexpr double cGoalWidth = 2.1, cGoalDepth = 0.6, cGoalHeight = 0.8;
static constexpr double cHalfFielfLength = cFieldLength/2.0, cHalfFieldWidth = cFieldWidth/2.0;
static constexpr double cHalfGoalWidth = cGoalWidth/2.0, cHalfPenaltyLength = cPenaltyLength/2.0;
static constexpr double cHalfPenaltyWidth = cPenaltyWidth/2.0;
static constexpr double cPenaltyBoxDistX = cHalfFielfLength-cPenaltyLength;
static constexpr double cRingLineLength = 1.2360679774997897;
static constexpr float cHalfHorizontalFoV = 60;
static constexpr float cHalfVerticalFoV = 60;
static constexpr float stdev_distance = 0.0965; //st. deviation of error ed (distance error=d/100*ed)
static constexpr float var_distance = 0.00931225; // variance of error ed (distance error=d/100*ed)
static constexpr float var_round_hundredth = 0.01*0.01/12; //variance of uniformly distributed random variable [-0.005,0.005]
class cFieldPoints{
public:
/**
* Constant list of field points
* Notation
* "PT1-.-PT2" midpoint between PT1 and PT2 (2D/3D)
* "PT1-PT2" point between PT1 and PT2 (in 2D only)
*/
static constexpr std::array<sFieldPoint,28> list {{
{-cHalfFielfLength,-cHalfGoalWidth, cGoalHeight, "post--"}, {-cHalfFielfLength, cHalfGoalWidth, cGoalHeight, "post-+"}, //Goalposts x<0
{ cHalfFielfLength,-cHalfGoalWidth, cGoalHeight, "post+-"}, { cHalfFielfLength, cHalfGoalWidth, cGoalHeight, "post++"}, //Goalposts x>0
{-cHalfFielfLength,-cHalfFieldWidth,0, "corner--"}, {-cHalfFielfLength, cHalfFieldWidth,0, "corner-+"}, //Corners x<0
{ cHalfFielfLength,-cHalfFieldWidth,0, "corner+-"}, { cHalfFielfLength, cHalfFieldWidth,0, "corner++"}, //Corners x>0
{0,-cHalfFieldWidth, 0, "halfway-"}, // Halfway line ending y<0
{0, cHalfFieldWidth, 0, "halfway+"}, // Halfway line ending y>0
{-cHalfFielfLength, -cHalfPenaltyWidth, 0, "boxBack--"}, {-cHalfFielfLength, cHalfPenaltyWidth, 0, "boxBack-+"}, //Penalty box goal line corner x<0
{ cHalfFielfLength, -cHalfPenaltyWidth, 0, "boxBack+-"}, { cHalfFielfLength, cHalfPenaltyWidth, 0, "boxBack++"}, //Penalty box goal line corner x>0
{-cPenaltyBoxDistX, -cHalfPenaltyWidth, 0, "boxFrnt--"}, {-cPenaltyBoxDistX, cHalfPenaltyWidth, 0, "boxFrnt-+"}, //Penalty box front corner x<0
{ cPenaltyBoxDistX, -cHalfPenaltyWidth, 0, "boxFrnt+-"}, { cPenaltyBoxDistX, cHalfPenaltyWidth, 0, "boxFrnt++"}, //Penalty box front corner x>0
{2, 0, 0, "r0"}, { 1.6180339887498948, 1.1755705045849463, 0, "r36" }, //(18,19) Ring 0/36 deg
{0.61803398874989485, 1.9021130325903071, 0, "r72" }, {-0.61803398874989485, 1.9021130325903071, 0, "r108"}, //(20,21) Ring 72/108 deg
{-1.6180339887498948, 1.1755705045849463, 0, "r144"}, {-2, 0, 0, "r180"}, //(22,23) Ring 144/180 deg
{-1.6180339887498948, -1.1755705045849463, 0, "r216"}, {-0.61803398874989485, -1.9021130325903071, 0, "r252"}, //(24,25) Ring 216/252 deg
{0.61803398874989485, -1.9021130325903071, 0, "r288"}, { 1.6180339887498948, -1.1755705045849463, 0, "r324"} //(26,27) Ring 288/324 deg
}};
static constexpr const sFieldPoint &goal_mm = list[0]; //Goalpost x<0 y<0
static constexpr const sFieldPoint &goal_mp = list[1]; //Goalpost x<0 y>0
static constexpr const sFieldPoint &goal_pm = list[2]; //Goalpost x>0 y<0
static constexpr const sFieldPoint &goal_pp = list[3]; //Goalpost x>0 y>0
static constexpr const sFieldPoint &corner_mm = list[4]; //Corner x<0 y<0
static constexpr const sFieldPoint &corner_mp = list[5]; //Corner x<0 y>0
static constexpr const sFieldPoint &corner_pm = list[6]; //Corner x>0 y<0
static constexpr const sFieldPoint &corner_pp = list[7]; //Corner x>0 y>0
static constexpr const sFieldPoint &halfway_m = list[8]; //Halfway line ending y<0
static constexpr const sFieldPoint &halfway_p = list[9]; //Halfway line ending y>0
static constexpr const sFieldPoint &boxgoal_mm = list[10]; //Penalty box goal line corner x<0 y<0
static constexpr const sFieldPoint &boxgoal_mp = list[11]; //Penalty box goal line corner x<0 y>0
static constexpr const sFieldPoint &boxgoal_pm = list[12]; //Penalty box goal line corner x>0 y<0
static constexpr const sFieldPoint &boxgoal_pp = list[13]; //Penalty box goal line corner x>0 y>0
static constexpr const sFieldPoint &box_mm = list[14]; //Penalty box front corner x<0 y<0
static constexpr const sFieldPoint &box_mp = list[15]; //Penalty box front corner x<0 y>0
static constexpr const sFieldPoint &box_pm = list[16]; //Penalty box front corner x>0 y<0
static constexpr const sFieldPoint &box_pp = list[17]; //Penalty box front corner x>0 y>0
static constexpr const sFieldPoint *rings = &list[18]; //iterator for 10 ring points
};
/**
* Constant list of field line segments
* Each line segment has 3 characteristics: {startc, endc, length, angle, print name},
* The angle is always positive, in [0,180[, and corresponds to the vector defined by (end-start)
* The print name should be used for printing purposes only,
* since the line segment can be identified by its constant index or address
*/
class cFieldLineSegments{
public:
static constexpr double c0deg = 0, c36deg = 0.62831853071795865, c72deg = 1.2566370614359173;
static constexpr double c90deg = 1.5707963267948966, c108deg = 1.8849555921538759, c144deg = 2.5132741228718346;
static constexpr std::array<sFieldSegment,21> list {{
{&cFieldPoints::corner_mm, &cFieldPoints::corner_pm, cFieldLength, c0deg , "side-"}, // Sideline y<0
{&cFieldPoints::corner_mp, &cFieldPoints::corner_pp, cFieldLength, c0deg , "side+"}, // Sideline y>0
{&cFieldPoints::corner_mm, &cFieldPoints::corner_mp, cFieldWidth, c90deg , "goal-"}, // Goal line x<0
{&cFieldPoints::corner_pm, &cFieldPoints::corner_pp, cFieldWidth, c90deg , "goal+"}, // Goal line x>0
{&cFieldPoints::halfway_m, &cFieldPoints::halfway_p, cFieldWidth, c90deg , "halfway"},// Halfway line
{&cFieldPoints::boxgoal_mm, &cFieldPoints::box_mm, cPenaltyLength, c0deg , "box--"}, // Penalty box sideline x<0 y<0
{&cFieldPoints::boxgoal_mp, &cFieldPoints::box_mp, cPenaltyLength, c0deg , "box-+"}, // Penalty box sideline x<0 y>0
{&cFieldPoints::box_pm, &cFieldPoints::boxgoal_pm, cPenaltyLength, c0deg , "box+-"}, // Penalty box sideline x>0 y<0
{&cFieldPoints::box_pp, &cFieldPoints::boxgoal_pp, cPenaltyLength, c0deg , "box++"}, // Penalty box sideline x>0 y>0
{&cFieldPoints::box_mm, &cFieldPoints::box_mp, cPenaltyWidth, c90deg , "box-"}, // Penalty box front line x<0
{&cFieldPoints::box_pm, &cFieldPoints::box_pp, cPenaltyWidth, c90deg , "box+"}, // Penalty box front line x>0
{&cFieldPoints::rings[0], &cFieldPoints::rings[1], cRingLineLength, c108deg, "rL0"}, // Ring line 0 -> 36
{&cFieldPoints::rings[1], &cFieldPoints::rings[2], cRingLineLength, c144deg, "rL1"}, // Ring line 36 -> 72
{&cFieldPoints::rings[3], &cFieldPoints::rings[2], cRingLineLength, c0deg , "rL2"}, // Ring line 72 <- 108
{&cFieldPoints::rings[4], &cFieldPoints::rings[3], cRingLineLength, c36deg , "rL3"}, // Ring line 108 <- 144
{&cFieldPoints::rings[5], &cFieldPoints::rings[4], cRingLineLength, c72deg , "rL4"}, // Ring line 144 <- 180
{&cFieldPoints::rings[6], &cFieldPoints::rings[5], cRingLineLength, c108deg, "rL5"}, // Ring line 180 <- 216
{&cFieldPoints::rings[7], &cFieldPoints::rings[6], cRingLineLength, c144deg, "rL6"}, // Ring line 216 <- 252
{&cFieldPoints::rings[7], &cFieldPoints::rings[8], cRingLineLength, c0deg , "rL7"}, // Ring line 252 -> 288
{&cFieldPoints::rings[8], &cFieldPoints::rings[9], cRingLineLength, c36deg , "rL8"}, // Ring line 288 -> 324
{&cFieldPoints::rings[9], &cFieldPoints::rings[0], cRingLineLength, c72deg , "rL9"} // Ring line 324 -> 0
}};
static constexpr const sFieldSegment &side_m = list[0]; // Sideline y<0
static constexpr const sFieldSegment &side_p = list[1]; // Sideline y>0
static constexpr const sFieldSegment &goal_m = list[2]; // Goal line x<0
static constexpr const sFieldSegment &goal_p = list[3]; // Goal line x>0
static constexpr const sFieldSegment &halfway = list[4]; //Halfway line
static constexpr const sFieldSegment &box_mm = list[5]; // Penalty box sideline x<0 y<0
static constexpr const sFieldSegment &box_mp = list[6]; // Penalty box sideline x<0 y>0
static constexpr const sFieldSegment &box_pm = list[7]; // Penalty box sideline x>0 y<0
static constexpr const sFieldSegment &box_pp = list[8]; // Penalty box sideline x>0 y>0
static constexpr const sFieldSegment &box_m = list[9]; // Penalty box front line x<0
static constexpr const sFieldSegment &box_p = list[10]; // Penalty box front line x>0
static constexpr const sFieldSegment *rings = &list[11]; //iterator for 10 ring lines
};
sSegment* get_known_segment(const sFieldSegment &id){
for( auto& s : list_known_segments){
if(s.fieldSegment == &id) return &s;
}
return nullptr;
}
//=================================================================================================
//================================================================================= Control methods
//=================================================================================================
/**
* Update markers, based on existing landmarks and lines
*/
void update();
/**
* Update markers, based on transformation matrix and existing lines
*/
void update_from_transformation(const Matrix4D& tmatrix);
/**
* Update the absolute position of unknown markers, based on transformation matrix and existing lines
*/
void update_unknown_markers(const Matrix4D& tmatrix);
/**
* Draw estimates of all visible lines, markers, self position and ball
*/
void draw_visible(const Matrix4D& headToFieldT, bool is_right_side) const;
/**
* Draw estimates of all visible lines, markers, self position and ball, but switch field sides
*/
void draw_visible_switch(const Matrix4D& headToFieldT) const;
//=================================================================================================
//============================================================================= Visible collections
//=================================================================================================
/**
* Visible landmarks: corners + goalposts
*/
vector<sMarker> list_landmarks;
/**
* Visible corners
*/
vector<sMarker> list_landmarks_corners;
/**
* Visible goalposts
*/
vector<sMarker> list_landmarks_goalposts;
/**
* Identified visible line segments
* Their start and endpoints' order is the same as the corresponding field segment to which they point
*/
vector<sSegment> list_known_segments;
/**
* Identified visible line segment endpoints + landmarks
* Each marker has a reference to the corresponding field point
*/
vector<sMarker> list_known_markers;
/**
* Endpoints (of identified visible line segments) whose corresponding field point is unknown
* Each marker has a reference to the corresponding field segment
* Note: list_known_markers + list_unknown_markers excludes points from unknown line segments
*/
vector<sMarker> list_unknown_markers;
/**
* Visible line endpoints + foot contact points + corner flags (the absolute position is always (0,0,0))
*/
vector<sMarker> list_ground_markers;
/**
* Number of visible non-collinear (line endpoints + foot contact points)
* Note: collinearity between lines is impossible; between feet<->lines it is possible but unlikely
*/
int non_collinear_ground_markers;
/**
* Same as list_ground_markers but closer points are repeated more often (proportional to distance)
*/
vector<sMarker> list_weighted_ground_markers;
/**
* Feet contact points
*/
vector<sMarker> list_feet_contact_points;
/**
* Visible line segments
*/
vector<Line6f> list_segments;
/**
* Redundant list of all 8 landmarks' relative cartesian coordinates (to speed up lookups)
* It's different from world.landmarks since it is ordered by position, not by name, and it holds the cartesian relPos
* (this ordering difference is important when the teams switch sides)
*/
class list_8_landmarks{
friend class Field;
private:
static sFixedMarker list[8];
//static std::array<sFixedMarker,8> list;
static constexpr sFixedMarker &_corner_mm = list[0];
static constexpr sFixedMarker &_corner_mp = list[1];
static constexpr sFixedMarker &_corner_pm = list[2];
static constexpr sFixedMarker &_corner_pp = list[3];
static constexpr sFixedMarker &_goal_mm = list[4];
static constexpr sFixedMarker &_goal_mp = list[5];
static constexpr sFixedMarker &_goal_pm = list[6];
static constexpr sFixedMarker &_goal_pp = list[7];
public:
static constexpr const sFixedMarker &corner_mm = list[0];
static constexpr const sFixedMarker &corner_mp = list[1];
static constexpr const sFixedMarker &corner_pm = list[2];
static constexpr const sFixedMarker &corner_pp = list[3];
static constexpr const sFixedMarker &goal_mm = list[4];
static constexpr const sFixedMarker &goal_mp = list[5];
static constexpr const sFixedMarker &goal_pm = list[6];
static constexpr const sFixedMarker &goal_pp = list[7];
};
//=================================================================================================
//================================================================================== Math Utilities
//=================================================================================================
/**
* Compute 3D distance between field line segment and cartesian point
* Field lines are on the ground (z=0), so the method is simplified
*/
static float fieldLineSegmentDistToCartPoint(const sFieldSegment& fLine, const Vector3f& cp);
/**
* Compute 2D distance between field line segment and cartesian point
* Field lines are on the ground (z=0), so the method is simplified
*/
static float fieldLineSegmentDistToCart2DPoint(const sFieldSegment& fLine, const Vector& cp);
/**
* Normalize angle between 2 lines
* @return angle between 0 and 90 deg
*/
static inline float normalize_line_angle_deg(float deg){
return 90.f-fabsf(fmodf(fabsf(deg), 180.f) - 90.f);
}
/**
* Normalize angle between 2 lines
* @return angle between 0 and pi/2 rad
*/
static inline float normalize_line_angle_rad(float rad){
return 1.57079633f-fabsf(fmod(fabsf(rad), 3.14159265f) - 1.57079633f);
}
/**
* Normalize angle between 2 vectors
* @return angle between 0 and 180 deg
*/
static inline float normalize_vector_angle_deg(float deg){
return 180.f-fabsf(fmodf(fabsf(deg), 360.f) - 180.f);
}
/**
* Normalize angle between 2 vectors
* @return angle between 0 and pi rad
*/
static inline float normalize_vector_angle_rad(float rad){
return 3.14159265f-fabsf(fmod(fabsf(rad), 6.28318531f) - 3.14159265f);
}
};
typedef Singleton<Field> SField;
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/Field.h
|
C++
|
agpl-3.0
| 22,552
|
#include "FieldNoise.h"
double FieldNoise::log_prob_r(double d, double r){
double c1 = 100.0 * ((r-0.005)/d - 1);
double c2 = 100.0 * ((r+0.005)/d - 1);
return log_prob_normal_distribution(0, 0.0965, c1, c2);
}
double FieldNoise::log_prob_h(double h, double phi){
double c1 = phi - 0.005 - h;
double c2 = phi + 0.005 - h;
return log_prob_normal_distribution(0, 0.1225, c1, c2);
}
double FieldNoise::log_prob_v(double v, double theta){
double c1 = theta - 0.005 - v;
double c2 = theta + 0.005 - v;
return log_prob_normal_distribution(0, 0.1480, c1, c2);
}
double FieldNoise::log_prob_normal_distribution(double mean, double std, double interval1, double interval2){
const double std2 = std * sqrt(2);
double erf1_x = (mean - interval1)/std2; //lowest interval, highest expression
double erf2_x = (mean - interval2)/std2; //highest interval, lowest expression
/**
* Computing erf(erf_x1) - erf(erf_x2) is the same as erf(erf_x1) + erf(-erf_x2).
* Intuitively, the former seems more natural.
* So, computationally, the former expression is accurate in the following situations:
* - erf_x1 * erf_x2 <= 0
* - |erf_x1| < 3 _or_ |erf_x2| < 3 ('3' is just a ballpark figure, not really relevant)
*
* Known issues: erf(6.5)-erf(6.0) = 1-1 = 0 (actual result: 2.148e-17)
* Using 128b functions only mitigates the issue, which is quite common actually.
*
* For these cases, erf_aux(x) is used, although it is not precise for |x|<1.
* - erf_aux(x) allows the computation of erf(6.5)-erf(6.0) with 7 digits of precision
* - erf_aux(x) allows the computation of erf(8.5)-erf(8.0) with 3 digits of precision
* - erf(12.5)-erf(12) = 0 (which is not good for probability comparison)
* - erf_aux(x) allows the computation of log(erf(6.5)-erf(6.0)) with 8 digits of precision
* - erf_aux(x) allows the computation of log(erf(8.5)-erf(8.0)) with 5 digits of precision
* - log(erf(12.5)-erf(12)) = -4647 (real: -147) (not accurate but good for comparisons)
*
* The complete algorithm below that uses erf_aux(x) is almost as fast as the one which uses erf() from math.h (+30% runtime)
*/
const double log05 = log(0.5);
//If they have different sign or |erf1_x|<1 || |erf2_x|<1
if( fabs(erf1_x) < 2 || fabs(erf2_x) < 2 || ((erf1_x > 0) ^ (erf2_x > 0))){
return log( erf(erf1_x) - erf(erf2_x) ) + log05; // same but faster than log( 0.5 * (erf(erf1_x) - erf(erf2_x)) )
}
//Otherwise use erf_aux(x)
//At this point, erf1_x and erf2_x have the same sign and are both distant from 0
double erf1 = erf_aux(erf1_x);
double erf2 = erf_aux(erf2_x);
//These operations are described in the documentation of erf_aux()
if(erf1_x > 0){ //both are positive
return log( 1.0 - exp(erf1-erf2) ) + erf2 + log05;
}else{ //both are negative
return log( 1.0 - exp(erf2-erf1) ) + erf1 + log05;
}
}
double FieldNoise::erf_aux(double a){
double r, s, t, u;
t = fabs (a);
s = a * a;
r = fma (-5.6271698458222802e-018, t, 4.8565951833159269e-016);
u = fma (-1.9912968279795284e-014, t, 5.1614612430130285e-013);
r = fma (r, s, u);
r = fma (r, t, -9.4934693735334407e-012);
r = fma (r, t, 1.3183034417266867e-010);
r = fma (r, t, -1.4354030030124722e-009);
r = fma (r, t, 1.2558925114367386e-008);
r = fma (r, t, -8.9719702096026844e-008);
r = fma (r, t, 5.2832013824236141e-007);
r = fma (r, t, -2.5730580226095829e-006);
r = fma (r, t, 1.0322052949682532e-005);
r = fma (r, t, -3.3555264836704290e-005);
r = fma (r, t, 8.4667486930270974e-005);
r = fma (r, t, -1.4570926486272249e-004);
r = fma (r, t, 7.1877160107951816e-005);
r = fma (r, t, 4.9486959714660115e-004);
r = fma (r, t, -1.6221099717135142e-003);
r = fma (r, t, 1.6425707149019371e-004);
r = fma (r, t, 1.9148914196620626e-002);
r = fma (r, t, -1.0277918343487556e-001);
r = fma (r, t, -6.3661844223699315e-001);
r = fma (r, t, -1.2837929411398119e-001);
r = fma (r, t, -t);
return r;
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/FieldNoise.cpp
|
C++
|
agpl-3.0
| 4,219
|
/**
* FILENAME: FieldNoise
* DESCRIPTION: efficient computation of relative probabilities (for the noise model of the RoboCup 3DSSL)
* AUTHOR: Miguel Abreu (m.abreu@fe.up.pt)
* DATE: 2021
*/
#pragma once
#include "math.h"
class FieldNoise{
public:
/**
* Log probability of real distance d, given noisy radius r
*/
static double log_prob_r(double d, double r);
/**
* Log probability of real horizontal angle h, given noisy angle phi
*/
static double log_prob_h(double h, double phi);
/**
* Log probability of real vertical angle v, given noisy angle theta
*/
static double log_prob_v(double v, double theta);
private:
FieldNoise(){}; //Disable construction
/**
* Log probability of normally distributed random variable X from interval1 to interval2:
* Log Pr[interval1 < X < interval2]
* @param mean mean of random variable
* @param std standard deviation of random variable
* @param interval1 minimum value of random variable
* @param interval2 maximum value of random variable
*/
static double log_prob_normal_distribution(double mean, double std, double interval1, double interval2);
/**
* This function returns ln(1-sgn(a)*erf(a)), but sgn(a)*erf(a) = |erf(a)|, because sgn(a) == sgn(erf(a))
* So, it returns: ln(1-|erf(a)|), which is <=0
*
* NOTE: condition to guarantee high precision: |a|>= 1
*
* how to compute erf(a) ?
* erf(a) = sgn(a)(1-e^erf_aux(a))
*
* how to compute erf(a)+erf(b) ?
* erf(a)+erf(b) = sgn(a)(1-e^erf_aux(a)) + sgn(b)(1-e^erf_aux(b))
* assuming a<0 and b>0:
* = e^erf_aux(a) -1 + 1 - e^erf_aux(b)
* = e^erf_aux(a) - e^erf_aux(b)
*
* example: erf(-7)+erf(7.1)
* if we computed it directly:
* erf(-7)+erf(7.1) = -0.9999999(...) + 0.9999999(...) = -1+1 = 0 (due to lack of precision, even if using double)
* if we use the proposed method:
* e^erf_aux(-7) - e^erf_aux(7.1) = -1.007340e-23 - -4.183826e-23 = 3.176486E-23
*
* how to compute ln(erf(a)+erf(b)) ?
* assuming a<0 and b>0:
* ln(erf(a)+erf(b)) = ln( exp(erf_aux(a)) - exp(erf_aux(b)) )
* = ln( exp(erf_aux(a)-k) - exp(erf_aux(b)-k) ) + k
* where k = min(erf_aux(a), erf_aux(b))
*
* how to compute ln(erf(a)-erf(b)) ? (the difference is just the assumption)
* assuming a*b >= 0
*
* ln(erf(a)-erf(b)) = ln( sgn(a)(1-e^erf_aux(a)) - sgn(a)(1-e^erf_aux(b)) ), note that sgn(a)=sgn(b)
*
* rule: log( exp(a) - exp(b) ) = log( exp(a-k) - exp(b-k) ) + k
*
* if(a>0)
* ln(erf(a)-erf(b)) = ln( 1 - e^erf_aux(a) - 1 + e^erf_aux(b))
* = ln( exp(erf_aux(b)) - exp(erf_aux(a)) )
* = ln( exp(erf_aux(b)-erf_aux(b)) - exp(erf_aux(a)-erf_aux(b)) ) + erf_aux(b)
* = ln( 1 - exp(erf_aux(a)-erf_aux(b)) ) + erf_aux(b)
* if(a<0)
* ln(erf(a)-erf(b)) = ln( -1 + e^erf_aux(a) + 1 - e^erf_aux(b))
* = ln( exp(erf_aux(a)) - exp(erf_aux(b)) )
* = ln( exp(erf_aux(a)-erf_aux(a)) - exp(erf_aux(b)-erf_aux(a)) ) + erf_aux(a)
* = ln( 1 - exp(erf_aux(b)-erf_aux(a)) ) + erf_aux(a)
*
*/
static double erf_aux(double a);
};
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/FieldNoise.h
|
C++
|
agpl-3.0
| 3,837
|
#include "Geometry.h"
/**
* This function returns the cosine of a given angle in degrees using the
* built-in cosine function that works with angles in radians.
*
* @param x an angle in degrees
* @return the cosine of the given angle
*/
float Cos(float x) {
return ( cos(x * M_PI / 180));
}
/**
* This function returns the sine of a given angle in degrees using the
* built-in sine function that works with angles in radians.
*
* @param x an angle in degrees
* @return the sine of the given angle
*/
float Sin(float x) {
return ( sin(x * M_PI / 180));
}
/**
* This function returns the principal value of the arc tangent of y/x in
* degrees using the signs of both arguments to determine the quadrant of the
* return value. For this the built-in 'atan2' function is used which returns
* this value in radians.
*
* @param x a float value
* @param y a float value
* @return the arc tangent of y/x in degrees taking the signs of x and y into
* account
*/
float ATan2(float x, float y) {
if (fabs(x) < EPSILON && fabs(y) < EPSILON)
return ( 0.0);
return ( atan2(x, y) * 180 / M_PI );
}
/************************************************************************/
/******************* CLASS VECTOR ***********************************/
/************************************************************************/
/*! Constructor for the Vector class. Arguments x and y
denote the x- and y-coordinates of the new position.
\param x the x-coordinate of the new position
\param y the y-coordinate of the new position
\return the Vector corresponding to the given arguments */
Vector::Vector(float vx, float vy) : x(vx), y(vy) {}
/*! Overloaded version of unary minus operator for Vectors. It returns the
negative Vector, i.e. both the x- and y-coordinates are multiplied by
-1. The current Vector itself is left unchanged.
\return a negated version of the current Vector */
Vector Vector::operator-() const{
return ( Vector(-x, -y));
}
/*! Overloaded version of the binary plus operator for adding a given float
value to a Vector. The float value is added to both the x- and
y-coordinates of the current Vector. The current Vector itself is
left unchanged.
\param d a float value which has to be added to both the x- and
y-coordinates of the current Vector
\return the result of adding the given float value to the current
Vector */
Vector Vector::operator+(const float &d) const{
return ( Vector(x + d, y + d));
}
/*! Overloaded version of the binary plus operator for Vectors. It returns
the sum of the current Vector and the given Vector by adding their
x- and y-coordinates. The Vectors themselves are left unchanged.
\param p a Vector
\return the sum of the current Vector and the given Vector */
Vector Vector::operator+(const Vector &p) const{
return ( Vector(x + p.x, y + p.y));
}
/*! Overloaded version of the binary minus operator for subtracting a
given float value from a Vector. The float value is
subtracted from both the x- and y-coordinates of the current
Vector. The current Vector itself is left unchanged.
\param d a float value which has to be subtracted from both the x- and
y-coordinates of the current Vector
\return the result of subtracting the given float value from the current
Vector */
Vector Vector::operator-(const float &d) const{
return ( Vector(x - d, y - d));
}
/*! Overloaded version of the binary minus operator for
Vectors. It returns the difference between the current
Vector and the given Vector by subtracting their x- and
y-coordinates. The Vectors themselves are left unchanged.
\param p a Vector
\return the difference between the current Vector and the given
Vector */
Vector Vector::operator-(const Vector &p) const {
return ( Vector(x - p.x, y - p.y));
}
/*! Overloaded version of the multiplication operator for multiplying a
Vector by a given float value. Both the x- and y-coordinates of the
current Vector are multiplied by this value. The current Vector
itself is left unchanged.
\param d the multiplication factor
\return the result of multiplying the current Vector by the given
float value */
Vector Vector::operator*(const float &d) const{
return ( Vector(x * d, y * d));
}
/*! Overloaded version of the multiplication operator for
Vectors. It returns the product of the current Vector
and the given Vector by multiplying their x- and
y-coordinates. The Vectors themselves are left unchanged.
\param p a Vector
\return the product of the current Vector and the given Vector */
Vector Vector::operator*(const Vector &p) const{
return ( Vector(x * p.x, y * p.y));
}
/*! Overloaded version of the division operator for dividing a
Vector by a given float value. Both the x- and y-coordinates
of the current Vector are divided by this value. The current
Vector itself is left unchanged.
\param d the division factor
\return the result of dividing the current Vector by the given float
value */
Vector Vector::operator/(const float &d) const{
return ( Vector(x / d, y / d));
}
/*! Overloaded version of the division operator for Vectors. It
returns the quotient of the current Vector and the given
Vector by dividing their x- and y-coordinates. The
Vectors themselves are left unchanged.
\param p a Vector
\return the quotient of the current Vector and the given one */
Vector Vector::operator/(const Vector &p) const{
return ( Vector(x / p.x, y / p.y));
}
/*! Overloaded version of the assignment operator for assigning a given float
value to both the x- and y-coordinates of the current Vector. This
changes the current Vector itself.
\param d a float value which has to be assigned to both the x- and
y-coordinates of the current Vector */
void Vector::operator=(const float &d) {
x = d;
y = d;
}
/*! Overloaded version of the sum-assignment operator for Vectors. It
returns the sum of the current Vector and the given Vector by
adding their x- and y-coordinates. This changes the current Vector
itself.
\param p a Vector which has to be added to the current Vector */
void Vector::operator+=(const Vector &p) {
x += p.x;
y += p.y;
}
/*! Overloaded version of the sum-assignment operator for adding a given float
value to a Vector. The float value is added to both the x- and
y-coordinates of the current Vector. This changes the current
Vector itself.
\param d a float value which has to be added to both the x- and
y-coordinates of the current Vector */
void Vector::operator+=(const float &d) {
x += d;
y += d;
}
/*! Overloaded version of the difference-assignment operator for
Vectors. It returns the difference between the current
Vector and the given Vector by subtracting their x- and
y-coordinates. This changes the current Vector itself.
\param p a Vector which has to be subtracted from the current
Vector */
void Vector::operator-=(const Vector &p) {
x -= p.x;
y -= p.y;
}
/*! Overloaded version of the difference-assignment operator for
subtracting a given float value from a Vector. The float
value is subtracted from both the x- and y-coordinates of the
current Vector. This changes the current Vector itself.
\param d a float value which has to be subtracted from both the x- and
y-coordinates of the current Vector */
void Vector::operator-=(const float &d) {
x -= d;
y -= d;
}
/*! Overloaded version of the multiplication-assignment operator for
Vectors. It returns the product of the current Vector
and the given Vector by multiplying their x- and
y-coordinates. This changes the current Vector itself.
\param p a Vector by which the current Vector has to be
multiplied */
void Vector::operator*=(const Vector &p) {
x *= p.x;
y *= p.y;
}
/*! Overloaded version of the multiplication-assignment operator for
multiplying a Vector by a given float value. Both the x- and
y-coordinates of the current Vector are multiplied by this
value. This changes the current Vector itself.
\param d a float value by which both the x- and y-coordinates of the
current Vector have to be multiplied */
void Vector::operator*=(const float &d) {
x *= d;
y *= d;
}
/*! Overloaded version of the division-assignment operator for
Vectors. It returns the quotient of the current Vector
and the given Vector by dividing their x- and
y-coordinates. This changes the current Vector itself.
\param p a Vector by which the current Vector is divided */
void Vector::operator/=(const Vector &p) {
x /= p.x;
y /= p.y;
}
/*! Overloaded version of the division-assignment operator for
dividing a Vector by a given float value. Both the x- and
y-coordinates of the current Vector are divided by this
value. This changes the current Vector itself.
\param d a float value by which both the x- and y-coordinates of the
current Vector have to be divided */
void Vector::operator/=(const float &d) {
x /= d;
y /= d;
}
/*! Overloaded version of the inequality operator for Vectors. It
determines whether the current Vector is unequal to the given
Vector by comparing their x- and y-coordinates.
\param p a Vector
\return true when either the x- or y-coordinates of the given Vector
and the current Vector are different; false otherwise */
bool Vector::operator!=(const Vector &p) {
return ( (x != p.x) || (y != p.y));
}
/*! Overloaded version of the inequality operator for comparing a
Vector to a float value. It determines whether either the x-
or y-coordinate of the current Vector is unequal to the given
float value.
\param d a float value with which both the x- and y-coordinates of the
current Vector have to be compared.
\return true when either the x- or y-coordinate of the current Vector
is unequal to the given float value; false otherwise */
bool Vector::operator!=(const float &d) {
return ( (x != d) || (y != d));
}
/*! Overloaded version of the equality operator for Vectors. It
determines whether the current Vector is equal to the given
Vector by comparing their x- and y-coordinates.
\param p a Vector
\return true when both the x- and y-coordinates of the given
Vector and the current Vector are equal; false
otherwise */
bool Vector::operator==(const Vector &p) {
return ( (x == p.x) && (y == p.y));
}
/*! Overloaded version of the equality operator for comparing a
Vector to a float value. It determines whether both the x-
and y-coordinates of the current Vector are equal to the
given float value.
\param d a float value with which both the x- and y-coordinates of the
current Vector have to be compared.
\return true when both the x- and y-coordinates of the current Vector
are equal to the given float value; false otherwise */
bool Vector::operator==(const float &d) {
return ( (x == d) && (y == d));
}
/*! This method determines the distance between the current
Vector and a given Vector. This is equal to the
magnitude (length) of the vector connecting the two positions
which is the difference vector between them.
\param p a Vecposition
\return the distance between the current Vector and the given
Vector */
float Vector::getDistanceTo(const Vector p) {
return ( (*this -p).length());
}
/*! This method determines the magnitude (length) of the vector
corresponding with the current Vector using the formula of
Pythagoras.
\return the length of the vector corresponding with the current
Vector */
float Vector::length() const {
return ( sqrt(x * x + y * y));
}
float Vector::crossProduct(const Vector p) {
return this->x*p.y - this->y*p.x;
}
/**
* This methods returns the inner product of this vector with another
*
* @param other the other vector
* @return inner product
*/
float Vector::innerProduct(const Vector& other) const {
return x * other.x + y * other.y;
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/Geometry.cpp
|
C++
|
agpl-3.0
| 12,187
|
#ifndef GEOMETRY_H
#define GEOMETRY_H
#include <cmath>
#include <algorithm>
using namespace std;
#define EPSILON 1e-10
/**
* Useful functions to operate with angles in degrees
*/
float Cos(float x);
float Sin(float x);
float ATan2(float x, float y);
/**
* @class Vector
*
* @brief This class represents a position in the 2d space
*
* A position is represented by a x-axis coordinate and a
* y-axis coordinate or in polar coordinates (r, phi)
*
* @author Hugo Picado (hugopicado@ua.pt)
* @author Nuno Almeida (nuno.alm@ua.pt)
* Adapted - Miguel Abreu
*/
class Vector {
public:
Vector(float vx = 0, float vy = 0);
// overloaded arithmetic operators
Vector operator-() const;
Vector operator+(const float &d) const;
Vector operator+(const Vector &p) const;
Vector operator-(const float &d) const;
Vector operator-(const Vector &p) const;
Vector operator*(const float &d) const;
Vector operator*(const Vector &p) const;
Vector operator/(const float &d) const;
Vector operator/(const Vector &p) const;
void operator=(const float &d);
void operator+=(const Vector &p);
void operator+=(const float &d);
void operator-=(const Vector &p);
void operator-=(const float &d);
void operator*=(const Vector &p);
void operator*=(const float &d);
void operator/=(const Vector &p);
void operator/=(const float &d);
bool operator!=(const Vector &p);
bool operator!=(const float &d);
bool operator==(const Vector &p);
bool operator==(const float &d);
float getDistanceTo(const Vector p);
float crossProduct(const Vector p);
float length() const;
float innerProduct(const Vector &p) const;
public:
float x;
float y;
};
#endif // GEOMETRY_H
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/Geometry.h
|
C++
|
agpl-3.0
| 1,768
|
#include "Line6f.h"
Line6f::Line6f(const Vector3f &polar_s, const Vector3f &polar_e) :
startp(polar_s), endp(polar_e), startc(polar_s.toCartesian()), endc(polar_e.toCartesian()), length(startc.dist(endc)) {};
Line6f::Line6f(const Vector3f &cart_s, const Vector3f &cart_e, float length) :
startp(cart_s.toPolar()), endp(cart_e.toPolar()), startc(cart_s), endc(cart_e), length(length) {};
Line6f::Line6f(const Line6f &obj) :
startp(obj.startp), endp(obj.endp), startc(obj.startc), endc(obj.endc), length(obj.length) {};
Vector3f Line6f::linePointClosestToCartPoint(const Vector3f &cp) const{
/**
* Equation of this line: (we want to find t, such that (cp-p) is perpendicular to this line)
* p = startc + t*(endc - startc)
*
* Let vecp=(cp-start) and vecline=(end-start)
* Scalar projection of vecp in the direction of vecline:
* sp = vecp.vecline/|vecline|
* Find the ratio t by dividing the scalar projection by the length of vecline:
* t = sp / |vecline|
* So the final expression becomes:
* t = vecp.vecline/|vecline|^2
*/
Vector3f lvec(endc-startc); //this line's vector
float t = (cp-startc).innerProduct(lvec) / (length*length);
return startc + lvec*t;
}
Vector3f Line6f::linePointClosestToPolarPoint(const Vector3f &pp) const{
return linePointClosestToCartPoint(pp.toCartesian());
}
float Line6f::lineDistToCartPoint(const Vector3f &cp) const{
return ((cp-startc).crossProduct(cp-endc)).length() / (endc-startc).length();
}
float Line6f::lineDistToPolarPoint(const Vector3f &pp) const{
return lineDistToCartPoint(pp.toCartesian());
}
//source: http://geomalgorithms.com/a07-_distance.html#dist3D_Line_to_Line()
float Line6f::lineDistToLine(const Line6f &l) const{
Vector3f u = endc - startc;
Vector3f v = l.endc - l.startc;
Vector3f w = startc - l.startc;
float a = u.innerProduct(u); // always >= 0
float b = u.innerProduct(v);
float c = v.innerProduct(v); // always >= 0
float d = u.innerProduct(w);
float e = v.innerProduct(w);
float D = a*c - b*b; // always >= 0
float sc, tc;
// compute the line parameters of the two closest points
if (D < 1e-8f) { // the lines are almost parallel
sc = 0.0;
tc = (b>c ? d/b : e/c); // use the largest denominator
}
else {
sc = (b*e - c*d) / D;
tc = (a*e - b*d) / D;
}
// get the difference of the two closest points
Vector3f dP = w + (u * sc) - (v * tc); // = L1(sc) - L2(tc)
return dP.length(); // return the closest distance
}
Vector3f Line6f::segmentPointClosestToCartPoint(const Vector3f &cp) const{
/**
* Equation of this line: (we want to find t, such that (cp-p) is perpendicular to this line)
* p = startc + t*(endc - startc)
*
* Let vecp=(cp-start) and vecline=(end-start)
* Scalar projection of vecp in the direction of vecline:
* sp = vecp.vecline/|vecline|
* Find the ratio t by dividing the scalar projection by the length of vecline:
* t = sp / |vecline|
* So the final expression becomes:
* t = vecp.vecline/|vecline|^2
* Since this version requires that p belongs to the line segment, there is an additional restriction:
* 0 < t < 1
*/
Vector3f lvec(endc-startc); //this line's vector
float t = (cp-startc).innerProduct(lvec) / (length*length);
if(t<0) t=0;
else if(t>1) t=1;
return startc + lvec*t;
}
Vector3f Line6f::segmentPointClosestToPolarPoint(const Vector3f &pp) const{
return segmentPointClosestToCartPoint(pp.toCartesian());
}
float Line6f::segmentDistToCartPoint(const Vector3f &cp) const{
Vector3f v = endc - startc; //line segment vector
Vector3f w1 = cp - startc;
if ( w1.innerProduct(v) <= 0) return w1.length();
Vector3f w2 = cp - endc;
if ( w2.innerProduct(v) >= 0) return w2.length();
return v.crossProduct(w1).length() / this->length;
}
float Line6f::segmentDistToPolarPoint(const Vector3f &pp) const{
return segmentDistToCartPoint(pp.toCartesian());
}
//source: http://geomalgorithms.com/a07-_distance.html#dist3D_Segment_to_Segment()
float Line6f::segmentDistToSegment(const Line6f &other) const{
Vector3f u = endc - startc;
Vector3f v = other.endc - other.startc;
Vector3f w = startc - other.startc;
float a = u.innerProduct(u); // always >= 0
float b = u.innerProduct(v);
float c = v.innerProduct(v); // always >= 0
float d = u.innerProduct(w);
float e = v.innerProduct(w);
float D = a*c - b*b; // always >= 0
float sc, sN, sD = D; // sc = sN / sD, default sD = D >= 0
float tc, tN, tD = D; // tc = tN / tD, default tD = D >= 0
// compute the line parameters of the two closest points
if (D < 1e-8f) { // the lines are almost parallel
sN = 0.0; // force using point start on segment S1
sD = 1.0; // to prevent possible division by 0.0 later
tN = e;
tD = c;
}
else { // get the closest points on the infinite lines
sN = (b*e - c*d);
tN = (a*e - b*d);
if (sN < 0.0) { // sc < 0 => the s=0 edge is visible
sN = 0.0;
tN = e;
tD = c;
}
else if (sN > sD) { // sc > 1 => the s=1 edge is visible
sN = sD;
tN = e + b;
tD = c;
}
}
if (tN < 0.0) { // tc < 0 => the t=0 edge is visible
tN = 0.0;
// recompute sc for this edge
if (-d < 0.0)
sN = 0.0;
else if (-d > a)
sN = sD;
else {
sN = -d;
sD = a;
}
}
else if (tN > tD) { // tc > 1 => the t=1 edge is visible
tN = tD;
// recompute sc for this edge
if ((-d + b) < 0.0)
sN = 0;
else if ((-d + b) > a)
sN = sD;
else {
sN = (-d + b);
sD = a;
}
}
// finally do the division to get sc and tc
sc = (abs(sN) < 1e-8f ? 0.0 : sN / sD);
tc = (abs(tN) < 1e-8f ? 0.0 : tN / tD);
// get the difference of the two closest points
Vector3f dP = w + (u * sc) - (v * tc); // = S1(sc) - S2(tc)
return dP.length(); // return the closest distance
}
Vector3f Line6f::midPointCart() const{
return (startc+endc)/2;
}
Vector3f Line6f::midPointPolar() const{
return midPointCart().toPolar();
}
bool Line6f::operator==(const Line6f& other) const {
return (startp == other.startp) && (endp == other.endp);
}
const Vector3f &Line6f::get_polar_pt(const int index) const{
if(index==0) return startp;
return endp;
}
const Vector3f &Line6f::get_cart_pt(const int index) const{
if(index==0) return startc;
return endc;
}
Vector3f Line6f::get_polar_vector() const{
return endp-startp;
}
Vector3f Line6f::get_cart_vector() const{
return endc-startc;
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/Line6f.cpp
|
C++
|
agpl-3.0
| 7,101
|
/**
* FILENAME: Line6f
* DESCRIPTION: Simple line (segment) class
* AUTHOR: Miguel Abreu (m.abreu@fe.up.pt)
* DATE: 2021
*
* Immutable Class for:
* 3D Line composed of 2 Vector3f points
* Optimized for:
* - regular access to cartesian coordinates
* - regular access to line segment length
**/
#pragma once
#include "Vector3f.h"
class Line6f {
public:
/**
* Polar start coordinate
*/
const Vector3f startp;
/**
* Polar end coordinate
*/
const Vector3f endp;
/**
* Cartesian start coordinate
*/
const Vector3f startc;
/**
* Cartesian end coordinate
*/
const Vector3f endc;
/**
* Length of line segment limited by (start,end)
*/
const float length;
/**
* Constructor
* @param polar_s polar start point
* @param polar_e polar end point
*/
Line6f(const Vector3f &polar_s, const Vector3f &polar_e);
/**
* Constructor
* @param cart_s cartesian start point
* @param cart_e cartesian end point
* @param length line segment length
*/
Line6f(const Vector3f &cart_s, const Vector3f &cart_e, float length);
/**
* Copy Constructor
* @param obj another line
*/
Line6f(const Line6f &obj);
/**
* Find point in line defined by (start,end) closest to given point
* @param cp cartesian point
* @return point in this infinite line closest to given point
*/
Vector3f linePointClosestToCartPoint(const Vector3f &cp) const;
/**
* Find point in line defined by (start,end) closest to given point
* @param pp polar point
* @return point in this infinite line closest to given point
*/
Vector3f linePointClosestToPolarPoint(const Vector3f &pp) const;
/**
* Find distance between line defined by (start,end) and given point
* @param cp cartesian point
* @return distance between line defined by (start,end) and given point
*/
float lineDistToCartPoint(const Vector3f &cp) const;
/**
* Find distance between line defined by (start,end) and given point
* @param pp polar point
* @return distance between line defined by (start,end) and given point
*/
float lineDistToPolarPoint(const Vector3f &pp) const;
/**
* Find distance between line defined by (start,end) and given line
* @param l line
* @return distance between line defined by (start,end) and given line
*/
float lineDistToLine(const Line6f &l) const;
/**
* Find point in line defined by (start,end) closest to given point
* @param cp cartesian point
* @return point in this infinite line closest to given point
*/
Vector3f segmentPointClosestToCartPoint(const Vector3f &cp) const;
/**
* Find point in line defined by (start,end) closest to given point
* @param pp polar point
* @return point in this infinite line closest to given point
*/
Vector3f segmentPointClosestToPolarPoint(const Vector3f &pp) const;
/**
* Find distance between line segment limited by (start,end) and given point
* @param cp cartesian point
* @return distance between line segment limited by (start,end) and given point
*/
float segmentDistToCartPoint(const Vector3f &cp) const;
/**
* Find distance between line segment limited by (start,end) and given point
* @param pp polar point
* @return distance between line segment limited by (start,end) and given point
*/
float segmentDistToPolarPoint(const Vector3f &pp) const;
/**
* Find distance between line segment limited by (start,end) and given line segment
* @param other line segment
* @return distance between line segment limited by (start,end) and given line segment
*/
float segmentDistToSegment(const Line6f &other) const;
/**
* Find midpoint of line segment limited by (start,end)
* @return cartesian midpoint of line segment limited by (start,end)
*/
Vector3f midPointCart() const;
/**
* Find midpoint of line segment limited by (start,end)
* @return polar midpoint of line segment limited by (start,end)
*/
Vector3f midPointPolar() const;
/**
* Operator ==
* @param other line
* @return true if both lines are the same
*/
bool operator==(const Line6f& other) const;
/**
* Get polar line ending by index
* @param index (0)->start or (1)->end
* @return polar line ending according to given index
*/
const Vector3f &get_polar_pt(const int index) const;
/**
* Get cartesian line ending by index
* @param index (0)->start or (1)->end
* @return cartesian line ending according to given index
*/
const Vector3f &get_cart_pt(const int index) const;
/**
* Get polar vector (end-start)
* @return polar vector (end-start)
*/
Vector3f get_polar_vector() const;
/**
* Get cartesian vector (end-start)
* @return cartesian vector (end-start)
*/
Vector3f get_cart_vector() const;
};
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/Line6f.h
|
C++
|
agpl-3.0
| 5,142
|
#include "LocalizerV2.h"
#include "math.h"
#include "iostream"
#include "World.h"
using namespace std;
static World& world = SWorld::getInstance();
/**
* Compute 3D position and 3D orientation
* */
void LocalizerV2::run(){
Field& fd = SField::getInstance();
stats_change_state(RUNNING);
//------------------ WORKFLOW: 0
_is_uptodate = false;
_is_head_z_uptodate = false;
_steps_since_last_update++;
prelim_reset(); //reset preliminary transformation matrix
fd.update(); //update visible collections
int lines_no = fd.list_segments.size();
int landmarks_no = fd.list_landmarks.size();
if( (landmarks_no == 0 && lines_no < 2) || (lines_no == 0) ){
if(lines_no==0 && landmarks_no==0){ stats_change_state(BLIND); } else { stats_change_state(MINFAIL); }
return;
}
//------------------ WORKFLOW: 1-2
if( ! find_z_axis_orient_vec() ){ return; }
//------------------ WORKFLOW: 3-4
if(!( landmarks_no >1 ? find_xy() : guess_xy() )){ return; }
//------------------ Update public variables
commit_everything();
stats_change_state(DONE);
//------------------ Statistics
//Ball position stats
if(world.ball_seen){
counter_ball += stats_sample_position_error(prelimHeadToField * world.ball_rel_pos_cart,
world.ball_cheat_abs_cart_pos, errorSum_ball);
}
//print_report(); //uncomment to enable report (average errors + solution analysis)
}
//=================================================================================================
//=================================================================================== GSL Utilities
//=================================================================================================
void add_gsl_regression_sample(gsl_matrix* m, gsl_vector* v, int sample_no, const Vector3f& relativeCoord, double absoluteCoord, double translCoeffMult=1){
gsl_matrix_set(m, sample_no, 0, relativeCoord.x);
gsl_matrix_set(m, sample_no, 1, relativeCoord.y);
gsl_matrix_set(m, sample_no, 2, relativeCoord.z);
gsl_matrix_set(m, sample_no, 3, translCoeffMult);
gsl_vector_set(v, sample_no, absoluteCoord );
}
template<std::size_t SIZE>
gsl_vector* create_gsl_vector(const std::array<double, SIZE> &content){
gsl_vector* v = gsl_vector_alloc (SIZE);
for(int i=0; i<SIZE; i++){
gsl_vector_set(v, i, content[i]);
}
return v;
}
//=================================================================================================
//================================================================================== Math utilities
//=================================================================================================
/**
* Get unit vector on plane Z=0, perpendicular to given vector
* Mathematically this is (0,0,1)x(vec)/|vec| with some additional checks
*/
Vector get_ground_unit_vec_perpendicular_to(const Vector3f& vec){
float gx = 1, gy = 0; //rotation axis unit vector, default:(1,0,0)
const float aux = sqrtf(vec.x*vec.x + vec.y*vec.y); //rotation axis length before becoming a unit vector
if(aux > 0){ //if vec is (0,0,1) or (0,0,-1) we keep the default rotation axis
gx = -vec.y / aux;
gy = vec.x / aux;
}
return Vector(gx,gy);
}
/**
* Rotate vector around ground axis defined as u=(0,0,1)x(Zvec)/|Zvec|
* Direction of rotation from Zvec to (0,0,1)
* To invert the rotation direction, invert Zvec.x and Zvec.y
* @param v vector to be rotated
* @param Zvec unit normal vector of rotated ground plane
*/
Vector3f fast_rotate_around_ground_axis(Vector3f v, Vector3f Zvec){
Vector u = get_ground_unit_vec_perpendicular_to(Zvec);
//Angle between unit normal vector of original plane and unit normal vector of rotated plane:
//cos(a) = (ov.rv)/(|ov||rv|) = ((0,0,1).(rvx,rvy,rvz))/(1*1) = rvz
float& cos_a = Zvec.z;
//assert(cos_a <= 1);
if(cos_a > 1) cos_a = 1; //Fix: it happens rarely, no cause was yet detected
float sin_a = -sqrtf(1 - cos_a*cos_a); //invert sin_a to invert a (direction was defined in method description)
const float i_cos_a = 1-cos_a;
const float uxuy_i = u.x*u.y*i_cos_a;
const float uxux_i = u.x*u.x*i_cos_a;
const float uyuy_i = u.y*u.y*i_cos_a;
const float uxsin_a = u.x*sin_a;
const float uysin_a = u.y*sin_a;
float x = (cos_a + uxux_i ) * v.x + uxuy_i * v.y + uysin_a * v.z;
float y = uxuy_i * v.x + (cos_a + uyuy_i ) * v.y - uxsin_a * v.z;
float z = - uysin_a * v.x + uxsin_a * v.y + cos_a * v.z;
return Vector3f(x,y,z);
}
/**
* Compute Xvec and Yvec from Zvec and angle
* @param Zvec input z-axis orientation vector
* @param angle input rotation of Xvec around Zvec (rads)
* @param Xvec output x-axis orientation vector
* @param Yvec output y-axis orientation vector
*/
void fast_compute_XYvec_from_Zvec(const Vector3f& Zvec, float agent_angle, Vector3f& Xvec, Vector3f& Yvec){
/**
* There are two coordinate systems being considered in this method:
* - The actual agent's vision (RELATIVE system -> RELsys)
* - A rotated perspective where the agent's seen Zvec is the real Zvec (ROTATED system -> ROTsys)
* (the agent's optical axis / line of sight is parallel to ground )
*
* SUMMARY:
* I provide an angle which defines the agent's rotation around Zvec (in the ROTsys)
* E.g. suppose the agent is rotated 5deg, then in the ROTsys, the agent sees Xvec as being rotated -5deg
* I then rotate Xvec to the RELsys, and compute the Yvec using cross product
*
* STEPS:
* 1st. Compute ROTsys, by finding rotation of plane defined by normal vector Zvec, in relation to seen XY plane
* (whose seen normal vector is (0,0,1)). We need: axis of rotation (unit vector lying on XY plane) and angle (rads):
* rotation axis:
* u = (0,0,1)x(Zvec) (vector perpendicular to XY plane and Zvec)
* = (-ZvecY,ZvecX,0) (rotation will be counterclockwise when u points towards the observer)
* (so a negative angle will bring the agent to the ROTsys)
* angle between Zvec and (0,0,1):
* a = acos( ((0,0,1).(Zvec)) / (|(0,0,1)|*|Zvec|) )
* = acos( ((0,0,1).(Zvec)) )
* = acos( ZvecZ )
*
* 2nd. Establish Xvec in ROTsys:
* Let agent_angle be the agent's angle. Then Xvec's angle is (b=-agent_angle).
* Xvec = (cos(b),sin(b),0)
*
* 3rd. Rotate Xvec to RELsys:
* Let R be the rotation matrix that rotates from ROTsys to RELsys (positive angle using u):
* Xvec = R * Xvec
* = R * (cos(b),sin(b),0)
* = (R00 * cos(b) + R01 * sin(b), R10 * cos(b) + R11 * sin(b), R20 * cos(b) + R21 * sin(b))
* where R is: (rotation matrix from axis and angle https://en.wikipedia.org/wiki/Rotation_matrix#Rotation_matrix_from_axis_and_angle)
* R00 = cos(a) + ux*ux(1-cos(a)) R01 = ux*uy(1-cos(a))
* R10 = uy*ux(1-cos(a)) R11 = cos(a) + uy*uy(1-cos(a))
* R20 = -uy*sin(a) R21 = ux*sin(a)
* so Xvec becomes:
* XvecX = cos(a)*cos(b) + (1-cos(a))*(ux*ux*cos(b) + ux*uy*sin(b))
* XvecY = cos(a)*sin(b) + (1-cos(a))*(uy*uy*sin(b) + ux*uy*cos(b))
* XvecZ = sin(a)*(ux*sin(b) - uy*cos(b))
*
* 4th. To find Yvec we have two options:
* A. add pi/2 to b and compute Yvec with the same expression used for Xvec
* B. Yvec = Zvec x Xvec (specifically in this order for original coordinate system)
*/
Vector u = get_ground_unit_vec_perpendicular_to(Zvec);
const float& cos_a = Zvec.z;
const float sin_a = sqrtf(1 - cos_a*cos_a);
const float uxuy = u.x * u.y;
const float b = -agent_angle; //Xvec's angle
const float cos_b = cosf(b);
const float sin_b = sinf(b);
const float i_cos_a = 1-cos_a;
Xvec.x = cos_a * cos_b + i_cos_a * ( u.x*u.x*cos_b + uxuy*sin_b );
Xvec.y = cos_a * sin_b + i_cos_a * ( u.y*u.y*sin_b + uxuy*cos_b );
Xvec.z = sin_a * ( u.x*sin_b - u.y*cos_b );
Yvec = Zvec.crossProduct(Xvec); //Using original coordinate system
}
//=================================================================================================
//================================================================================== Main algorithm
//=================================================================================================
/**
* WORKFLOW: 1
* See workflow description on header
*/
bool LocalizerV2::find_z_axis_orient_vec(){
Field& fd = SField::getInstance();
const int goalNo = fd.list_landmarks_goalposts.size();
if(fd.non_collinear_ground_markers >= 3){
fit_ground_plane();
return true;
}
/**
* At this point we have > 0 lines. Having 1 line is the most common option.
* But having 2 lines is also possible if at least one of them is too short to create 2 reference points.
* But those 2 lines can be both too short, which would not be ideal for the algorithms below.
* Steps: find largest line, check if it is large enough
*/
const Line6f* l = &fd.list_segments.front();
if(fd.list_segments.size() > 1){
for(const auto& ll: fd.list_segments){
if(ll.length > l->length) l = ≪
}
}
if(l->length < 1){ //larger line is too short
stats_change_state(FAILzLine);
return false;
}
if(goalNo == 0){ //Case e.g.: 1 corner and 1 line (or 2 short lines)
stats_change_state(FAILzNOgoal);
return false;
}
//------------------------------ Prepare variables for solution A & B
// Get any crossbar point
Vector3f crossbar_pt;
auto& glist = fd.list_landmarks_goalposts;
if(goalNo == 1){
crossbar_pt = glist.front().relPosCart;
}else{
if(glist[0].absPos.x == glist[1].absPos.x){
crossbar_pt = Vector3f::determineMidpoint( glist[0].relPosCart, glist[1].relPosCart);
}else{
//extremely rare: there are other solutions when goalNo>2 but cost/benefit not worth it
crossbar_pt = glist.front().relPosCart;
}
}
//------------------------------ Identify and apply solution A & B
//Get line point closest to crossbar point
Vector3f p = l->linePointClosestToCartPoint( crossbar_pt );
Vector3f possibleZvec = crossbar_pt - p;
float possibleZvecLength = possibleZvec.length();
if(fabsf(possibleZvecLength - 0.8) < 0.05){ //Solution A & B
Vector3f unit_zvec = possibleZvec / possibleZvecLength;
// save as the new z axis orientation vector
prelimHeadToField.set(2,0,unit_zvec.x);
prelimHeadToField.set(2,1,unit_zvec.y);
prelimHeadToField.set(2,2,unit_zvec.z);
find_z(unit_zvec);
return true;
}
//------------------------------ Apply solution C
Vector3f crossbar_left_vec, crossbar_midp; //this crossbar vector points left if seen from the midfield (this is important for the cross product)
const auto& goal_mm = Field::list_8_landmarks::goal_mm;
const auto& goal_mp = Field::list_8_landmarks::goal_mp;
const auto& goal_pm = Field::list_8_landmarks::goal_pm;
const auto& goal_pp = Field::list_8_landmarks::goal_pp;
if( goal_mm.visible && goal_mp.visible){
crossbar_left_vec = goal_mm.relPosCart - goal_mp.relPosCart;
crossbar_midp = (goal_mm.relPosCart + goal_mp.relPosCart)/2;
}else if( goal_pp.visible && goal_pm.visible){
crossbar_left_vec = goal_pp.relPosCart - goal_pm.relPosCart;
crossbar_midp = (goal_pp.relPosCart + goal_pm.relPosCart)/2;
}
/**
* Check if angle between line and crossbar is between 45deg and 135deg
* 45deg < acos(line.crossbar / |line||crossbar|) < 135deg <=>
* | line.crossbar / |line||crossbar| | < cos(45deg) <=>
* | line.crossbar | < cos(45deg) * |line| * ~2.1 <=>
* | line.crossbar | < 1.485 * |line|
*/
Vector3f lvec = l->get_cart_vector();
if( goalNo > 1 && fabsf(lvec.innerProduct(crossbar_left_vec)) < 1.485 * l->length ){
Vector3f Zvec;
if(l->startc.dist(crossbar_midp) > l->endc.dist(crossbar_midp)){
Zvec = lvec.crossProduct(crossbar_left_vec);
}else{
Zvec = crossbar_left_vec.crossProduct(lvec);
}
Zvec = Zvec.normalize(); // get unit vector
// save as the new z axis orientation vector
prelimHeadToField.set(2,0,Zvec.x);
prelimHeadToField.set(2,1,Zvec.y);
prelimHeadToField.set(2,2,Zvec.z);
find_z(Zvec);
return true;
}
stats_change_state(FAILz);
return false; //no solution was found
}
/**
* Find the best fitting ground plane's normal vector using Singular Value Decomposition
* Also compute the agent's height based on the ground references' centroid
* Dependency: at least 3 ground references
*/
void LocalizerV2::fit_ground_plane(){
Field& fd = SField::getInstance();
const auto& ground_markers = fd.list_weighted_ground_markers;
const int ground_m_size = ground_markers.size();
//------------------------------------ Compute groundmarks plane (if we have at least 3 groundmarks)
gsl_matrix *A, *V;
gsl_vector *S, *work;
A = gsl_matrix_alloc(ground_m_size, 3);
V = gsl_matrix_alloc(3, 3);
S = gsl_vector_alloc(3);
work = gsl_vector_alloc(3);
// Find the centroid
Vector3f centroid(0,0,0);
for(const auto& g : ground_markers){ // Insert all weighted groundmarks in matrix
centroid += g.relPosCart;
}
centroid /= (float)ground_m_size;
// Insert all groundmarks in matrix after subtracting the centroid
for(int i=0; i<ground_m_size; i++){
gsl_matrix_set(A,i,0, ground_markers[i].relPosCart.x - centroid.x );
gsl_matrix_set(A,i,1, ground_markers[i].relPosCart.y - centroid.y );
gsl_matrix_set(A,i,2, ground_markers[i].relPosCart.z - centroid.z );
}
// Singular value decomposition to find best fitting plane
gsl_linalg_SV_decomp(A,V,S,work);
// plane: ax + by + cz = d
double a = gsl_matrix_get(V,0,2);
double b = gsl_matrix_get(V,1,2);
double c = gsl_matrix_get(V,2,2);
/**
* Plane equation ax + by + cz = d
* therefore, considering the centroid(x,y,z) and normalvec(a,b,c):
* d = ax + by + cz
* = normalvec . centroid
*/
double d = a*centroid.x + b*centroid.y + c*centroid.z;
//Note: |d| is an estimate of the agent's height, but we can do better by including aerial references in the prediction later
gsl_matrix_free (A);
gsl_matrix_free (V);
gsl_vector_free (S);
gsl_vector_free (work);
/**
* Unfortunately, the normal vector doesn't always point up
* The plane is defined by (ax + by + cz - d = 0)
* Replacing [x,y,z] by a random point p, the sign of (ax + by + cz - d) gives us p's side
* Proof that the sign of (ax + by + cz - d) is positive for (rnd_ground_pt(i,j,k) + normal vector(a,b,c)):
* ax + by + cz - d =
* a(i+a) + b(j+b) + c(k+c) - (a*i + b*j + c*k) =
* ai + aa + bj + bb + ck + cc - ai - bj - ck =
* aa + bb + cc (which is always positive and equal to 1 because it is a unit vector)
* Since the agent is always above the field, we only need to make sure it's on the same side as the normal vector
* To do that, we check if the origin of the relative coordinate system (agent's head) gives a positive sign for (ax + by + cz - d)
* ax + by + cz - d > 0 (replacing x=0, y=0, z=0)
* d < 0
*
* However, if the agent is lying down, the optimized plane may be slightly above its head due to vision errors
* So, if we have a better reference point, such as a goal post, we use it
*/
if(!fd.list_landmarks_goalposts.empty()){ //If there are visible goal posts
const Vector3f& aerialpt = fd.list_landmarks_goalposts.front().relPosCart; //random aerial point (goal post)
if( a*aerialpt.x + b*aerialpt.y + c*aerialpt.z < d ){ //the goalpost is on the negative side, so we invert the normal vector
a=-a; b=-b; c=-c;
}
}else{ //If there are no visible goal posts, we rely on the agent's head
if(d > 0){// the normal vectors points down, so we invert it (we ignore 'd' from this point forward)
a=-a; b=-b; c=-c;
}
}
// save the ground plane's normal vector as the new z axis orientation vector
prelimHeadToField.set(2,0,a);
prelimHeadToField.set(2,1,b);
prelimHeadToField.set(2,2,c);
// compute the agent's height
float h = max( -centroid.x*a - centroid.y*b - centroid.z*c ,0.064);
prelimHeadToField.set(2,3, h );
//Set public independent coordinate z (Not a problem: may be out of sync with transf matrix)
last_z = final_z;
final_z = h;
_is_head_z_uptodate = true;
}
/**
* Compute translation in z (height)
* Note: Apparently there's no real benefit in involving goalposts (weighted or not), only when the
* visible objects are below 5/6, and even then the difference is minimal.
*/
void LocalizerV2::find_z(const Vector3f& Zvec){
Field& fd = SField::getInstance();
Vector3f zsum;
for(const auto& g: fd.list_weighted_ground_markers){
zsum += g.relPosCart;
}
//Minimum height: 0.064m
float z = max( -(zsum/fd.list_weighted_ground_markers.size()).innerProduct(Zvec) ,0.064f);
prelimHeadToField.set( 2,3,z );
//Set public independent coordinate z (Not a problem: may be out of sync with transf matrix)
last_z = final_z;
final_z = z;
_is_head_z_uptodate = true;
}
/**
* Computes mapping error using distance probabilities
* @return negative log of ["normalized" probability = (p1*p2*p3*...*pn)^(1/n)]
*/
double LocalizerV2::map_error_logprob(const gsl_vector *v, void *params){
float angle;
Field& fd = SField::getInstance();
//Get angle from optimization vector, or from params (as a constant)
if(v->size == 3){
angle = gsl_vector_get(v,2);
}else{
angle = *(float *)params;
}
Matrix4D& transfMat = SLocalizerV2::getInstance().prelimHeadToField;
Vector3f Zvec(transfMat.get(2,0), transfMat.get(2,1), transfMat.get(2,2));
Vector3f Xvec, Yvec;
fast_compute_XYvec_from_Zvec(Zvec, angle, Xvec, Yvec );
//These are the transformation coefficients that are being optimized
transfMat.set(0,0,Xvec.x);
transfMat.set(0,1,Xvec.y);
transfMat.set(0,2,Xvec.z);
transfMat.set(0,3,gsl_vector_get(v, 0));
transfMat.set(1,0,Yvec.x);
transfMat.set(1,1,Yvec.y);
transfMat.set(1,2,Yvec.z);
transfMat.set(1,3,gsl_vector_get(v, 1));
Matrix4D inverseTransMat = transfMat.inverse_tranformation_matrix();
double total_logprob = 0;
int total_err_cnt =0;
//Add log probability of unknown markers (with known corresponding field segment)
for(const auto& u : fd.list_unknown_markers){
//We know the closest field segment, so we can bring it to the agent's frame
Vector3f rel_field_s_start = inverseTransMat * u.fieldSeg->point[0]->get_vector();
Vector3f rel_field_s_end = inverseTransMat * u.fieldSeg->point[1]->get_vector();
Line6f rel_field_s(rel_field_s_start, rel_field_s_end, u.fieldSeg->length); //Convert to Line6f
Vector3f closest_polar_pt = rel_field_s.segmentPointClosestToCartPoint(u.relPosCart).toPolar();
total_logprob += FieldNoise::log_prob_r(closest_polar_pt.x, u.relPosPolar.x);
total_logprob += FieldNoise::log_prob_h(closest_polar_pt.y, u.relPosPolar.y);
total_logprob += FieldNoise::log_prob_v(closest_polar_pt.z, u.relPosPolar.z);
total_err_cnt++;
}
//Add log probability of known markers
for(const auto& k : fd.list_known_markers){
//Bring marker to agent's frame
Vector3f rel_k = (inverseTransMat * k.absPos.get_vector()).toPolar();
total_logprob += FieldNoise::log_prob_r(rel_k.x, k.relPosPolar.x);
total_logprob += FieldNoise::log_prob_h(rel_k.y, k.relPosPolar.y);
total_logprob += FieldNoise::log_prob_v(rel_k.z, k.relPosPolar.z);
total_err_cnt++;
}
//return log of "normalized" probability = (p1*p2*p3*...*pn)^(1/n)
//negative because the optimization method minimizes the loss function
double logNormProb = -total_logprob / total_err_cnt;
if(!gsl_finite(logNormProb)) return 1e6; //fix
return logNormProb;
}
/**
* Computes mapping error using 2d euclidian distances
* @return average distance
*/
double LocalizerV2::map_error_2d(const gsl_vector *v, void *params){
float angle;
Field& fd = SField::getInstance();
//Get angle from optimization vector, or from params (as a constant)
if(v->size == 3){
angle = gsl_vector_get(v,2);
}else{
angle = *(float *)params;
}
Matrix4D& transfMat = SLocalizerV2::getInstance().prelimHeadToField;
Vector3f Zvec(transfMat.get(2,0), transfMat.get(2,1), transfMat.get(2,2));
Vector3f Xvec, Yvec;
fast_compute_XYvec_from_Zvec(Zvec, angle, Xvec, Yvec );
//These are the transformation coefficients that are being optimized
transfMat.set(0,0,Xvec.x);
transfMat.set(0,1,Xvec.y);
transfMat.set(0,2,Xvec.z);
transfMat.set(0,3,gsl_vector_get(v, 0));
transfMat.set(1,0,Yvec.x);
transfMat.set(1,1,Yvec.y);
transfMat.set(1,2,Yvec.z);
transfMat.set(1,3,gsl_vector_get(v, 1));
float total_err = 0;
int total_err_cnt =0;
for(const Line6f& l : fd.list_segments){
//Compute line absolute coordinates according to current transformation
Vector3f ls = transfMat * l.startc;
Vector3f le = transfMat * l.endc;
//Compute line angle and establish a tolerance
float l_angle = 0;
float l_angle_tolerance = 10; //default full tolerance (no point in being larger than pi/2 but no harm either)
if(l.length > 0.8){
//This is the easy case: find the angle and establish a small tolerance (which allows some visual rotation attempts)
l_angle = atan2f(le.y - ls.y, le.x - ls.x);
if(l_angle < 0) { l_angle += 3.14159265f; } //this is a line, not a vector, so positive angles are enough
l_angle_tolerance = 0.35f; //20 degrees
} else if(fd.list_segments.size() <= 3) {
//It gets to a point where the cost/benefit is not very inviting. If there are many lines (>3),
//the small ones are not as decisive for the mapping error. Otherwise, we proceed:
//If the small line is touching a big line, they have different orientations (it's a characteristic from the field lines)
for(const Line6f& lbig : fd.list_segments){
if(lbig.length < 2 || &lbig == &l ) continue; //check if line is big and different from current
if(lbig.segmentDistToSegment(l)<0.5){
//this would only generate false positives with the halfway line and 4 ring lines (if enough vision error)
//but even then, their orientation would be very different, so the method still holds
//---------- get angle perpendicular to bigline (that is either the small line's angle, or at least close enough)
//get bigline angle
Vector3f lbigs = transfMat * lbig.startc;
Vector3f lbige = transfMat * lbig.endc;
l_angle = atan2f(lbige.y - lbigs.y, lbige.x - lbigs.x);
// add 90deg while keeping the angle between 0-180deg (same logic used when l.length > 0.8)
if (l_angle < -1.57079632f){ l_angle += 4.71238898f; } //Q3 -> add pi*3/2
else if(l_angle < 0 ){ l_angle += 1.57079632f; } //Q4 -> add pi/2
else if(l_angle < 1.57079632f ){ l_angle += 1.57079632f; } //Q1 -> add pi/2
else { l_angle -= 1.57079632f; } //Q2 -> subtract pi/2
//This large tolerance means that this small line can be matched with almost everything except perpendicular lines
l_angle_tolerance = 1.22f; //70 deg tolerance
break; //end search for close big lines
}
}
}
//this default error of 1e6f is applied when there is no match (which means the transf. matrix's Xvec/Yvec are wrong)
float min_err = 1e6f;
for(const auto& s : Field::cFieldLineSegments::list){ //find distance to closest field line
//Skip field line if seen line is substantially larger
if( l.length > (s.length + 0.7) ){ continue; }
//Skip field line if orientation does not match
float angle_difference = fabsf(l_angle - s.angle);
if(angle_difference > 1.57079632f) angle_difference = 3.14159265f - angle_difference;
if(angle_difference > l_angle_tolerance) continue;
//Error is the sum of the distance of a single line segment to both endpoints of seen line
float err = Field::fieldLineSegmentDistToCart2DPoint(s,ls.to2d());
if(err < min_err) err += Field::fieldLineSegmentDistToCart2DPoint(s,le.to2d());
if(err < min_err) min_err = err;
}
total_err += min_err;
total_err_cnt+=2; //a line has 2 points, double the weight of a single landmark
}
for(const Field::sMarker& m : fd.list_landmarks){
Vector3f lpt = transfMat * m.relPosCart; //compute absolute coordinates according to current transformation
float err = lpt.to2d().getDistanceTo(Vector(m.absPos.x,m.absPos.y));
total_err += err > 0.5 ? err * 100 : err;
total_err_cnt++;
}
double avg_error = total_err / total_err_cnt;
if(!gsl_finite(avg_error)) return 1e6; //fix
return avg_error; //return average error
}
/**
* Apply fine tuning directly on the prelimHeadToField matrix
* 1st - improve map fitting
* 2nd - identify line segments and their endpoints
* 3rd - fine tune again using known markers
* @param initial_angle initial angle of Xvec around Zvec
* @param initial_x initial translation in x
* @param initial_y initial translation in y
*/
bool LocalizerV2::fine_tune(float initial_angle, float initial_x, float initial_y){
Field& fd = SField::getInstance();
//Statistics before fine tune
counter_fineTune += stats_sample_position_error(Vector3f(initial_x,initial_y,prelimHeadToField.get(11)), world.my_cheat_abs_cart_pos, errorSum_fineTune_before);
//Fine tune, changing the initial parameters directly
if(!fine_tune_aux(initial_angle, initial_x, initial_y, false)) return false;
//Statistics for 1st fine tune
stats_sample_position_error(Vector3f(initial_x,initial_y,prelimHeadToField.get(11)), world.my_cheat_abs_cart_pos, errorSum_fineTune_euclidianDist);
//Identify new markers
fd.update_from_transformation(prelimHeadToField);
//Probabilistic fine tune
fine_tune_aux(initial_angle, initial_x, initial_y, true);
//Statistics for 2nd fine tune
stats_sample_position_error(prelimHeadToField.toVector3f(), world.my_cheat_abs_cart_pos, errorSum_fineTune_probabilistic);
//Update unknown markers absolute position based on refined transformation matrix
fd.update_unknown_markers(prelimHeadToField);
return true;
}
/**
* Apply fine tuning:
* - directly on: initial_angle, initial_x, initial_y (if use_probabilities == false)
* - directly on the prelimHeadToField matrix using probabilities (if use_probabilities == true)
* @param initial_angle initial angle of Xvec around Zvec
* @param initial_x initial translation in x
* @param initial_y initial translation in y
*/
bool LocalizerV2::fine_tune_aux(float &initial_angle, float &initial_x, float &initial_y, bool use_probabilities){
int status, iter=0;
gsl_vector* x = create_gsl_vector<3>({initial_x, initial_y, initial_angle}); // Initial transformation
gsl_vector* ss = create_gsl_vector<3>({0.02, 0.02, 0.03}); // Set initial step sizes
gsl_multimin_function minex_func = {map_error_2d, 3, nullptr}; // error func, variables no., params
if(use_probabilities) minex_func.f = map_error_logprob; // probablity-based error function
const gsl_multimin_fminimizer_type *T = gsl_multimin_fminimizer_nmsimplex2; // algorithm type
gsl_multimin_fminimizer *s = gsl_multimin_fminimizer_alloc (T, 3); // allocate workspace
gsl_multimin_fminimizer_set (s, &minex_func, x, ss); // set workspace
float best_x, best_y, best_ang;
do{
iter++;
status = gsl_multimin_fminimizer_iterate(s);
//*s holds the best solution, not the last solution
best_x = gsl_vector_get (s->x, 0);
best_y = gsl_vector_get (s->x, 1);
best_ang = gsl_vector_get (s->x, 2);
if (status) break;
double size = gsl_multimin_fminimizer_size (s); //minimizer-specific characteristic size
status = gsl_multimin_test_size (size, 1e-3); //This size can be used as a stopping criteria, as the simplex contracts itself near the minimum
}
while ((status == GSL_CONTINUE || use_probabilities) && iter < 40);
float best_map_error = s->fval;
gsl_vector_free(x);
gsl_vector_free(ss);
gsl_multimin_fminimizer_free (s);
if(!use_probabilities){
if(best_map_error > 0.10){
stats_change_state(FAILtune);
return false;
}else{
initial_angle = best_ang;
initial_x = best_x;
initial_y = best_y;
return true;
}
}
/**
* At this point, use_probabilities is true
* Note: The transformations are directly tested on prelimHeadToField but it currently
* holds the last test, so we set it manually here to the best found solution
*/
//Convert angle into Xvec and Yvec
Vector3f Zvec(prelimHeadToField.get(2,0), prelimHeadToField.get(2,1), prelimHeadToField.get(2,2));
Vector3f Xvec, Yvec;
fast_compute_XYvec_from_Zvec(Zvec, best_ang, Xvec, Yvec );
prelimHeadToField.set(0,0, Xvec.x);
prelimHeadToField.set(0,1, Xvec.y);
prelimHeadToField.set(0,2, Xvec.z);
prelimHeadToField.set(0,3, best_x);
prelimHeadToField.set(1,0, Yvec.x);
prelimHeadToField.set(1,1, Yvec.y);
prelimHeadToField.set(1,2, Yvec.z);
prelimHeadToField.set(1,3, best_y);
return true;
}
/**
* Find XY translation/rotation
* A unique solution is guaranteed if Zvec points in the right direction
* Requirement: 2 visible landmarks
*/
bool LocalizerV2::find_xy(){
Field& fd = SField::getInstance();
Vector3f Zvec(prelimHeadToField.get(2,0), prelimHeadToField.get(2,1), prelimHeadToField.get(2,2));
Field::sMarker *m1 = nullptr, *m2 = nullptr;
//Get as many corners as possible
if(fd.list_landmarks_corners.size()>1){
m1 = &fd.list_landmarks_corners[0];
m2 = &fd.list_landmarks_corners[1];
}else if(fd.list_landmarks_corners.size()==1){
m1 = &fd.list_landmarks_corners[0];
m2 = &fd.list_landmarks_goalposts[0];
}else{
m1 = &fd.list_landmarks_goalposts[0];
m2 = &fd.list_landmarks_goalposts[1];
}
Vector3f realVec(m2->absPos.x - m1->absPos.x, m2->absPos.y - m1->absPos.y, m2->absPos.z - m1->absPos.z);
float real_angle = atan2f(realVec.y, realVec.x); //angle of real vector
Vector3f seenVec(m2->relPosCart - m1->relPosCart);
Vector3f rotated_abs_vec = fast_rotate_around_ground_axis(seenVec, Zvec);
float seen_angle = atan2f(rotated_abs_vec.y, rotated_abs_vec.x); //angle of real vector
float AgentAngle = real_angle - seen_angle; //no normalization is needed
Vector3f Xvec, Yvec;
fast_compute_XYvec_from_Zvec(Zvec, AgentAngle, Xvec, Yvec );
/**
* Let m be a landmark, rel:(mx,my,mz), abs:(mabsx, mabsy, mabsz)
* XvecX*mx + XvecY*my + XvecZ*mz + AgentX = mabsx
* AgentX = mabsx - (XvecX*mx + XvecY*my + XvecZ*mz)
* AgentX = mabsx - (XvecX . m)
*
* Generalizing for N estimates:
* AgentX = sum( mabsx - (XvecX . m) )/N
*/
float initial_x = 0, initial_y = 0;
for(const Field::sMarker& m : fd.list_landmarks){
initial_x += m.absPos.x - Xvec.innerProduct(m.relPosCart);
initial_y += m.absPos.y - Yvec.innerProduct(m.relPosCart);
}
initial_x /= fd.list_landmarks.size();
initial_y /= fd.list_landmarks.size();
return fine_tune(AgentAngle, initial_x, initial_y);
}
bool LocalizerV2::guess_xy(){
Field& fd = SField::getInstance();
//Get Zvec from previous steps
Vector3f Zvec(prelimHeadToField.get(2,0), prelimHeadToField.get(2,1), prelimHeadToField.get(2,2));
Vector last_known_position(head_position.x, head_position.y);
//------------------------------------------------------------ Get longest line and use it as X or Y vector
const Line6f* longestLine = &fd.list_segments.front();
for(const Line6f& l : fd.list_segments){
if(l.length > longestLine->length) longestLine = &l;
}
if(longestLine->length < 1.6){
stats_change_state(FAILguessLine);
return false; //largest line is too short, it could be mistaken for a ring line
}
//Rotate line to real ground plane, where it loses the 3rd dimension
Vector3f longestLineVec = longestLine->endc - longestLine->startc;
Vector3f rotated_abs_line = fast_rotate_around_ground_axis(longestLineVec, Zvec);
//The line can be aligned with X or Y, positively or negatively (these angles don't need to be normalized)
float fixed_angle[4];
fixed_angle[0] = -atan2f(rotated_abs_line.y,rotated_abs_line.x); //if longestLineVec is Xvec
fixed_angle[1] = fixed_angle[0] + 3.14159265f; //if longestLineVec is -Xvec
fixed_angle[2] = fixed_angle[0] + 1.57079633f; //if longestLineVec is Yvec
fixed_angle[3] = fixed_angle[0] - 1.57079633f; //if longestLineVec is -Yvec
//------------------------------------------------------------ Get initial translation
//if we see 1 landmark, we use it, if not, we get the last position
float initial_x[4], initial_y[4];
bool noLandmarks = fd.list_landmarks.empty();
if(noLandmarks){
for(int i=0; i<4; i++){
initial_x[i] = last_known_position.x;
initial_y[i] = last_known_position.y;
}
} else {
Vector3f Xvec = longestLineVec / longestLine->length;
Vector3f Yvec(Zvec.crossProduct(Xvec));
/**
* Let m be a landmark, rel:(mx,my,mz), abs:(mabsx, mabsy, mabsz)
* XvecX*mx + XvecY*my + XvecZ*mz + AgentX = mabsx
* AgentX = mabsx - (XvecX*mx + XvecY*my + XvecZ*mz)
* AgentX = mabsx - (XvecX . m)
*/
const Field::sMarker& m = fd.list_landmarks.front();
const float x_aux = Xvec.innerProduct(m.relPosCart);
const float y_aux = Yvec.innerProduct(m.relPosCart);
initial_x[0] = m.absPos.x - x_aux;
initial_y[0] = m.absPos.y - y_aux;
initial_x[1] = m.absPos.x + x_aux; //2nd version: X is inverted
initial_y[1] = m.absPos.y + y_aux; //2nd version: Y is inverted
initial_x[2] = m.absPos.x + y_aux; //3rd version: X is inverted Y
initial_y[2] = m.absPos.y - x_aux; //3rd version: Y is X
initial_x[3] = m.absPos.x - y_aux; //4th version: X is Y
initial_y[3] = m.absPos.y + x_aux; //4th version: Y is inverted X
}
//------------------------------------------------------------ Optimize XY rotation for each possible orientation
const gsl_multimin_fminimizer_type *T = gsl_multimin_fminimizer_nmsimplex2;
gsl_multimin_fminimizer *s[4] = {nullptr,nullptr,nullptr,nullptr};
gsl_vector *ss[4], *x[4];
gsl_multimin_function minex_func[4];
size_t iter = 0;
int status;
double size;
for(int i=0; i<4; i++){
x[i] = create_gsl_vector<2>({initial_x[i], initial_y[i]}); // Initial transformation
ss[i] = create_gsl_vector<2>({1, 1}); //Set initial step sizes to 1
/* Initialize method */
minex_func[i].n = 2;
minex_func[i].f = map_error_2d;
minex_func[i].params = &fixed_angle[i];
s[i] = gsl_multimin_fminimizer_alloc (T, 2);
gsl_multimin_fminimizer_set (s[i], &minex_func[i], x[i], ss[i]);
}
/* start iterating */
bool running[4] = {true,true,true,true};
float current_error[4] = {1e6,1e6,1e6,1e6};
float lowest_error = 1e6;
Vector best_xy[4];
const int maximum_iterations = 50;
bool plausible_solution[4] = {false,false,false,false};
do{
iter++;
for(int i=0; i<4; i++){
if(!running[i]) continue;
status = gsl_multimin_fminimizer_iterate(s[i]);
current_error[i] = s[i]->fval;
if(current_error[i] < lowest_error) lowest_error = current_error[i];
// Possible errors:
// GSL_ERROR ("incompatible size of x", GSL_EINVAL); This should only be a concern during code design
// GSL_ERROR ("contraction failed", GSL_EFAILED); Evaluation function produced non finite value
if (status) {
running[i]=false; //This is not a valid solution
continue;
}
size = gsl_multimin_fminimizer_size (s[i]); //minimizer-specific characteristic size
status = gsl_multimin_test_size (size, 1e-2); //This size can be used as a stopping criteria, as the simplex contracts itself near the minimum
if(status != GSL_CONTINUE || (lowest_error * 50 < current_error[i])) { //finished or aborted
best_xy[i].x = gsl_vector_get (s[i]->x, 0);
best_xy[i].y = gsl_vector_get (s[i]->x, 1);
running[i]=false;
plausible_solution[i]=(status == GSL_SUCCESS); //only valid if it converged to local minimum
continue;
}
}
} while (iter < maximum_iterations && (running[0] || running[1] || running[2] || running[3]));
for(int i=0; i<4; i++){
gsl_vector_free(x[i]);
gsl_vector_free(ss[i]);
gsl_multimin_fminimizer_free (s[i]);
}
//At this point, a solution is plausible if it converged to a local minimum
//So, we apply the remaining criteria for plausiblity
int plausible_count = 0;
int last_i;
for(int i=0; i<4; i++){
if(!plausible_solution[i]) continue;
bool isDistanceOk = (!noLandmarks) || last_known_position.getDistanceTo(best_xy[i]) < 0.5; // distance to last known position
if(current_error[i] < 0.12 && isDistanceOk){ // mapping error
plausible_count++;
last_i = i;
}
}
// If there is 1 landmark, and multiple options, the distance to last known pos is now used to eliminate candidates
if(!noLandmarks && plausible_count>1){
plausible_count = 0;
for(int i=0; i<4; i++){
if(plausible_solution[i] && last_known_position.getDistanceTo(best_xy[i]) < 0.5){ // distance to last known position
plausible_count++;
last_i = i;
}
}
}
//Check if best solution is good if all others are not even plausible
if(plausible_count==0){
stats_change_state(FAILguessNone);
return false;
}else if(plausible_count>1){
stats_change_state(FAILguessMany);
return false;
}else if(current_error[last_i] > 0.06 || (noLandmarks && last_known_position.getDistanceTo(best_xy[last_i]) > 0.3)){ // mapping error / distance to last known position
stats_change_state(FAILguessTest);
return false;
}
return fine_tune(fixed_angle[last_i],best_xy[last_i].x, best_xy[last_i].y);
}
/**
* Called to update every public variable (rotation + translation)
*/
void LocalizerV2::commit_everything(){
final_headTofieldTransform = prelimHeadToField; //Full transformation (relative to absolute)
final_headTofieldTransform.inverse_tranformation_matrix( final_fieldToheadTransform ); //Full transformation (absolute to relative)
for(int i=0; i<3; i++){
//Rotation (relative to absolute)
final_headTofieldRotate.set(i ,final_headTofieldTransform.get(i )); //Copy rotation line 1
final_headTofieldRotate.set(i+4,final_headTofieldTransform.get(i+4)); //Copy rotation line 2
final_headTofieldRotate.set(i+8,final_headTofieldTransform.get(i+8)); //Copy rotation line 3
//Rotation (absolute to relative)
final_fieldToheadRotate.set(i ,final_fieldToheadTransform.get(i )); //Copy rotation line 1
final_fieldToheadRotate.set(i+4,final_fieldToheadTransform.get(i+4)); //Copy rotation line 2
final_fieldToheadRotate.set(i+8,final_fieldToheadTransform.get(i+8)); //Copy rotation line 3
}
final_translation = final_headTofieldTransform.toVector3f();
_is_uptodate = true;
_steps_since_last_update = 0;
//Add current 3D position to history
position_history[position_history_ptr++] = final_translation;
if(position_history_ptr >= position_history.size()) position_history_ptr=0;
}
Vector3f LocalizerV2::relativeToAbsoluteCoordinates(const Vector3f relativeCoordinates) const{
return headTofieldTransform * relativeCoordinates;
}
Vector3f LocalizerV2::absoluteToRelativeCoordinates(const Vector3f absoluteCoordinates) const{
return fieldToheadTransform * absoluteCoordinates;
}
/**
* Reset preliminary matrix with a constant for the first 3 rows
*/
void LocalizerV2::prelim_reset(){
//Since it was initialized as identity matrix we never need to change the last row
for(int i=0; i<12; i++){
prelimHeadToField.set(i,9999); //flag unchanged cells with a constant
}
}
//=================================================================================================
//=============================================================================== useful statistics
//=================================================================================================
Vector3f LocalizerV2::get_velocity(unsigned int n) const{
//assert(n > 0 && n < position_history.size() && "LocalizerV2::get_velocity(unsigned int n) -> n must be between 1 and 9!");
int l = position_history.size() - 1;
Vector3f current_pos = position_history[(position_history_ptr + l) % position_history.size()];
Vector3f last_pos = position_history[(position_history_ptr + l - n) % position_history.size()];
return current_pos - last_pos;
}
//=================================================================================================
//================================================================================ debug statistics
//=================================================================================================
void LocalizerV2::print_report() const{
if(counter_fineTune == 0){
cout << "LocalizerV2 Report - Check if the server is providing cheat data.\n";
return;
}
if(counter_fineTune < 2) return; //otherwise, c-1=0
const int &c = counter_fineTune;
const int &cb = counter_ball;
const int c1 = c-1;
const int cb1 = cb-1;
const double* ptr = errorSum_fineTune_before;
float e1_2d_var = (ptr[4] - (ptr[3]*ptr[3]) / c) / c1;
float e1_3d_var = (ptr[6] - (ptr[5]*ptr[5]) / c) / c1;
float e1[] = { ptr[3]/c, sqrt(e1_2d_var), ptr[5]/c, sqrt(e1_3d_var), ptr[0]/c, ptr[1]/c, ptr[2]/c };
ptr = errorSum_fineTune_euclidianDist;
float e2_2d_var = (ptr[4] - (ptr[3]*ptr[3]) / c) / c1;
float e2_3d_var = (ptr[6] - (ptr[5]*ptr[5]) / c) / c1;
float e2[] = { ptr[3]/c, sqrt(e2_2d_var), ptr[5]/c, sqrt(e2_3d_var), ptr[0]/c, ptr[1]/c, ptr[2]/c };
ptr = errorSum_fineTune_probabilistic;
float e3_2d_var = (ptr[4] - (ptr[3]*ptr[3]) / c) / c1;
float e3_3d_var = (ptr[6] - (ptr[5]*ptr[5]) / c) / c1;
float e3[] = { ptr[3]/c, sqrt(e3_2d_var), ptr[5]/c, sqrt(e3_3d_var), ptr[0]/c, ptr[1]/c, ptr[2]/c };
ptr = errorSum_ball;
float e4_2d_var=0, e4_3d_var=0;
if(cb1 > 0){
e4_2d_var = (ptr[4] - (ptr[3]*ptr[3]) / cb) / cb1;
e4_3d_var = (ptr[6] - (ptr[5]*ptr[5]) / cb) / cb1;
}
float e4[] = { ptr[3]/cb, sqrt(e4_2d_var), ptr[5]/cb, sqrt(e4_3d_var), ptr[0]/cb, ptr[1]/cb, ptr[2]/cb };
const int* st = state_counter;
printf("---------------------------------- LocalizerV2 Report ----------------------------------\n");
printf("SAMPLING STAGE 2D-MAE 2D-STD 3D-MAE 3D-STD x-MBE y-MBE z-MBE\n");
printf("Before fine-tune: %.4f %.4f %.4f %.4f %7.4f %7.4f %7.4f\n", e1[0],e1[1],e1[2],e1[3],e1[4],e1[5],e1[6]);
printf("After Euclidian dist. fit: %.4f %.4f %.4f %.4f %7.4f %7.4f %7.4f\n", e2[0],e2[1],e2[2],e2[3],e2[4],e2[5],e2[6]);
printf("After probabilistic fit: %.4f %.4f %.4f %.4f %7.4f %7.4f %7.4f\n", e3[0],e3[1],e3[2],e3[3],e3[4],e3[5],e3[6]);
printf("Ball: %.4f %.4f %.4f %.4f %7.4f %7.4f %7.4f\n\n", e4[0],e4[1],e4[2],e4[3],e4[4],e4[5],e4[6]);
printf("* MBE(Mean Bias Error) MAE(Mean Abs Error) STD(Standard Deviation)\n");
printf("* Note: the cheat positions should be active in server (preferably with >2 decimal places)\n\n");
printf("------------------LocalizerV2::run calls analysis:\n");
printf("- Total: %i \n", st[RUNNING]);
printf("- Successful: %i \n", st[DONE]);
printf("- Blind agent: %i \n", st[BLIND]);
printf("- Almost blind: %i \n", st[MINFAIL] + st[FAILzNOgoal] + st[FAILzLine] + st[FAILz]);
printf("- Guess location fail: %i \n", st[FAILguessLine] + st[FAILguessNone] + st[FAILguessMany] + st[FAILguessTest]);
printf("--- Lines too short: %i \n", st[FAILguessLine]);
printf("--- No solution: %i \n", st[FAILguessNone]);
printf("--- >1 solution: %i \n", st[FAILguessMany]);
printf("--- Weak solution: %i \n", st[FAILguessTest]);
printf("- Eucl. tune fail: %i \n", st[FAILtune]); //Euclidian distance tune error above 6cm
printf("----------------------------------------------------------------------------------------\n");
}
void LocalizerV2::stats_reset(){
counter_fineTune = 0;
for(int i=0; i<sizeof(errorSum_fineTune_before)/sizeof(errorSum_fineTune_before[0]); i++){
errorSum_fineTune_before[i] = 0;
errorSum_fineTune_euclidianDist[i] = 0;
errorSum_fineTune_probabilistic[i] = 0;
}
for(int i=0; i<STATE::ENUMSIZE; i++){
state_counter[i] = 0;
}
}
void LocalizerV2::stats_change_state(enum STATE s){
state_counter[s]++;
state = s;
}
/**
* Take a sample of the current position error
* @param position estimated agent's position
* @param cheat actual position provided by server
* @param error_placeholder variable where result is stored
*/
int LocalizerV2::stats_sample_position_error(const Vector3f sample, const Vector3f& cheat, double error_placeholder[]){
if(world.my_cheat_abs_cart_pos == Vector3f(0,0,0)) return 0;
double x_err = sample.x - cheat.x;
double y_err = sample.y - cheat.y;
double z_err = sample.z - cheat.z;
double xx_err = x_err * x_err;
double yy_err = y_err * y_err;
double zz_err = z_err * z_err;
error_placeholder[0] += x_err;
error_placeholder[1] += y_err;
error_placeholder[2] += z_err;
double sq_err_2d = xx_err + yy_err;
error_placeholder[3] += sqrt(sq_err_2d);
error_placeholder[4] += sq_err_2d;
double sq_err_3d = xx_err + yy_err + zz_err;
error_placeholder[5] += sqrt(sq_err_3d);
error_placeholder[6] += sq_err_3d;
return 1;
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/LocalizerV2.cpp
|
C++
|
agpl-3.0
| 44,661
|
/**
* FILENAME: LocalizerV2
* DESCRIPTION: main 6D localization algorithm
* AUTHOR: Miguel Abreu (m.abreu@fe.up.pt)
* DATE: 2021
*
* ===================================================================================
* WORKFLOW
* ===================================================================================
*
* References can be obtained from:
* - landmarks (which are identified by the server and can be corner flags or goal posts)
* - line segments (which are always on the ground) (hereinafter referred to as lines)
* - feet contact points (we are assuming the contact point is on the ground plane)
*
* WARNING:
* When partial information is available, it is used to change the head position (e.g. translation in x/y/z may be updated
* without visual information). However, the transformation matrix (including the translation) are not changed, because
* this would affect the perceived position of previously seen objects. For this reason, the worldstate should not rely
* on the head position to convert relative to absolute coordinates. Instead, it should only use transformation matrix,
* or internal conversion methods. The head position can still be used for other purposes, such as machine learning.
*
* -----------------------------------------------------------------------------------
* 0. Perform basic tasks and checks
*
* Excluded scenarios:
* 0 landmarks & <2 lines - it's a requirement from step 1
* 0 lines - step 1 allows this if there are 3 ground refs but the only marginally common
* option would be 2 feet and a corner (which is undesirable)
*
* -----------------------------------------------------------------------------------
* 1. Find the Z axis orientation vector (and add it to the preliminary transformation matrix):
* 1.1. there are >= 3 noncollinear ground references (z=0)
* ASSUMPTION: the ground references are not collinear. Why?
* If we see 2 lines their endpoints are never collinear.
* If we see one and we are on top of it, the feet contact points can cause collinearity but it is very rare.
* SOLUTION: Find the best fitting ground plane's normal vector using Singular Value Decomposition
*
* 1.2. there are < 3 noncollinear ground references (z=0)
*
* Possible combinations:
* If there is 1 corner flag, either we have >= 3 ground references, or it is impossible.
* So, below, we assume there are 0 corner flags.
*
* | 0 lines + 0/1/2 feet | 1 line + 0 feet |
* -------------|------------------------|------------------|
* 0 goalposts | ----- | ----- | (Only 1 line: there is no way of identifying it)
* 1 goalpost | ----- | A,C | (1 goalpost and 0/1/2 feet: infinite solutions)
* 2 goalposts | * | B,C |
*
*
* If it sees 1 or 2 goalposts and only 1 line, we assume for A & B that it is the endline (aka goal line)
*
* SOLUTIONS:
* 1.2.A. IF IT IS THE GOALLINE. Find the line's nearest point (p) to the goalpost (g), Zvec = (g-p) / |Zvec|
* 1.2.B. IF IT IS THE GOALLINE. Find the line's nearest point (p) to the goalposts (g1,g2) midpoint (m), Zvec = (m-p) / |Zvec|
* (This solution is more accurate than using only 1 goalpost. Even m is more accurate, on average, than g1 or g2.)
* 1.2.C. IF IT IS NOT THE GOALLINE. There are 3 options:
* I - There are 2 goalposts (crossbar line) and an orthogonal line:
* Zvec = crossbar x line (or) line x crossbar (depending of the direction of both vectors)
* II - Other situation if the z translation coordinate was externally provided through machine learning:
* Find any horizontal line (e.g. line between 2 goalposts, or ground line)
* Let M be any mark with known absolute z, and let Z be the externally provided z coordinate:
* Find Zvec such that (HorLine.Zvec=0) and (Zvec.Mrel=Mabsz-Z)
* III - If z was not provided:
* Skip to last step.
* 1.2.*. This scenario was tested and it is not valid. In certain body positions there are two solutions, and even though
* one is correct and generally yields lower error, the other one is a local optimum outside the field. One could
* exclude the out-of-field solution with some mildly expensive modifications to the optimization's error function,
* but the out-of-field scenario is not unrealistic, so this is not the way. Adding an external z source could help
* increasing the error of the wrong local optimum, but it may not be enough. Another shortcoming of this scenario is
* when we see the goalposts from the opposite goal, creating large visual error.
*
* 1.3. impossible / not implemented: in this case skip to last step
*
* -----------------------------------------------------------------------------------
* 2. Compute z:
*
* Here's what we know about the transformation matrix so far:
* | - | - | - | - |
* | - | - | - | - |
* | zx | zy | zz | ? | We want to know the translation in z
* | 0 | 0 | 0 | 1 |
*
* Given a random point (p) with known relative coordinates and known absolute z coordinate,
* we can find the translation in z:
* p.relx * zx + p.rely * zy + p.relz * zz + ? = p.absz
*
* If we do this for every point, we can then average z
*
* -----------------------------------------------------------------------------------
* 3. Compute a rough estimate for entire transformation (2 first rows):
*
* Solution quality for possible combinations:
*
* short line (length < 0.5m) *hard to detect orientation, *generated displacement error is insuficient for optimization
* long line (length >= 0.5m)
*
* | 0 landmarks | 1 goalpost | 1 corner | >= 2 landmarks |
* -----------------|---------------|--------------|------------|----------------|
* 0 long lines | --- | --- | --- | A |
* 1 long line | --- | B+ | B | A |
* 2 long lines | B | B+ | B++ | A |
*
* SOLUTIONS:
* A - the solution is unique
* STEPS:
* - Compute the X-axis and Y-axis orientation from 2 landmarks
* - Average the translation for every visible landmark
* - Fine-tune XY translation/rotation
* B - there is more than 1 solution, so we use the last known position as reference
* Minimum Requirements:
* - longest line must be >1.6m so that we can extract the orientation (hor/ver) while not being mistaken for a ring line
* - there is exactly 1 plausible solution
* Notes:
* B+ (the solution is rarely unique)
* B++ (the solution is virtually unique but cannot be computed with the algorithm for A scenarios)
* STEPS:
* - Find 4 possible orientations based on the longest line (which should be either aligned with X or Y)
* - Determine reasonable initial translation:
* - If the agent sees 1 landmark: compute XY translation for each of the 4 orientations based on that 1 landmark
* - If the agent sees 0 landmarks: use last known position
* Note: Why not use always last known position if that is a criterion in the end?
* Because in this stage it would only delay the optimization.
* - Optimize the X/Y translation for every possible orientation
* - Perform quality assessment
* Plausible solution if:
* - Optimization converged to local minimum
* - Distance to last known position <50cm (applicable if no visible landmarks)
* - Mapping error <0.12m/point
* - Given the agent's FOV, inverse mapping error <0.2m/point (disabled in V2)
* NOTE: If there is 1 landmark, plausibility is defined by mapping errors, not distance to last known pos. So if
* one guess has the only acceptable mapping error, but is the farthest from previous position, it is still chosen.
* However, if >1 guess has acceptable mapping error, the 0.5m threshold is used to eliminate candidates.
* Likely if:
* - Plausible
* - Distance to last known position <30cm (applicable if no visible landmarks)
* - Mapping error <0.06m/point
* - Given the agent's FOV, inverse mapping error <0.1m/point (not currently active)
* - Choose likely solution if all others are not even plausible
* - Fine-tune XY translation/rotation
*
* -----------------------------------------------------------------------------------
* 4. Identify visible elements and perform 2nd fine tune based on distance probabilites
*
* -----------------------------------------------------------------------------------
* Last step. Analyze preliminary transformation matrix to update final matrices
*
* For the reasons stated in the beginning (see warning), if the preliminary matrix was not entirely set, the
* actual transformation matrix will not be changed. But the head position will always reflect the latest changes.
*
*
* ===================================================================================
* LOCALIZATION BASED ON PROBABILITY DENSITIES
* ===================================================================================
* ================================PROBABILITY DENSITY================================
*
* For 1 distance measurement from RCSSSERVER3D:
*
* Error E = d/100 * A~N(0,0.0965^2) + B~U(-0.005,0.005)
* PDF[d/100 * A](w) = PDF[N(0,(d/100 * 0.0965)^2)](w)
* PDF[E](w) = PDF[N(0,(d/100 * 0.0965)^2)](w) convoluted with PDF[U(-0.005,0.005)](w)
*
* where d is the distance from a given [p]oint (px,py,pz) to the [o]bject (ox,oy,oz)
* and w is the [m]easurement error: w = d-measurement = sqrt((px-ox)^2+(py-oy)^2+(pz-oz)^2) - measurement
*
* PDF[E](w) -> PDF[E](p,o,m)
* ---------------------------------------------------------------
* For n independent measurements:
*
* PDF[En](p,on,mn) = PDF[E1](p,o1,m1) * PDF[E2](p,o2,m2) * PDF[E3](p,o3,m3) * ...
* ---------------------------------------------------------------
* Adding z estimation:
*
* PDF[zE](wz) = PDF[N(mean,std^2)](wz)
* where wz is the zError = estz - pz
*
* PDF[zE](wz) -> PDF[zE](pz,estz)
* PDF[En](p,on,mn,estz) = PDF[En](p,on,mn) * PDF[zE](pz,estz)
* ===================================================================================
* =====================================GRADIENT======================================
*
* Grad(PDF[En](p,on,mn,estz)) wrt p = Grad( PDF[E1](p,o1,m1) * ... * PDF[E2](p,on,mn) * PDF[zE](pz,estz)) wrt {px,py,pz}
*
* Generalizing the product rule for n factors, we have:
*
* Grad(PDF[En](p,on,mn)) wrt p = sum(gradPDF[Ei] / PDF[Ei]) * prod(PDF[Ei])
* Grad(PDF[En](p,on,mn)) wrt p = sum(gradPDF[Ei] / PDF[Ei]) * PDF[En](p,on,mn)
*
* note that: gradPDF[zE](pz,estz) wrt {px,py,pz} = {0,0,d/d_pz}
* ===================================================================================
* */
#pragma once
#include "Singleton.h"
#include "Field.h"
#include "Matrix4D.h"
#include "FieldNoise.h"
#include <gsl/gsl_multifit.h> //Linear least-squares fitting
#include <gsl/gsl_linalg.h> //Singular value decomposition
#include <gsl/gsl_multimin.h> //Multidimensional minimization
class LocalizerV2 {
friend class Singleton<LocalizerV2>;
public:
/**
* Compute 3D position and 3D orientation
* sets "is_uptodate" to true if there is new information available (rotation+translation)
* If no new information is available, the last known position is provided
*/
void run();
/**
* Print report (average errors + solution analysis)
*/
void print_report() const;
/**
* Transformation matrices
* They are initialized as 4x4 identity matrices
*/
const Matrix4D &headTofieldTransform = final_headTofieldTransform; // rotation + translation
const Matrix4D &headTofieldRotate = final_headTofieldRotate; // rotation
const Matrix4D &fieldToheadTransform = final_fieldToheadTransform; // rotation + translation
const Matrix4D &fieldToheadRotate = final_fieldToheadRotate; // rotation
/**
* Head position
* translation part of headTofieldTransform
*/
const Vector3f &head_position = final_translation;
/**
* True if head_position and the transformation matrices are up to date
* (false if this is not a visual step, or not enough elements are visible)
*/
const bool &is_uptodate = _is_uptodate;
/**
* Number of simulation steps since last update (see is_uptodate)
* If (is_uptodate==true) then "steps_since_last_update" is zero
*/
const unsigned int &steps_since_last_update = _steps_since_last_update;
/**
* Head z coordinate
* This variable is often equivalent to head_position.z, but sometimes it differs.
* There are situations in which the rotation and translation cannot be computed,
* but the z-coordinate can still be found through vision.
* It should be used in applications which rely on z as an independent coordinate,
* such as detecting if the robot has fallen, or as machine learning observations.
* It should not be used for 3D transformations.
*/
const float &head_z = final_z;
/**
* Since head_z can be computed in situations where self-location is impossible,
* this variable is set to True when head_z is up to date
*/
const bool &is_head_z_uptodate = _is_head_z_uptodate;
/**
* Transform relative to absolute coordinates using headTofieldTransform
* @return absolute coordinates
*/
Vector3f relativeToAbsoluteCoordinates(const Vector3f relativeCoordinates) const;
/**
* Transform absolute to relative coordinates using fieldToheadTransform
* @return relative coordinates
*/
Vector3f absoluteToRelativeCoordinates(const Vector3f absoluteCoordinates) const;
/**
* Get 3D velocity (based on last n 3D positions)
* @param n number of last positions to evaluate (min 1, max 9)
* Example for n=3:
* current position: p0 (current time step)
* last position: p1 (typically* 3 time steps ago)
* position before: p2 (typically* 6 time steps ago)
* position before: p3 (typically* 9 time steps ago)
* RETURN value: p0-p3
* *Note: number of actual time steps depends on server configuration and whether
* the agent was able to self-locate on the last n visual steps
* @return 3D velocity vector
*/
Vector3f get_velocity(unsigned int n) const;
/**
* Get last known head z coordinate
* Note: this variable is based on head_z. It can be used as an independent coordinate,
* but it should not be used for 3D transformations, as it may be out of sync with
* the x and y coordinates. (see head_z)
* @return last known head z coordinate
*/
float get_last_head_z() const {return last_z;}
private:
//=================================================================================================
//============================================================================ main private methods
//=================================================================================================
bool find_z_axis_orient_vec(); //returns true if successful
void fit_ground_plane();
void find_z(const Vector3f& Zvec);
bool find_xy();
bool guess_xy();
bool fine_tune_aux(float &initial_angle, float &initial_x, float &initial_y, bool use_probabilities);
bool fine_tune(float initial_angle, float initial_x, float initial_y);
static double map_error_logprob(const gsl_vector *v, void *params);
static double map_error_2d(const gsl_vector *v, void *params);
void commit_everything();
//=================================================================================================
//=================================================================== private transformation matrix
//=================================================================================================
//PRELIMINARY MATRIX - where all operations are stored
//if the algorithm is not successful, the final matrix is not modified
void prelim_reset();
Matrix4D prelimHeadToField = Matrix4D(); //initialized as identity matrix
//FINAL MATRIX - the user has access to a public const reference of the variables below
Matrix4D final_headTofieldTransform; // rotation + translation
Matrix4D final_headTofieldRotate; // rotation
Matrix4D final_fieldToheadTransform; // rotation + translation
Matrix4D final_fieldToheadRotate; // rotation
Vector3f final_translation; //translation
float final_z; //independent z translation (may be updated more often)
//=================================================================================================
//=============================================================================== useful statistics
//=================================================================================================
std::array<Vector3f, 10> position_history;
unsigned int position_history_ptr = 0;
float last_z = 0.5;
unsigned int _steps_since_last_update = 0;
bool _is_uptodate = false;
bool _is_head_z_uptodate = false;
//=================================================================================================
//================================================================================ debug statistics
//=================================================================================================
int stats_sample_position_error(const Vector3f sample, const Vector3f& cheat, double error_placeholder[]);
void stats_reset();
double errorSum_fineTune_before[7] = {0}; //[0,1,2]- xyz err sum, [3]-2D err sum, [4]-2D err sq sum, [5]-3D err sum, [6]-3D err sq sum
double errorSum_fineTune_euclidianDist[7] = {0}; //[0,1,2]- xyz err sum, [3]-2D err sum, [4]-2D err sq sum, [5]-3D err sum, [6]-3D err sq sum
double errorSum_fineTune_probabilistic[7] = {0}; //[0,1,2]- xyz err sum, [3]-2D err sum, [4]-2D err sq sum, [5]-3D err sum, [6]-3D err sq sum
double errorSum_ball[7] = {0}; //[0,1,2]- xyz err sum, [3]-2D err sum, [4]-2D err sq sum, [5]-3D err sum, [6]-3D err sq sum
int counter_fineTune = 0;
int counter_ball = 0;
enum STATE{NONE, RUNNING, MINFAIL, BLIND, FAILzNOgoal, FAILzLine, FAILz, FAILtune, FAILguessLine, FAILguessNone, FAILguessMany, FAILguessTest, DONE, ENUMSIZE};
STATE state = NONE;
void stats_change_state(enum STATE s);
int state_counter[STATE::ENUMSIZE] = {0};
};
typedef Singleton<LocalizerV2> SLocalizerV2;
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/LocalizerV2.h
|
C++
|
agpl-3.0
| 19,470
|
src = $(wildcard *.cpp)
obj = $(src:.c=.o)
LDFLAGS = -lgsl -lgslcblas
CFLAGS = -O3 -shared -std=c++11 -fPIC -Wall $(PYBIND_INCLUDES)
all: $(obj)
g++ $(CFLAGS) -o localization.so $^ $(LDFLAGS)
debug: $(filter-out lib_main.cpp,$(obj))
g++ -O0 -std=c++14 -Wall -g -o debug.bin debug_main.cc $^ $(LDFLAGS)
.PHONY: clean
clean:
rm -f $(obj) all
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/Makefile
|
Makefile
|
agpl-3.0
| 348
|
#include "Matrix4D.h"
Matrix4D::Matrix4D() {
// identity matrix
const float tmp[M_LENGTH] = {
1, 0, 0, 0,
0, 1, 0, 0,
0, 0, 1, 0,
0, 0, 0, 1
};
for (int i = 0; i < M_LENGTH; i++)
content[i] = tmp[i];
}
Matrix4D::Matrix4D(const float a[M_LENGTH]) {
// creates a matrix using a vector of floats
for (int i = 0; i < M_LENGTH; i++)
content[i] = a[i];
}
Matrix4D::Matrix4D(const Matrix4D& other) {
// creates a matrix using another matrix
for (int i = 0; i < M_LENGTH; i++)
content[i] = other.content[i];
}
Matrix4D::Matrix4D(const Vector3f& v) {
float x = v.getX();
float y = v.getY();
float z = v.getZ();
// gets a translation matrix from xyz coordinates
const float tmp[M_LENGTH] = {
1, 0, 0, x,
0, 1, 0, y,
0, 0, 1, z,
0, 0, 0, 1
};
for (int i = 0; i < M_LENGTH; i++)
content[i] = tmp[i];
}
Matrix4D::~Matrix4D() {
// nothing to do
}
void Matrix4D::set(unsigned i, float value) {
content[i] = value;
}
void Matrix4D::set(unsigned i, unsigned j, float value) {
content[M_COLS * i + j] = value;
}
float Matrix4D::get(unsigned i) const{
return content[i];
}
float Matrix4D::get(unsigned i, unsigned j) const{
return content[M_COLS * i + j];
}
Matrix4D Matrix4D::operator+(const Matrix4D& other) const {
// sums two matrices
float tmp[M_LENGTH];
for (int i = 0; i < M_LENGTH; i++)
tmp[i] = this->content[i] + other.content[i];
return Matrix4D(tmp);
}
Matrix4D Matrix4D::operator-(const Matrix4D& other) const {
// subtracts a matrix from another
float tmp[M_LENGTH];
for (int i = 0; i < M_LENGTH; i++)
tmp[i] = this->content[i] - other.content[i];
return Matrix4D(tmp);
}
Matrix4D Matrix4D::operator*(const Matrix4D& other) const {
// multiplies two matrices
float tmp[M_LENGTH];
for (int i = 0; i < M_ROWS; i++) {
for (int j = 0; j < M_COLS; j++) {
tmp[M_COLS * i + j] = 0;
for (int k = 0; k < M_COLS; k++)
tmp[M_COLS * i + j] += this->content[M_COLS * i + k] * other.content[M_COLS * k + j];
}
}
return Matrix4D(tmp);
}
Vector3f Matrix4D::operator*(const Vector3f& vec) const {
// multiplies this matrix by a vector of four floats
// the first three are from vec and the remaining float is 1.0
float x = 0;
float y = 0;
float z = 0;
x = this->content[0] * vec.getX();
x += this->content[1] * vec.getY();
x += this->content[2] * vec.getZ();
x += this->content[3];
y = this->content[4] * vec.getX();
y += this->content[5] * vec.getY();
y += this->content[6] * vec.getZ();
y += this->content[7];
z = this->content[8] * vec.getX();
z += this->content[9] * vec.getY();
z += this->content[10] * vec.getZ();
z += this->content[11];
return Vector3f(x, y, z);
}
void Matrix4D::operator=(const Matrix4D& other) {
// assigns another matrix to this one
for (int i = 0; i < M_LENGTH; i++)
content[i] = other.content[i];
}
bool Matrix4D::operator==(const Matrix4D& other) const {
// checks whether this matrix is equal to another
for (int i = 0; i < M_LENGTH; i++)
if (content[i] != other.content[i])
return false;
return true;
}
void Matrix4D::operator+=(const Matrix4D& other) {
// sums this matrix to another and returns the result
for (int i = 0; i < M_LENGTH; i++)
content[i] += other.content[i];
}
void Matrix4D::operator-=(const Matrix4D& other) {
// subtracts this matrix from another and returns the result
for (int i = 0; i < M_LENGTH; i++)
content[i] -= other.content[i];
}
float& Matrix4D::operator[](const unsigned pos) {
// gets a value from position
return content[pos];
}
Vector3f Matrix4D::toVector3f() const {
// gets the translation vector from the matrix
float x = get(0, M_COLS - 1);
float y = get(1, M_COLS - 1);
float z = get(2, M_COLS - 1);
return Vector3f(x, y, z);
}
Matrix4D Matrix4D::transpose() {
// returns the transpose of this matrix
Matrix4D result;
for (int i = 0; i < M_ROWS; i++)
for (int j = 0; j < M_COLS; j++)
result.set(j, i, get(i, j));
return result;
}
Matrix4D Matrix4D::inverse_tranformation_matrix() const {
Matrix4D inv; //Initialized as identity matrix
inverse_tranformation_matrix(inv);
return inv;
}
void Matrix4D::inverse_tranformation_matrix(Matrix4D& inv) const {
//Rotation
inv[0] = content[0]; inv[1] = content[4]; inv[2] = content[8];
inv[4] = content[1]; inv[5] = content[5]; inv[6] = content[9];
inv[8] = content[2]; inv[9] = content[6]; inv[10] = content[10];
//Translation
inv[3] = -content[0]*content[3] - content[4]*content[7] - content[8]*content[11];
inv[7] = -content[1]*content[3] - content[5]*content[7] - content[9]*content[11];
inv[11] = -content[2]*content[3] - content[6]*content[7] - content[10]*content[11];
}
bool Matrix4D::inverse(Matrix4D& inverse_out) const{
// returns the inverse of this matrix
float inv[16], det;
const float* m = content;
int i;
inv[0] = m[5] * m[10] * m[15] - m[5] * m[11] * m[14] - m[9] * m[6] * m[15] + m[9] * m[7] * m[14] + m[13] * m[6] * m[11] - m[13] * m[7] * m[10];
inv[4] = -m[4] * m[10] * m[15] + m[4] * m[11] * m[14] + m[8] * m[6] * m[15] - m[8] * m[7] * m[14] - m[12] * m[6] * m[11] + m[12] * m[7] * m[10];
inv[8] = m[4] * m[9] * m[15] - m[4] * m[11] * m[13] - m[8] * m[5] * m[15] + m[8] * m[7] * m[13] + m[12] * m[5] * m[11] - m[12] * m[7] * m[9];
inv[12] = -m[4] * m[9] * m[14] + m[4] * m[10] * m[13] + m[8] * m[5] * m[14] - m[8] * m[6] * m[13] - m[12] * m[5] * m[10] + m[12] * m[6] * m[9];
inv[1] = -m[1] * m[10] * m[15] + m[1] * m[11] * m[14] + m[9] * m[2] * m[15] - m[9] * m[3] * m[14] - m[13] * m[2] * m[11] + m[13] * m[3] * m[10];
inv[5] = m[0] * m[10] * m[15] - m[0] * m[11] * m[14] - m[8] * m[2] * m[15] + m[8] * m[3] * m[14] + m[12] * m[2] * m[11] - m[12] * m[3] * m[10];
inv[9] = -m[0] * m[9] * m[15] + m[0] * m[11] * m[13] + m[8] * m[1] * m[15] - m[8] * m[3] * m[13] - m[12] * m[1] * m[11] + m[12] * m[3] * m[9];
inv[13] = m[0] * m[9] * m[14] - m[0] * m[10] * m[13] - m[8] * m[1] * m[14] + m[8] * m[2] * m[13] + m[12] * m[1] * m[10] - m[12] * m[2] * m[9];
inv[2] = m[1] * m[6] * m[15] - m[1] * m[7] * m[14] - m[5] * m[2] * m[15] + m[5] * m[3] * m[14] + m[13] * m[2] * m[7] - m[13] * m[3] * m[6];
inv[6] = -m[0] * m[6] * m[15] + m[0] * m[7] * m[14] + m[4] * m[2] * m[15] - m[4] * m[3] * m[14] - m[12] * m[2] * m[7] + m[12] * m[3] * m[6];
inv[10] = m[0] * m[5] * m[15] - m[0] * m[7] * m[13] - m[4] * m[1] * m[15] + m[4] * m[3] * m[13] + m[12] * m[1] * m[7] - m[12] * m[3] * m[5];
inv[14] = -m[0] * m[5] * m[14] + m[0] * m[6] * m[13] + m[4] * m[1] * m[14] - m[4] * m[2] * m[13] - m[12] * m[1] * m[6] + m[12] * m[2] * m[5];
inv[3] = -m[1] * m[6] * m[11] + m[1] * m[7] * m[10] + m[5] * m[2] * m[11] - m[5] * m[3] * m[10] - m[9] * m[2] * m[7] + m[9] * m[3] * m[6];
inv[7] = m[0] * m[6] * m[11] - m[0] * m[7] * m[10] - m[4] * m[2] * m[11] + m[4] * m[3] * m[10] + m[8] * m[2] * m[7] - m[8] * m[3] * m[6];
inv[11] = -m[0] * m[5] * m[11] + m[0] * m[7] * m[9] + m[4] * m[1] * m[11] - m[4] * m[3] * m[9] - m[8] * m[1] * m[7] + m[8] * m[3] * m[5];
inv[15] = m[0] * m[5] * m[10] - m[0] * m[6] * m[9] - m[4] * m[1] * m[10] + m[4] * m[2] * m[9] + m[8] * m[1] * m[6] - m[8] * m[2] * m[5];
det = m[0] * inv[0] + m[1] * inv[4] + m[2] * inv[8] + m[3] * inv[12];
if (det == 0)
return false;
det = 1.0 / det;
for (i = 0; i < 16; i++)
inverse_out.set(i, inv[i] * det);
return true;
}
Matrix4D Matrix4D::rotationX(float angle) {
// rotates this matrix around the x-axis
const float tmp[M_LENGTH] = {
1, 0, 0, 0,
0, Cos(angle), -Sin(angle), 0,
0, Sin(angle), Cos(angle), 0,
0, 0, 0, 1
};
return Matrix4D(tmp);
}
Matrix4D Matrix4D::rotationY(float angle) {
// rotates this matrix around the y-axis
const float tmp[M_LENGTH] = {
Cos(angle), 0, Sin(angle), 0,
0, 1, 0, 0,
-Sin(angle), 0, Cos(angle), 0,
0, 0, 0, 1
};
return Matrix4D(tmp);
}
Matrix4D Matrix4D::rotationZ(float angle) {
// rotates this matrix around the z axis
const float tmp[M_LENGTH] = {
Cos(angle), -Sin(angle), 0, 0,
Sin(angle), Cos(angle), 0, 0,
0, 0, 1, 0,
0, 0, 0, 1
};
return Matrix4D(tmp);
}
Matrix4D Matrix4D::rotation(Vector3f axis, float angle) {
// assuming that axis is a unit vector
float x = axis.getX();
float y = axis.getY();
float z = axis.getZ();
const float tmp[M_LENGTH] = {
(x * x * (1 - Cos(angle)) + Cos(angle)), (x * y * (1 - Cos(angle)) - z * Sin(angle)), (x * z * (1 - Cos(angle)) + y * Sin(angle)), 0,
(x * y * (1 - Cos(angle)) + z * Sin(angle)), (y * y * (1 - Cos(angle)) + Cos(angle)), (y * z * (1 - Cos(angle)) - x * Sin(angle)), 0,
(x * z * (1 - Cos(angle)) - y * Sin(angle)), (y * z * (1 - Cos(angle)) + x * Sin(angle)), (z * z * (1 - Cos(angle)) + Cos(angle)), 0,
0, 0, 0, 1
};
return Matrix4D(tmp);
}
Matrix4D Matrix4D::translation(float x, float y, float z) {
// gets a translation matrix from xyz coordinates
const float tmp[M_LENGTH] = {
1, 0, 0, x,
0, 1, 0, y,
0, 0, 1, z,
0, 0, 0, 1
};
return Matrix4D(tmp);
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/Matrix4D.cpp
|
C++
|
agpl-3.0
| 9,664
|
#ifndef MATRIX4D_H_
#define MATRIX4D_H_
#include "Vector3f.h"
#define M_ROWS 4
#define M_COLS 4
#define M_LENGTH (M_ROWS * M_COLS)
/**
* @class Matrix4D
*
* This class represents a 4x4 matrix and contains methods
* to operate on it
*
* @author Nuno Almeida (nuno.alm@ua.pt)
* Adapted - Miguel Abreu
*/
class Matrix4D {
public:
float content[M_LENGTH]; // content of the matrix, vector-like
/**
* Default constructor returns the identity matrix
*/
Matrix4D();
/**
* Constructor returns a matrix from a vector of floats
*/
Matrix4D(const float[]);
/**
* Copy constructor
*/
Matrix4D(const Matrix4D& other);
/**
* Constructor returns a translation matrix
*/
Matrix4D(const Vector3f& v);
/**
* Destructor
*/
~Matrix4D();
/**
* Sets a value in some position (vector-like)
*/
void set(unsigned i, float value);
/**
* Sets a value in some position (matrix-like)
*/
void set(unsigned i, unsigned j, float value);
/**
* Gets a value from some position (vector-like)
*/
float get(unsigned i) const;
/**
* Gets a value from some position (matrix-like)
*/
float get(unsigned i, unsigned j) const;
/**
* Assigns another matrix to this one
*
* @param other Another matrix
*/
void operator=(const Matrix4D& other);
/**
* Gets the sum of another vector with this one
*
* @param other Another matrix
*/
Matrix4D operator+(const Matrix4D& other) const;
/**
* Sums this matrix to another
*
* @param other Another matrix
* @return Sum of this matrix with another
*/
void operator+=(const Matrix4D& other);
/**
* Gets the subtraction of another vector from this one
*
* @param other Another matrix
*/
Matrix4D operator-(const Matrix4D&) const;
/**
* Subtracts another matrix from this one
*
* @param other Another matrix
* @return This matrix minus another
*/
void operator-=(const Matrix4D& other);
/**
* Multiplies two matrices
*
* @param other Another matrix
* @return Multiplication matrix
*/
Matrix4D operator*(const Matrix4D& other) const;
/**
* Multiplies a matrix with a vector
*
* @param other Another matrix
* @return Multiplication vector
*/
Vector3f operator*(const Vector3f& other) const;
/**
* Checks whether this matrix is equal to another
*
* @param other Another matrix
* @return true/false
*/
bool operator==(const Matrix4D&) const;
/**
* Gets the content of the position i (in vector representation) of this
* matrix
*
* @param pos Position
* @return Value in the position
*/
float& operator[](const unsigned pos);
/**
* Gets the translation vector from this matrix
*
* @return Translation vector
*/
Vector3f toVector3f() const;
/**
* Gets the transpose of this matrix
*
* @return Transpose
*/
Matrix4D transpose();
/**
* Gets the inverse of this matrix (m.abreu@2020)
*
* @param inverse_out inverse matrix
* @return true if it exists
*/
bool inverse(Matrix4D& inverse_out) const;
/**
* Gets the inverse of this matrix, (m.abreu@2020)
* assuming that it represents an affine transformation with only translation and rotation
* This method creates a new matrix
*
* @return inverse matrix
*/
Matrix4D inverse_tranformation_matrix() const;
/**
* Gets the inverse of this matrix, (m.abreu@2020)
* assuming that it represents an affine transformation with only translation and rotation
* This method overwrites the given matrix
*
* @param inverse_out inverse matrix output
*/
void inverse_tranformation_matrix(Matrix4D& inverse_out) const;
/**
* Gets the rotation matrix around x-axis
*
* @param angle Angle (degrees)
*/
static Matrix4D rotationX(float angle);
/**
* Gets the rotation matrix around y-axis
*
* @param angle Angle (degrees)
*/
static Matrix4D rotationY(float angle);
/**
* Gets the rotation matrix around z-axis
*
* @param angle Angle (degrees)
*/
static Matrix4D rotationZ(float angle);
/**
* Gets the rotation matrix around an arbitrary axis
*
* @param axis Axis (x, y and z)
* @param angle Angle (degrees)
*/
static Matrix4D rotation(Vector3f axis, float angle);
/**
* Gets the translation matrix
*
* @param x x-axis coordinate
* @param y y-axis coordinate
* @param z z-axis coordinate
*/
static Matrix4D translation(Vector3f v) {
return translation(v.getX(), v.getY(), v.getZ());
}
static Matrix4D translation(float x, float y, float z);
};
#endif // MATRIX4D_H_
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/Matrix4D.h
|
C++
|
agpl-3.0
| 5,024
|
#include <iostream>
#include "RobovizLogger.h"
#include "robovizdraw.h"
#define ROBOVIZ_HOST "localhost"
#define ROBOVIZ_PORT "32769"
RobovizLogger* RobovizLogger::Instance() {
static RobovizLogger instance;
return &instance;
}
RobovizLogger::RobovizLogger() {}
RobovizLogger::~RobovizLogger() {
destroy();
}
int RobovizLogger::init() {
if (is_initialized) return 0;
struct addrinfo hints;
int rv;
int numbytes;
memset(&hints, 0, sizeof hints);
hints.ai_family = AF_UNSPEC;
hints.ai_socktype = SOCK_DGRAM;
if ((rv = getaddrinfo(ROBOVIZ_HOST, ROBOVIZ_PORT, &hints, &servinfo)) != 0) {
//fprintf(stderr, "getaddrinfo: %s\n", gai_strerror(rv));
return 1;
}
// loop through all the results and make a socket
for (p = servinfo; p != NULL; p = p->ai_next) {
if ((sockfd = socket(p->ai_family, p->ai_socktype,
p->ai_protocol)) == -1) {
perror("socket");
continue;
}
break;
}
if (p == NULL) {
return 2;
}
is_initialized = true;
return 0;
}
void RobovizLogger::destroy() {
freeaddrinfo(servinfo);
servinfo=NULL;
close(sockfd);
}
void RobovizLogger::swapBuffers(const string* setName) {
int bufSize = -1;
unsigned char* buf = newBufferSwap(setName, &bufSize);
sendto(sockfd, buf, bufSize, 0, p->ai_addr, p->ai_addrlen);
delete[] buf;
}
void RobovizLogger::drawLine(float x1, float y1, float z1, float x2, float y2, float z2,
float thickness, float r, float g, float b, const string* setName) {
float pa[3] = {x1, y1, z1};
float pb[3] = {x2, y2, z2};
float color[3] = {r, g, b};
int bufSize = -1;
unsigned char* buf = newLine(pa, pb, thickness, color, setName, &bufSize);
sendto(sockfd, buf, bufSize, 0, p->ai_addr, p->ai_addrlen);
delete[] buf;
}
void RobovizLogger::drawCircle(float x, float y, float radius, float thickness,
float r, float g, float b, const string* setName) {
float center[2] = {x, y};
float color[3] = {r, g, b};
int bufSize = -1;
unsigned char* buf = newCircle(center, radius, thickness, color, setName, &bufSize);
sendto(sockfd, buf, bufSize, 0, p->ai_addr, p->ai_addrlen);
delete[] buf;
}
void RobovizLogger::drawSphere(float x, float y, float z, float radius,
float r, float g, float b, const string* setName) {
float center[3] = {x, y, z};
float color[3] = {r, g, b};
int bufSize = -1;
unsigned char* buf = newSphere(center, radius, color, setName, &bufSize);
sendto(sockfd, buf, bufSize, 0, p->ai_addr, p->ai_addrlen);
delete[] buf;
}
void RobovizLogger::drawPoint(float x, float y, float z, float size,
float r, float g, float b, const string* setName) {
float center[3] = {x, y, z};
float color[3] = {r, g, b};
int bufSize = -1;
unsigned char* buf = newPoint(center, size, color, setName, &bufSize);
sendto(sockfd, buf, bufSize, 0, p->ai_addr, p->ai_addrlen);
delete[] buf;
}
void RobovizLogger::drawPolygon(const float* v, int numVerts, float r, float g, float b,
float a, const string* setName) {
float color[4] = {r, g, b, a};
int bufSize = -1;
unsigned char* buf = newPolygon(v, numVerts, color, setName, &bufSize);
sendto(sockfd, buf, bufSize, 0, p->ai_addr, p->ai_addrlen);
delete[] buf;
}
void RobovizLogger::drawAnnotation(const string* text, float x, float y, float z, float r,
float g, float b, const string* setName) {
float color[3] = {r, g, b};
float pos[3] = {x, y, z};
int bufSize = -1;
unsigned char* buf = newAnnotation(text, pos, color, setName, &bufSize);
sendto(sockfd, buf, bufSize, 0, p->ai_addr, p->ai_addrlen);
delete[] buf;
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/RobovizLogger.cpp
|
C++
|
agpl-3.0
| 3,779
|
/*
* RobovizLogger.h
*
* Created on: 2013/06/24
* Author: Rui Ferreira
*/
#ifndef _ROBOVIZLOGGER_H_
#define _ROBOVIZLOGGER_H_
#define RobovizLoggerInstance RobovizLogger::Instance()
#include <stdlib.h>
#include <unistd.h>
#include <errno.h>
#include <string>
#include <sys/types.h>
#include <sys/socket.h>
#include <netinet/in.h>
#include <arpa/inet.h>
#include <netdb.h>
#include <math.h>
class RobovizLogger {
private:
RobovizLogger();
RobovizLogger(const RobovizLogger&);
RobovizLogger& operator=(const RobovizLogger&);
virtual ~RobovizLogger();
bool is_initialized = false;
public:
static RobovizLogger* Instance();
int init();
void destroy();
void swapBuffers(const std::string* setName);
void drawLine(float x1, float y1, float z1, float x2, float y2, float z2,
float thickness, float r, float g, float b, const std::string* setName);
void drawCircle(float x, float y, float radius, float thickness,
float r, float g, float b, const std::string* setName);
void drawSphere(float x, float y, float z, float radius,
float r, float g, float b, const std::string* setName);
void drawPoint(float x, float y, float z, float size,
float r, float g, float b, const std::string* setName);
void drawPolygon(const float* v, int numVerts, float r, float g, float b,
float a, const std::string* setName);
void drawAnnotation(const std::string* text, float x, float y, float z, float r,
float g, float b, const std::string* setName);
private:
int sockfd;
struct addrinfo* p;
struct addrinfo* servinfo;
};
#endif // _ROBOVIZLOGGER_H_
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/RobovizLogger.h
|
C++
|
agpl-3.0
| 1,682
|
#ifndef SINGLETON_H
#define SINGLETON_H
template <class T>
class Singleton {
public:
static T& getInstance() {
static T instance;
return instance;
}
private:
Singleton();
~Singleton();
Singleton(Singleton const&);
Singleton& operator=(Singleton const&);
};
#endif // SINGLETON_H
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/Singleton.h
|
C++
|
agpl-3.0
| 325
|
#include "Vector3f.h"
using namespace std;
Vector3f::Vector3f() {
x = 0.0;
y = 0.0;
z = 0.0;
}
Vector3f::Vector3f(float x, float y, float z) {
this->x = x;
this->y = y;
this->z = z;
}
Vector3f::Vector3f(const Vector3f& other) {
x = other.x;
y = other.y;
z = other.z;
}
Vector3f::Vector3f(const Vector& other) {
x = other.x;
y = other.y;
z = 0.0;
}
Vector3f::~Vector3f() {
}
float Vector3f::getX() const {
return x;
}
void Vector3f::setX(float x) {
this->x = x;
}
float Vector3f::getY() const {
return y;
}
void Vector3f::setY(float y) {
this->y = y;
}
float Vector3f::getZ() const {
return z;
}
void Vector3f::setZ(float z) {
this->z = z;
}
float Vector3f::operator[](const int index) const {
float val=0.0;
switch (index) {
case 0: val = x;
break;
case 1: val = y;
break;
case 2: val = z;
break;
}
return val;
}
Vector3f Vector3f::operator+(const Vector3f& other) const {
return Vector3f(x + other.x, y + other.y, z + other.z);
}
Vector3f Vector3f::operator-(const Vector3f& other) const {
return Vector3f(x - other.x, y - other.y, z - other.z);
}
Vector3f Vector3f::operator-() const {
return Vector3f() - * this;
}
Vector3f Vector3f::operator*(const Vector3f& other) const {
return Vector3f(x * other.x, y * other.y, z * other.z);
}
Vector3f Vector3f::operator/(const Vector3f& other) const {
return Vector3f(x / other.x, y / other.y, z / other.z);
}
bool Vector3f::operator==(const Vector3f& other) const {
return x == other.x && y == other.y && z == other.z;
}
Vector3f Vector3f::operator/(float factor) const {
return Vector3f(x / factor, y / factor, z / factor);
}
Vector3f Vector3f::operator+(float factor) const {
return Vector3f(x + factor, y + factor, z + factor);
}
Vector3f Vector3f::operator%(float factor) const {
return Vector3f(fmod(x, factor), fmod(y, factor), fmod(z, factor));
}
Vector3f Vector3f::operator*(float factor) const {
return Vector3f(x * factor, y * factor, z * factor);
}
Vector3f Vector3f::operator+=(const Vector3f& other) {
x += other.x;
y += other.y;
z += other.z;
return *this;
}
Vector3f Vector3f::operator+=(float factor) {
x += factor;
y += factor;
z += factor;
return *this;
}
Vector3f Vector3f::operator-=(const Vector3f& other) {
x -= other.x;
y -= other.y;
z -= other.z;
return *this;
}
Vector3f Vector3f::operator-=(float factor) {
x -= factor;
y -= factor;
z -= factor;
return *this;
}
Vector3f Vector3f::operator/=(float factor) {
x /= factor;
y /= factor;
z /= factor;
return *this;
}
float Vector3f::innerProduct(const Vector3f& other) const {
return x * other.x + y * other.y + z * other.z;
}
Vector3f Vector3f::crossProduct(const Vector3f& other) const {
Vector3f aux;
aux.x = this->y * other.z - this->z * other.y;
aux.y = this->z * other.x - this->x * other.z;
aux.z = this->x * other.y - this->y * other.x;
return aux;
}
float Vector3f::length() const {
return sqrt(x * x + y * y + z * z);
}
Vector3f Vector3f::normalize(float len) const {
return (*this) * (len / this->length());
}
Vector3f Vector3f::toCartesian() const {
// x = distance
// y = theta
// z = phi
return Vector3f(x * Cos(z) * Cos(y), x * Cos(z) * Sin(y), x * Sin(z));
}
Vector3f Vector3f::toPolar() const {
return Vector3f(this->length(), // distance
ATan2(y, x), // theta
ATan2(z, sqrt(x * x + y * y))); // phi
}
float Vector3f::dist(const Vector3f &other) const {
return (*this -other).length();
}
Vector Vector3f::to2d() const {
return Vector(x, y);
}
Vector3f Vector3f::determineMidpoint(Vector3f a, Vector3f b) {
return (a+b)/2; /* m.abreu@2020 */
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/Vector3f.cpp
|
C++
|
agpl-3.0
| 3,892
|
#ifndef VECTOR3F_H
#define VECTOR3F_H
#include <cmath>
#include "Geometry.h"
#include <math.h>
//! Describes a vector of three floats
/*!
* \author Hugo Picado (hugopicado@ua.pt)
* \author Nuno Almeida (nuno.alm@ua.pt)
* Adapted - Miguel Abreu
*/
class Vector3f {
public:
//! Default constructor
Vector3f();
/*!
* Constructor
*
* \param x x-axis coordinate
* \param y y-axis coordinate
* \param z z-axis coordinate
*/
Vector3f(float x, float y, float z);
//! Copy constructor
Vector3f(const Vector3f& other);
Vector3f(const Vector& other);
//! Destructor
~Vector3f();
//! getX
float getX() const;
void setX(float x);
//! getY
float getY() const;
void setY(float y);
//! getZ
float getZ() const;
void setZ(float z);
//! Access X Y Z through indexes 0 1 2
float operator[](const int) const;
//! Sums this vector to another
Vector3f operator+(const Vector3f& other) const;
//! Subtracts another vector from this
Vector3f operator-(const Vector3f& other) const;
//! Negates the vector
Vector3f operator-() const;
//! Multiples this vector by another
Vector3f operator*(const Vector3f& other) const;
//! Divides this vector by another
Vector3f operator/(const Vector3f& other) const;
bool operator==(const Vector3f& other) const;
/*!
* Multiples this vector by a scalar
*
* \param factor Scalar number
*/
Vector3f operator*(float factor) const;
/*!
* Divides this vector by a scalar
*
* \param factor Scalar number
*/
Vector3f operator/(float factor) const;
/*!
* Add this vector to a scalar
*
* \param factor Scalar number
*/
Vector3f operator+(float factor) const;
/*!
* Integer remainder this vector by a scalar
*
* \param factor Scalar number
*/
Vector3f operator%(float factor) const;
/**
* Sums this vector to another assuming the value of the result
*
* \param other Vector3f
*/
Vector3f operator+=(const Vector3f& other);
/**
* Add this vector to a scalar assuming the value of the result
*
* \param factor Scalar number
*/
Vector3f operator+=(float factor);
/**
* Subtracts other vector from this vector assuming the value of the result
*
* \param other Vector3f
*/
Vector3f operator-=(const Vector3f& other);
/**
* Subtracts a scalar from this vector assuming the value of the result
*
* \param factor Scalar number
*/
Vector3f operator-=(float factor);
/*!
* Divides this vector by a scalar assuming the value of the result
*
* \param factor Scalar number
*/
Vector3f operator/=(float factor);
/*!
* Computes the inner product of this vector with another
*
* \param other Vector to compute the inner product
* \return Resultant vector
*/
float innerProduct(const Vector3f& other) const;
/*!
* Computes the cross product of this vector with another
*
* \param other Vector to compute the cross product
* \return Resultant vector
*/
Vector3f crossProduct(const Vector3f& other) const;
/*!
* Gets the length of the vector
*
* \return Length of the vector
*/
float length() const;
/*!
* Normalizes the vector to an arbitrary length (default is 1)
*
* \param len Length
*/
Vector3f normalize(float len = 1) const;
/*!
* Converts the vector coordinates from polar to cartesian
* It is assumed that the vector has the angular coordinates in degrees
*/
Vector3f toCartesian() const;
//! Converts the vector coordinates from cartesian to polar
Vector3f toPolar() const;
//! Converts the 3d vector to a 2d vector
Vector to2d() const;
//! Gets the distance between this vector and another
float dist(const Vector3f& other) const;
/* !Determines the midpoint between 2 points in a 3D space
*
**/
static Vector3f determineMidpoint(Vector3f a, Vector3f b);
public:
float x; // x-axis coordinate
float y; // y-axis coordinate
float z; // z-axis coordinate
};
#endif // VECTOR3F_H
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/Vector3f.h
|
C++
|
agpl-3.0
| 4,320
|
/**
* FILENAME: World
* DESCRIPTION: World data from Python
* AUTHOR: Miguel Abreu (m.abreu@fe.up.pt)
* DATE: 2021
*/
#pragma once
#include "Vector3f.h"
#include "Singleton.h"
#include "Matrix4D.h"
#include "Line6f.h"
#include <vector>
#include <array>
using namespace std;
class World {
friend class Singleton<World>;
private:
World(){};
public:
//Feet variables: (0) left, (1) right
bool foot_touch[2]; // is foot touching ground
Vector3f foot_contact_rel_pos[2]; // foot_transform * translation(foot_contact_pt)
bool ball_seen;
Vector3f ball_rel_pos_cart;
Vector3f ball_cheat_abs_cart_pos;
Vector3f my_cheat_abs_cart_pos;
struct sLMark {
bool seen;
bool isCorner;
Vector3f pos;
Vector3f rel_pos;
};
sLMark landmark[8];
struct sLine {
Vector3f start, end;
sLine(const Vector3f& s, const Vector3f& e) : start(s), end(e) {};
};
vector<sLine> lines_polar;
};
typedef Singleton<World> SWorld;
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/World.h
|
C++
|
agpl-3.0
| 1,067
|
#include <iostream>
#include "Geometry.h"
#include "Vector3f.h"
#include "Matrix4D.h"
#include "FieldNoise.h"
#include "Line6f.h"
#include "World.h"
#include "Field.h"
#include "LocalizerV2.h"
using namespace std;
static LocalizerV2& loc = SLocalizerV2::getInstance();
void print_python_data(){
static World &world = SWorld::getInstance();
cout << "Foot touch: " << world.foot_touch[0] << " " << world.foot_touch[1] << endl;
cout << "LFoot contact rpos: " << world.foot_contact_rel_pos[0].x << " " << world.foot_contact_rel_pos[0].y << " " << world.foot_contact_rel_pos[0].z << endl;
cout << "RFoot contact rpos: " << world.foot_contact_rel_pos[1].x << " " << world.foot_contact_rel_pos[1].y << " " << world.foot_contact_rel_pos[1].z << endl;
cout << "Ball seen: " << world.ball_seen << endl;
cout << "Ball rpos cart: " << world.ball_rel_pos_cart.x << " " << world.ball_rel_pos_cart.y << " " << world.ball_rel_pos_cart.z << endl;
cout << "Ball cheat: " << world.ball_cheat_abs_cart_pos.x << " " << world.ball_cheat_abs_cart_pos.y << " " << world.ball_cheat_abs_cart_pos.z << endl;
cout << "Me cheat: " << world.my_cheat_abs_cart_pos.x << " " << world.my_cheat_abs_cart_pos.y << " " << world.my_cheat_abs_cart_pos.z << endl;
for(int i=0; i<8; i++){
cout << "Landmark " << i << ": " <<
world.landmark[i].seen << " " <<
world.landmark[i].isCorner << " " <<
world.landmark[i].pos.x << " " <<
world.landmark[i].pos.y << " " <<
world.landmark[i].pos.z << " " <<
world.landmark[i].rel_pos.x << " " <<
world.landmark[i].rel_pos.y << " " <<
world.landmark[i].rel_pos.z << endl;
}
for(int i=0; i<world.lines_polar.size(); i++){
cout << "Line " << i << ": " <<
world.lines_polar[i].start.x << " " <<
world.lines_polar[i].start.y << " " <<
world.lines_polar[i].start.z << " " <<
world.lines_polar[i].end.x << " " <<
world.lines_polar[i].end.y << " " <<
world.lines_polar[i].end.z << endl;
}
}
float *compute(bool lfoot_touch, bool rfoot_touch,
double feet_contact[],
bool ball_seen, double ball_pos[],
double me_pos[],
double landmarks[],
double lines[],
int lines_no){
// ================================================= 1. Parse data
static World &world = SWorld::getInstance();
world.foot_touch[0] = lfoot_touch;
world.foot_touch[1] = rfoot_touch;
//Structure of feet_contact {lfoot_contact_pt, rfoot_contact_pt, lfoot_contact_rel_pos, rfoot_contact_rel_pos}
world.foot_contact_rel_pos[0].x = feet_contact[0];
world.foot_contact_rel_pos[0].y = feet_contact[1];
world.foot_contact_rel_pos[0].z = feet_contact[2];
world.foot_contact_rel_pos[1].x = feet_contact[3];
world.foot_contact_rel_pos[1].y = feet_contact[4];
world.foot_contact_rel_pos[1].z = feet_contact[5];
world.ball_seen = ball_seen;
//Structure of ball_pos {ball_rel_pos_cart, ball_cheat_abs_cart_pos}
world.ball_rel_pos_cart.x = ball_pos[0];
world.ball_rel_pos_cart.y = ball_pos[1];
world.ball_rel_pos_cart.z = ball_pos[2];
world.ball_cheat_abs_cart_pos.x = ball_pos[3];
world.ball_cheat_abs_cart_pos.y = ball_pos[4];
world.ball_cheat_abs_cart_pos.z = ball_pos[5];
world.my_cheat_abs_cart_pos.x = me_pos[0];
world.my_cheat_abs_cart_pos.y = me_pos[1];
world.my_cheat_abs_cart_pos.z = me_pos[2];
for(int i=0; i<8; i++){
world.landmark[i].seen = (bool) landmarks[0];
world.landmark[i].isCorner = (bool) landmarks[1];
world.landmark[i].pos.x = landmarks[2];
world.landmark[i].pos.y = landmarks[3];
world.landmark[i].pos.z = landmarks[4];
world.landmark[i].rel_pos.x = landmarks[5];
world.landmark[i].rel_pos.y = landmarks[6];
world.landmark[i].rel_pos.z = landmarks[7];
landmarks += 8;
}
world.lines_polar.clear();
for(int i=0; i<lines_no; i++){
Vector3f s(lines[0],lines[1],lines[2]);
Vector3f e(lines[3],lines[4],lines[5]);
world.lines_polar.emplace_back(s, e);
lines += 6;
}
print_python_data();
// ================================================= 2. Compute 6D pose
loc.run();
// ================================================= 3. Prepare data to return
float retval[35];
float *ptr = retval;
for(int i=0; i<16; i++){
ptr[i] = loc.headTofieldTransform.content[i];
}
ptr += 16;
for(int i=0; i<16; i++){
ptr[i] = loc.fieldToheadTransform.content[i];
}
ptr += 16;
ptr[0] = (float) loc.is_uptodate;
ptr[1] = loc.head_z;
ptr[2] = (float) loc.is_head_z_uptodate;
return retval;
}
void print_report(){
loc.print_report();
}
void draw_visible_elements(bool is_right_side){
Field& fd = SField::getInstance();
fd.draw_visible(loc.headTofieldTransform, is_right_side);
}
int main(){
double feet_contact[] = {0.02668597, 0.055 , -0.49031584, 0.02668597, -0.055 , -0.49031584};
double ball_pos[] = {22.3917517 , 4.91904904, -0.44419865, -0. , -0. , 0.04 };
double me_pos[] = {-22.8 , -2.44, 0.48};
double landmarks[] = { 1. , 1. , -15. , -10. , 0. , 10.88, -37.74, -2.42,
0. , 1. , -15. , 10. , 0. , 0. , 0. , 0. ,
1. , 1. , 15. , -10. , 0. , 38.56, -4.9 , -0.66,
1. , 1. , 15. , 10. , 0. , 39.75, 24.4 , -0.7 ,
1. , 0. , -15. , -1.05, 0.8 , 7.94, 16.31, 2.42,
1. , 0. , -15. , 1.05, 0.8 , 8.55, 30.15, 2.11,
1. , 0. , 15. , -1.05, 0.8 , 37.82, 8.16, 0.5 ,
1. , 0. , 15. , 1.05, 0.8 , 37.94, 11.77, 0.44 };
double lines[] = { 25.95, 35.02, -1.14, 24.02, -12.26, -1.12,
13.18, 59.93, -2.11, 10.87, -37.8 , -2.69,
39.78, 24.32, -0.75, 38.64, -5.05, -0.67,
10.89, -37.56, -2.6 , 38.52, -5.24, -0.68,
15.44, 59.85, -1.87, 39.76, 24.77, -0.88,
9.62, 3.24, -2.67, 11.02, 36.02, -2.54,
9.63, 2.82, -3.16, 7.82, 2.14, -3.67,
11.02, 36.09, -2.61, 9.51, 41.19, -2.94,
36.03, 5.33, -0.66, 36.46, 14.9 , -0.74,
35.94, 5.43, -0.72, 37.81, 5.26, -0.73,
36.42, 14.72, -0.83, 38.16, 14.68, -0.85,
20.93, 13.26, -1.33, 21.25, 9.66, -1.15,
21.21, 9.75, -1.6 , 22.18, 7.95, -1.19,
22.21, 7.94, -1.17, 23.43, 7.82, -1.11,
23.38, 7.55, -1.18, 24.42, 9.47, -1.16,
24.43, 9.37, -1.25, 24.9 , 11.72, -0.98,
24.89, 11.73, -1.2 , 24.68, 14.54, -1.05,
24.7 , 14.85, -1.06, 23.85, 16.63, -1.1 ,
23.82, 16.53, -1.14, 22.61, 17.14, -1.32,
22.65, 17.53, -1.23, 21.5 , 16.19, -1.34,
21.49, 15.92, -1.32, 20.95, 13.07, -1.32 };
int lines_no = sizeof(lines)/sizeof(lines[0])/6;
compute(true, // lfoot_touch
true, // rfoot_touch
feet_contact,
true, // ball_seen
ball_pos,
me_pos,
landmarks,
lines,
lines_no);
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/debug_main.cc
|
C++
|
agpl-3.0
| 7,957
|
#include <iostream>
#include "Geometry.h"
#include "Vector3f.h"
#include "Matrix4D.h"
#include "FieldNoise.h"
#include "Line6f.h"
#include "World.h"
#include "Field.h"
#include "LocalizerV2.h"
#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
namespace py = pybind11;
using namespace std;
static LocalizerV2& loc = SLocalizerV2::getInstance();
void print_python_data(){
static World &world = SWorld::getInstance();
cout << "Foot touch: " << world.foot_touch[0] << " " << world.foot_touch[1] << endl;
cout << "LFoot contact rpos: " << world.foot_contact_rel_pos[0].x << " " << world.foot_contact_rel_pos[0].y << " " << world.foot_contact_rel_pos[0].z << endl;
cout << "RFoot contact rpos: " << world.foot_contact_rel_pos[1].x << " " << world.foot_contact_rel_pos[1].y << " " << world.foot_contact_rel_pos[1].z << endl;
cout << "Ball seen: " << world.ball_seen << endl;
cout << "Ball rpos cart: " << world.ball_rel_pos_cart.x << " " << world.ball_rel_pos_cart.y << " " << world.ball_rel_pos_cart.z << endl;
cout << "Ball cheat: " << world.ball_cheat_abs_cart_pos.x << " " << world.ball_cheat_abs_cart_pos.y << " " << world.ball_cheat_abs_cart_pos.z << endl;
cout << "Me cheat: " << world.my_cheat_abs_cart_pos.x << " " << world.my_cheat_abs_cart_pos.y << " " << world.my_cheat_abs_cart_pos.z << endl;
for(int i=0; i<8; i++){
cout << "Landmark " << i << ": " <<
world.landmark[i].seen << " " <<
world.landmark[i].isCorner << " " <<
world.landmark[i].pos.x << " " <<
world.landmark[i].pos.y << " " <<
world.landmark[i].pos.z << " " <<
world.landmark[i].rel_pos.x << " " <<
world.landmark[i].rel_pos.y << " " <<
world.landmark[i].rel_pos.z << endl;
}
for(int i=0; i<world.lines_polar.size(); i++){
cout << "Line " << i << ": " <<
world.lines_polar[i].start.x << " " <<
world.lines_polar[i].start.y << " " <<
world.lines_polar[i].start.z << " " <<
world.lines_polar[i].end.x << " " <<
world.lines_polar[i].end.y << " " <<
world.lines_polar[i].end.z << endl;
}
}
py::array_t<float> compute(
bool lfoot_touch, bool rfoot_touch,
py::array_t<double> feet_contact,
bool ball_seen, py::array_t<double> ball_pos,
py::array_t<double> me_pos,
py::array_t<double> landmarks,
py::array_t<double> lines){
// ================================================= 1. Parse data
static World &world = SWorld::getInstance();
world.foot_touch[0] = lfoot_touch;
world.foot_touch[1] = rfoot_touch;
//Structure of feet_contact {lfoot_contact_pt, rfoot_contact_pt, lfoot_contact_rel_pos, rfoot_contact_rel_pos}
py::buffer_info feet_contact_buf = feet_contact.request();
double *feet_contact_ptr = (double *) feet_contact_buf.ptr;
world.foot_contact_rel_pos[0].x = feet_contact_ptr[0];
world.foot_contact_rel_pos[0].y = feet_contact_ptr[1];
world.foot_contact_rel_pos[0].z = feet_contact_ptr[2];
world.foot_contact_rel_pos[1].x = feet_contact_ptr[3];
world.foot_contact_rel_pos[1].y = feet_contact_ptr[4];
world.foot_contact_rel_pos[1].z = feet_contact_ptr[5];
world.ball_seen = ball_seen;
//Structure of ball_pos {ball_rel_pos_cart, ball_cheat_abs_cart_pos}
py::buffer_info ball_pos_buf = ball_pos.request();
double *ball_pos_ptr = (double *) ball_pos_buf.ptr;
world.ball_rel_pos_cart.x = ball_pos_ptr[0];
world.ball_rel_pos_cart.y = ball_pos_ptr[1];
world.ball_rel_pos_cart.z = ball_pos_ptr[2];
world.ball_cheat_abs_cart_pos.x = ball_pos_ptr[3];
world.ball_cheat_abs_cart_pos.y = ball_pos_ptr[4];
world.ball_cheat_abs_cart_pos.z = ball_pos_ptr[5];
py::buffer_info me_pos_buf = me_pos.request();
double *me_pos_ptr = (double *) me_pos_buf.ptr;
world.my_cheat_abs_cart_pos.x = me_pos_ptr[0];
world.my_cheat_abs_cart_pos.y = me_pos_ptr[1];
world.my_cheat_abs_cart_pos.z = me_pos_ptr[2];
py::buffer_info landmarks_buf = landmarks.request();
double *landmarks_ptr = (double *) landmarks_buf.ptr;
for(int i=0; i<8; i++){
world.landmark[i].seen = (bool) landmarks_ptr[0];
world.landmark[i].isCorner = (bool) landmarks_ptr[1];
world.landmark[i].pos.x = landmarks_ptr[2];
world.landmark[i].pos.y = landmarks_ptr[3];
world.landmark[i].pos.z = landmarks_ptr[4];
world.landmark[i].rel_pos.x = landmarks_ptr[5];
world.landmark[i].rel_pos.y = landmarks_ptr[6];
world.landmark[i].rel_pos.z = landmarks_ptr[7];
landmarks_ptr += 8;
}
py::buffer_info lines_buf = lines.request();
int lines_len = lines_buf.shape[0];
double *lines_ptr = (double *) lines_buf.ptr;
world.lines_polar.clear();
for(int i=0; i<lines_len; i++){
Vector3f s(lines_ptr[0],lines_ptr[1],lines_ptr[2]);
Vector3f e(lines_ptr[3],lines_ptr[4],lines_ptr[5]);
world.lines_polar.emplace_back(s, e);
lines_ptr += 6;
}
// ================================================= 2. Compute 6D pose
loc.run();
// ================================================= 3. Prepare data to return
py::array_t<float> retval = py::array_t<float>(35); //allocate
py::buffer_info buff = retval.request();
float *ptr = (float *) buff.ptr;
for(int i=0; i<16; i++){
ptr[i] = loc.headTofieldTransform.content[i];
}
ptr += 16;
for(int i=0; i<16; i++){
ptr[i] = loc.fieldToheadTransform.content[i];
}
ptr += 16;
ptr[0] = (float) loc.is_uptodate;
ptr[1] = loc.head_z;
ptr[2] = (float) loc.is_head_z_uptodate;
return retval;
}
void print_report(){
loc.print_report();
}
void draw_visible_elements(bool is_right_side){
Field& fd = SField::getInstance();
fd.draw_visible(loc.headTofieldTransform, is_right_side);
}
using namespace pybind11::literals; //to add informative argument names as -> "argname"_a
PYBIND11_MODULE(localization, m) { //the python module name, m is the interface to create bindings
m.doc() = "Probabilistic 6D localization algorithm"; // optional module docstring
//optional arguments names
m.def("compute", &compute, "Compute the 6D pose based on visual information and return transformation matrices and other relevant data",
"lfoot_touch"_a,
"rfoot_touch"_a,
"feet_contact"_a,
"ball_seen"_a,
"ball_pos"_a,
"me_pos"_a,
"landmarks"_a,
"lines"_a);
m.def("print_python_data", &print_python_data, "Print data received from Python");
m.def("print_report", &print_report, "Print localization report");
m.def("draw_visible_elements", &draw_visible_elements, "Draw all visible elements in RoboViz", "is_right_side"_a);
}
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/lib_main.cpp
|
C++
|
agpl-3.0
| 6,887
|
/*
* Copyright (C) 2011 Justin Stoecker
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef _ROBOVIZDRAW_H_
#define _ROBOVIZDRAW_H_
#include <string>
#include <cstdio>
#include <cstring>
using namespace std;
inline int writeCharToBuf(unsigned char* buf, unsigned char value) {
*buf = value;
return 1;
}
inline int writeFloatToBuf(unsigned char* buf, float value) {
char temp[20];
sprintf(temp, "%6f", value);
memcpy(buf, temp, 6);
return 6;
}
inline int writeColorToBuf(unsigned char* buf, const float* color, int channels) {
int i;
for (i = 0; i < channels; i++)
writeCharToBuf(buf + i, (unsigned char) (color[i]*255));
return i;
}
inline int writeStringToBuf(unsigned char* buf, const string* text) {
long i = 0;
if (text != NULL)
i += text->copy((char*) buf + i, text->length(), 0);
i += writeCharToBuf(buf + i, 0);
return i;
}
unsigned char* newBufferSwap(const string* name, int* bufSize) {
*bufSize = 3 + ((name != NULL) ? name->length() : 0);
unsigned char* buf = new unsigned char[*bufSize];
long i = 0;
i += writeCharToBuf(buf + i, 0);
i += writeCharToBuf(buf + i, 0);
i += writeStringToBuf(buf + i, name);
return buf;
}
unsigned char* newCircle(const float* center, float radius, float thickness,
const float* color, const string* setName, int* bufSize) {
*bufSize = 30 + ((setName != NULL) ? setName->length() : 0);
unsigned char* buf = new unsigned char[*bufSize];
long i = 0;
i += writeCharToBuf(buf + i, 1);
i += writeCharToBuf(buf + i, 0);
i += writeFloatToBuf(buf + i, center[0]);
i += writeFloatToBuf(buf + i, center[1]);
i += writeFloatToBuf(buf + i, radius);
i += writeFloatToBuf(buf + i, thickness);
i += writeColorToBuf(buf + i, color, 3);
i += writeStringToBuf(buf + i, setName);
return buf;
}
unsigned char* newLine(const float* a, const float* b, float thickness,
const float* color, const string* setName, int* bufSize) {
*bufSize = 48 + ((setName != NULL) ? setName->length() : 0);
unsigned char* buf = new unsigned char[*bufSize];
long i = 0;
i += writeCharToBuf(buf + i, 1);
i += writeCharToBuf(buf + i, 1);
i += writeFloatToBuf(buf + i, a[0]);
i += writeFloatToBuf(buf + i, a[1]);
i += writeFloatToBuf(buf + i, a[2]);
i += writeFloatToBuf(buf + i, b[0]);
i += writeFloatToBuf(buf + i, b[1]);
i += writeFloatToBuf(buf + i, b[2]);
i += writeFloatToBuf(buf + i, thickness);
i += writeColorToBuf(buf + i, color, 3);
i += writeStringToBuf(buf + i, setName);
return buf;
}
unsigned char* newPoint(const float* p, float size, const float* color,
const string* setName, int* bufSize) {
*bufSize = 30 + ((setName != NULL) ? setName->length() : 0);
unsigned char* buf = new unsigned char[*bufSize];
long i = 0;
i += writeCharToBuf(buf + i, 1);
i += writeCharToBuf(buf + i, 2);
i += writeFloatToBuf(buf + i, p[0]);
i += writeFloatToBuf(buf + i, p[1]);
i += writeFloatToBuf(buf + i, p[2]);
i += writeFloatToBuf(buf + i, size);
i += writeColorToBuf(buf + i, color, 3);
i += writeStringToBuf(buf + i, setName);
return buf;
}
unsigned char* newSphere(const float* p, float radius, const float* color,
const string* setName, int* bufSize) {
*bufSize = 30 + ((setName != NULL) ? setName->length() : 0);
unsigned char* buf = new unsigned char[*bufSize];
long i = 0;
i += writeCharToBuf(buf + i, 1);
i += writeCharToBuf(buf + i, 3);
i += writeFloatToBuf(buf + i, p[0]);
i += writeFloatToBuf(buf + i, p[1]);
i += writeFloatToBuf(buf + i, p[2]);
i += writeFloatToBuf(buf + i, radius);
i += writeColorToBuf(buf + i, color, 3);
i += writeStringToBuf(buf + i, setName);
return buf;
}
unsigned char* newPolygon(const float* v, int numVerts, const float* color,
const string* setName, int* bufSize) {
*bufSize = 18 * numVerts + 8 + ((setName != NULL) ? setName->length() : 0);
unsigned char* buf = new unsigned char[*bufSize];
long i = 0;
i += writeCharToBuf(buf + i, 1);
i += writeCharToBuf(buf + i, 4);
i += writeCharToBuf(buf + i, numVerts);
i += writeColorToBuf(buf + i, color, 4);
for (int j = 0; j < numVerts; j++) {
i += writeFloatToBuf(buf + i, v[j * 3 + 0]);
i += writeFloatToBuf(buf + i, v[j * 3 + 1]);
i += writeFloatToBuf(buf + i, v[j * 3 + 2]);
}
i += writeStringToBuf(buf + i, setName);
return buf;
}
unsigned char* newAnnotation(const string* text, const float* p,
const float* color, const string* setName, int* bufSize) {
*bufSize = 25 + text->length() + ((setName != NULL) ? setName->length() : 0);
unsigned char* buf = new unsigned char[*bufSize];
long i = 0;
i += writeCharToBuf(buf + i, 2);
i += writeCharToBuf(buf + i, 0);
i += writeFloatToBuf(buf + i, p[0]);
i += writeFloatToBuf(buf + i, p[1]);
i += writeFloatToBuf(buf + i, p[2]);
i += writeColorToBuf(buf + i, color, 3);
i += writeStringToBuf(buf + i, text);
i += writeStringToBuf(buf + i, setName);
return buf;
}
unsigned char* newAgentAnnotation(const string* text, bool leftTeam,
int agentNum, const float* color, int* bufSize) {
*bufSize = (text == NULL) ? 3 : 7 + text->length();
unsigned char* buf = new unsigned char[*bufSize];
long i = 0;
i += writeCharToBuf(buf + i, 2);
if (text == NULL) {
i += writeCharToBuf(buf + i, 2);
i += writeCharToBuf(buf + i, (leftTeam ? agentNum - 1 : agentNum + 127));
} else {
i += writeCharToBuf(buf + i, 1);
i += writeCharToBuf(buf + i, (leftTeam ? agentNum - 1 : agentNum + 127));
i += writeColorToBuf(buf + i, color, 3);
i += writeStringToBuf(buf + i, text);
}
return buf;
}
#endif
|
2302_81918214/robocup3D
|
FCPCodebase-main/cpp/localization/robovizdraw.h
|
C++
|
agpl-3.0
| 6,427
|
#!/bin/bash
pkill -9 -e -f "python3 ./Run_"
|
2302_81918214/robocup3D
|
FCPCodebase-main/kill.sh
|
Shell
|
agpl-3.0
| 44
|
from pathlib import Path
from datetime import datetime
import random
from string import ascii_uppercase
class Logger():
_folder = None
def __init__(self, is_enabled:bool, topic:str) -> None:
self.no_of_entries = 0
self.enabled = is_enabled
self.topic = topic
def write(self, msg:str, timestamp:bool=True, step:int=None) -> None:
'''
Write `msg` to file named `self.topic`
Parameters
----------
msg : str
message to be written
step : int
simulation step is written before the message to provide additional information
default is `None` (nothing is written before the message)
'''
if not self.enabled: return
# The log folder is only created if needed
if Logger._folder is None:
rnd = ''.join(random.choices(ascii_uppercase, k=6)) # Useful if multiple processes are running in parallel
Logger._folder = "./logs/" + datetime.now().strftime("%Y-%m-%d_%H.%M.%S__") + rnd + "/"
print("\nLogger Info: see",Logger._folder)
Path(Logger._folder).mkdir(parents=True, exist_ok=True)
self.no_of_entries += 1
with open(Logger._folder + self.topic + ".log", 'a+') as f:
prefix = ""
write_step = step is not None
if timestamp or write_step:
prefix = "{"
if timestamp:
prefix += datetime.now().strftime("%a %H:%M:%S")
if write_step: prefix += " "
if write_step:
prefix += f'Step:{step}'
prefix += "} "
f.write(prefix + msg + "\n")
|
2302_81918214/robocup3D
|
FCPCodebase-main/logs/Logger.py
|
Python
|
agpl-3.0
| 1,713
|
from math import asin, atan, atan2, pi, sqrt
from math_ops.Matrix_3x3 import Matrix_3x3
from math_ops.Math_Ops import Math_Ops as M
import numpy as np
class Inverse_Kinematics():
# leg y deviation, upper leg height, upper leg depth, lower leg length, knee extra angle, max ankle z
NAO_SPECS_PER_ROBOT = ((0.055, 0.12, 0.005, 0.1, atan(0.005/0.12), -0.091),
(0.055, 0.13832, 0.005, 0.11832, atan(0.005/0.13832), -0.106),
(0.055, 0.12, 0.005, 0.1, atan(0.005/0.12), -0.091),
(0.072954143,0.147868424, 0.005, 0.127868424, atan(0.005/0.147868424), -0.114),
(0.055, 0.12, 0.005, 0.1, atan(0.005/0.12), -0.091))
TORSO_HIP_Z = 0.115 # distance in the z-axis, between the torso and each hip (same for all robots)
TORSO_HIP_X = 0.01 # distance in the x-axis, between the torso and each hip (same for all robots) (hip is 0.01m to the back)
def __init__(self, robot) -> None:
self.robot = robot
self.NAO_SPECS = Inverse_Kinematics.NAO_SPECS_PER_ROBOT[robot.type]
def torso_to_hip_transform(self, coords, is_batch=False):
'''
Convert cartesian coordinates that are relative to torso to coordinates that are relative the center of both hip joints
Parameters
----------
coords : array_like
One 3D position or list of 3D positions
is_batch : `bool`
Indicates if coords is a batch of 3D positions
Returns
-------
coord : `list` or ndarray
A numpy array is returned if is_batch is False, otherwise, a list of arrays is returned
'''
if is_batch:
return [c + (Inverse_Kinematics.TORSO_HIP_X, 0, Inverse_Kinematics.TORSO_HIP_Z) for c in coords]
else:
return coords + (Inverse_Kinematics.TORSO_HIP_X, 0, Inverse_Kinematics.TORSO_HIP_Z)
def head_to_hip_transform(self, coords, is_batch=False):
'''
Convert cartesian coordinates that are relative to head to coordinates that are relative the center of both hip joints
Parameters
----------
coords : array_like
One 3D position or list of 3D positions
is_batch : `bool`
Indicates if coords is a batch of 3D positions
Returns
-------
coord : `list` or ndarray
A numpy array is returned if is_batch is False, otherwise, a list of arrays is returned
'''
coords_rel_torso = self.robot.head_to_body_part_transform( "torso", coords, is_batch )
return self.torso_to_hip_transform(coords_rel_torso, is_batch)
def get_body_part_pos_relative_to_hip(self, body_part_name):
''' Get body part position relative to the center of both hip joints '''
bp_rel_head = self.robot.body_parts[body_part_name].transform.get_translation()
return self.head_to_hip_transform(bp_rel_head)
def get_ankle_pos_relative_to_hip(self, is_left):
''' Internally calls get_body_part_pos_relative_to_hip() '''
return self.get_body_part_pos_relative_to_hip("lankle" if is_left else "rankle")
def get_linear_leg_trajectory(self, is_left:bool, p1, p2=None, foot_ori3d=(0,0,0), dynamic_pose:bool=True, resolution=100):
'''
Compute leg trajectory so that the ankle moves linearly between two 3D points (relative to hip)
Parameters
----------
is_left : `bool`
set to True to select left leg, False to select right leg
p1 : array_like, length 3
if p2 is None:
p1 is the target position (relative to hip), and the initial point is given by the ankle's current position
if p2 is not None:
p1 is the initial point (relative to hip)
p2 : array_like, length 3 / `None`
target position (relative to hip) or None (see p1)
foot_ori3d : array_like, length 3
rotation around x,y,z (rotation around x & y are biases, relative to a vertical pose, or dynamic pose, if enabled)
dynamic_pose : `bool`
enable dynamic feet rotation to be parallel to the ground, based on IMU
resolution : int
interpolation resolution; more resolution is always better, but it takes more time to compute;
having more points does not make the movement slower, because if there are excessive points they are removed
during the analytical optimization
Returns
-------
trajecory : `tuple`
indices, [[values_1,error_codes_1], [values_2,error_codes_2], ...]
See leg() for further details
'''
if p2 is None:
p2 = np.asarray(p1, float)
p1 = self.get_body_part_pos_relative_to_hip('lankle' if is_left else 'rankle')
else:
p1 = np.asarray(p1, float)
p2 = np.asarray(p2, float)
vec = (p2 - p1) / resolution
hip_points = [p1 + vec * i for i in range(1,resolution+1)]
interpolation = [self.leg(p, foot_ori3d, is_left, dynamic_pose) for p in hip_points]
indices = [2,4,6,8,10,12] if is_left else [3,5,7,9,11,13]
last_joint_values = self.robot.joints_position[indices[0:4]] #exclude feet joints to compute ankle trajectory
next_step = interpolation[0]
trajectory = []
for p in interpolation[1:-1]:
if np.any(np.abs(p[1][0:4]-last_joint_values) > 7.03):
trajectory.append(next_step[1:3])
last_joint_values = next_step[1][0:4]
next_step = p
else:
next_step = p
trajectory.append(interpolation[-1][1:3])
return indices, trajectory
def leg(self, ankle_pos3d, foot_ori3d, is_left:bool, dynamic_pose:bool):
'''
Compute inverse kinematics for the leg, considering as input the relative 3D position of the ankle and 3D orientation* of the foot
*the yaw can be controlled directly, but the pitch and roll are biases (see below)
Parameters
----------
ankle_pos3d : array_like, length 3
(x,y,z) position of ankle in 3D, relative to the center of both hip joints
foot_ori3d : array_like, length 3
rotation around x,y,z (rotation around x & y are biases, relative to a vertical pose, or dynamic pose, if enabled)
is_left : `bool`
set to True to select left leg, False to select right leg
dynamic_pose : `bool`
enable dynamic feet rotation to be parallel to the ground, based on IMU
Returns
-------
indices : `list`
indices of computed joints
values : `list`
values of computed joints
error_codes : `list`
list of error codes
Error codes:
(-1) Foot is too far (unreachable)
(x) Joint x is out of range
'''
error_codes = []
leg_y_dev, upper_leg_height, upper_leg_depth, lower_leg_len, knee_extra_angle, _ = self.NAO_SPECS
sign = -1 if is_left else 1
# Then we translate to origin of leg by shifting the y coordinate
ankle_pos3d = np.asarray(ankle_pos3d) + (0,sign*leg_y_dev,0)
# First we rotate the leg, then we rotate the coordinates to abstract from the rotation
ankle_pos3d = Matrix_3x3().rotate_z_deg(-foot_ori3d[2]).multiply(ankle_pos3d)
# Use geometric solution to compute knee angle and foot pitch
dist = np.linalg.norm(ankle_pos3d) #dist hip <-> ankle
sq_dist = dist * dist
sq_upper_leg_h = upper_leg_height * upper_leg_height
sq_lower_leg_l = lower_leg_len * lower_leg_len
sq_upper_leg_l = upper_leg_depth * upper_leg_depth + sq_upper_leg_h
upper_leg_len = sqrt(sq_upper_leg_l)
knee = M.acos((sq_upper_leg_l + sq_lower_leg_l - sq_dist)/(2 * upper_leg_len * lower_leg_len)) + knee_extra_angle # Law of cosines
foot = M.acos((sq_lower_leg_l + sq_dist - sq_upper_leg_l)/(2 * lower_leg_len * dist)) # foot perpendicular to vec(origin->ankle_pos)
# Check if target is reachable
if dist > upper_leg_len + lower_leg_len:
error_codes.append(-1)
# Knee and foot
knee_angle = pi - knee
foot_pitch = foot - atan(ankle_pos3d[0] / np.linalg.norm(ankle_pos3d[1:3]))
foot_roll = atan(ankle_pos3d[1] / min(-0.05, ankle_pos3d[2])) * -sign # avoid instability of foot roll (not relevant above -0.05m)
# Raw hip angles if all joints were straightforward
raw_hip_yaw = foot_ori3d[2]
raw_hip_pitch = foot_pitch - knee_angle
raw_hip_roll = -sign * foot_roll
# Rotate 45deg due to yaw joint orientation, then rotate yaw, roll and pitch
m = Matrix_3x3().rotate_y_rad(raw_hip_pitch).rotate_x_rad(raw_hip_roll).rotate_z_deg(raw_hip_yaw).rotate_x_deg(-45*sign)
# Get actual hip angles considering the yaw joint orientation
hip_roll = (pi/4) - (sign * asin(m.m[1,2])) #Add pi/4 due to 45deg rotation
hip_pitch = - atan2(m.m[0,2],m.m[2,2])
hip_yaw = sign * atan2(m.m[1,0],m.m[1,1])
# Convert rad to deg
values = np.array([hip_yaw,hip_roll,hip_pitch,-knee_angle,foot_pitch,foot_roll]) * 57.2957795 #rad to deg
# Set feet rotation bias (based on vertical pose, or dynamic_pose)
values[4] -= foot_ori3d[1]
values[5] -= foot_ori3d[0] * sign
indices = [2,4,6,8,10,12] if is_left else [3,5,7,9,11,13]
if dynamic_pose:
# Rotation of torso in relation to foot
m : Matrix_3x3 = Matrix_3x3.from_rotation_deg((self.robot.imu_torso_roll, self.robot.imu_torso_pitch, 0))
m.rotate_z_deg(foot_ori3d[2], True)
roll = m.get_roll_deg()
pitch = m.get_pitch_deg()
# Simple balance algorithm
correction = 1 #correction to motivate a vertical torso (in degrees)
roll = 0 if abs(roll) < correction else roll - np.copysign(correction,roll)
pitch = 0 if abs(pitch) < correction else pitch - np.copysign(correction,pitch)
values[4] += pitch
values[5] += roll * sign
# Check and limit range of joints
for i in range(len(indices)):
if values[i] < self.robot.joints_info[indices[i]].min or values[i] > self.robot.joints_info[indices[i]].max:
error_codes.append(indices[i])
values[i] = np.clip(values[i], self.robot.joints_info[indices[i]].min, self.robot.joints_info[indices[i]].max)
return indices, values, error_codes
|
2302_81918214/robocup3D
|
FCPCodebase-main/math_ops/Inverse_Kinematics.py
|
Python
|
agpl-3.0
| 10,887
|
from math import acos, asin, atan2, cos, pi, sin, sqrt
import numpy as np
import sys
try:
GLOBAL_DIR = sys._MEIPASS # temporary folder with libs & data files
except:
GLOBAL_DIR = "."
class Math_Ops():
'''
This class provides general mathematical operations that are not directly available through numpy
'''
@staticmethod
def deg_sph2cart(spherical_vec):
''' Converts SimSpark's spherical coordinates in degrees to cartesian coordinates '''
r = spherical_vec[0]
h = spherical_vec[1] * pi / 180
v = spherical_vec[2] * pi / 180
return np.array([r * cos(v) * cos(h), r * cos(v) * sin(h), r * sin(v)])
@staticmethod
def deg_sin(deg_angle):
''' Returns sin of degrees '''
return sin(deg_angle * pi / 180)
@staticmethod
def deg_cos(deg_angle):
''' Returns cos of degrees '''
return cos(deg_angle * pi / 180)
@staticmethod
def to_3d(vec_2d, value=0) -> np.ndarray:
''' Returns new 3d vector from 2d vector '''
return np.append(vec_2d,value)
@staticmethod
def to_2d_as_3d(vec_3d) -> np.ndarray:
''' Returns new 3d vector where the 3rd dimension is zero '''
vec_2d_as_3d = np.copy(vec_3d)
vec_2d_as_3d[2] = 0
return vec_2d_as_3d
@staticmethod
def normalize_vec(vec) -> np.ndarray:
''' Divides vector by its length '''
size = np.linalg.norm(vec)
if size == 0: return vec
return vec / size
@staticmethod
def get_active_directory(dir:str) -> str:
global GLOBAL_DIR
return GLOBAL_DIR + dir
@staticmethod
def acos(val):
''' arccosine function that limits input '''
return acos( np.clip(val,-1,1) )
@staticmethod
def asin(val):
''' arcsine function that limits input '''
return asin( np.clip(val,-1,1) )
@staticmethod
def normalize_deg(val):
''' normalize val in range [-180,180[ '''
return (val + 180.0) % 360 - 180
@staticmethod
def normalize_rad(val):
''' normalize val in range [-pi,pi[ '''
return (val + pi) % (2*pi) - pi
@staticmethod
def deg_to_rad(val):
''' convert degrees to radians '''
return val * 0.01745329251994330
@staticmethod
def rad_to_deg(val):
''' convert radians to degrees '''
return val * 57.29577951308232
@staticmethod
def vector_angle(vector, is_rad=False):
''' angle (degrees or radians) of 2D vector '''
if is_rad:
return atan2(vector[1], vector[0])
else:
return atan2(vector[1], vector[0]) * 180 / pi
@staticmethod
def vectors_angle(vec1, vec2, is_rad=False):
''' get angle between vectors (degrees or radians) '''
ang_rad = acos(np.dot(Math_Ops.normalize_vec(vec1),Math_Ops.normalize_vec(vec2)))
return ang_rad if is_rad else ang_rad * 180 / pi
@staticmethod
def vector_from_angle(angle, is_rad=False):
''' unit vector with direction given by `angle` '''
if is_rad:
return np.array([cos(angle), sin(angle)], float)
else:
return np.array([Math_Ops.deg_cos(angle), Math_Ops.deg_sin(angle)], float)
@staticmethod
def target_abs_angle(pos2d, target, is_rad=False):
''' angle (degrees or radians) of vector (target-pos2d) '''
if is_rad:
return atan2(target[1]-pos2d[1], target[0]-pos2d[0])
else:
return atan2(target[1]-pos2d[1], target[0]-pos2d[0]) * 180 / pi
@staticmethod
def target_rel_angle(pos2d, ori, target, is_rad=False):
''' relative angle (degrees or radians) of target if we're located at 'pos2d' with orientation 'ori' (degrees or radians) '''
if is_rad:
return Math_Ops.normalize_rad( atan2(target[1]-pos2d[1], target[0]-pos2d[0]) - ori )
else:
return Math_Ops.normalize_deg( atan2(target[1]-pos2d[1], target[0]-pos2d[0]) * 180 / pi - ori )
@staticmethod
def rotate_2d_vec(vec, angle, is_rad=False):
''' rotate 2D vector anticlockwise around the origin by `angle` '''
cos_ang = cos(angle) if is_rad else cos(angle * pi / 180)
sin_ang = sin(angle) if is_rad else sin(angle * pi / 180)
return np.array([cos_ang*vec[0]-sin_ang*vec[1], sin_ang*vec[0]+cos_ang*vec[1]])
@staticmethod
def distance_point_to_line(p:np.ndarray, a:np.ndarray, b:np.ndarray):
'''
Distance between point p and 2d line 'ab' (and side where p is)
Parameters
----------
a : ndarray
2D point that defines line
b : ndarray
2D point that defines line
p : ndarray
2D point
Returns
-------
distance : float
distance between line and point
side : str
if we are at a, looking at b, p may be at our "left" or "right"
'''
line_len = np.linalg.norm(b-a)
if line_len == 0: # assumes vertical line
dist = sdist = np.linalg.norm(p-a)
else:
sdist = np.cross(b-a,p-a)/line_len
dist = abs(sdist)
return dist, "left" if sdist>0 else "right"
@staticmethod
def distance_point_to_segment(p:np.ndarray, a:np.ndarray, b:np.ndarray):
''' Distance from point p to 2d line segment 'ab' '''
ap = p-a
ab = b-a
ad = Math_Ops.vector_projection(ap,ab)
# Is d in ab? We can find k in (ad = k * ab) without computing any norm
# we use the largest dimension of ab to avoid division by 0
k = ad[0]/ab[0] if abs(ab[0])>abs(ab[1]) else ad[1]/ab[1]
if k <= 0: return np.linalg.norm(ap)
elif k >= 1: return np.linalg.norm(p-b)
else: return np.linalg.norm(p-(ad + a)) # p-d
@staticmethod
def distance_point_to_ray(p:np.ndarray, ray_start:np.ndarray, ray_direction:np.ndarray):
''' Distance from point p to 2d ray '''
rp = p-ray_start
rd = Math_Ops.vector_projection(rp,ray_direction)
# Is d in ray? We can find k in (rd = k * ray_direction) without computing any norm
# we use the largest dimension of ray_direction to avoid division by 0
k = rd[0]/ray_direction[0] if abs(ray_direction[0])>abs(ray_direction[1]) else rd[1]/ray_direction[1]
if k <= 0: return np.linalg.norm(rp)
else: return np.linalg.norm(p-(rd + ray_start)) # p-d
@staticmethod
def closest_point_on_ray_to_point(p:np.ndarray, ray_start:np.ndarray, ray_direction:np.ndarray):
''' Point on ray closest to point p '''
rp = p-ray_start
rd = Math_Ops.vector_projection(rp,ray_direction)
# Is d in ray? We can find k in (rd = k * ray_direction) without computing any norm
# we use the largest dimension of ray_direction to avoid division by 0
k = rd[0]/ray_direction[0] if abs(ray_direction[0])>abs(ray_direction[1]) else rd[1]/ray_direction[1]
if k <= 0: return ray_start
else: return rd + ray_start
@staticmethod
def does_circle_intersect_segment(p:np.ndarray, r, a:np.ndarray, b:np.ndarray):
''' Returns true if circle (center p, radius r) intersect 2d line segment '''
ap = p-a
ab = b-a
ad = Math_Ops.vector_projection(ap,ab)
# Is d in ab? We can find k in (ad = k * ab) without computing any norm
# we use the largest dimension of ab to avoid division by 0
k = ad[0]/ab[0] if abs(ab[0])>abs(ab[1]) else ad[1]/ab[1]
if k <= 0: return np.dot(ap,ap) <= r*r
elif k >= 1: return np.dot(p-b,p-b) <= r*r
dp = p-(ad + a)
return np.dot(dp,dp) <= r*r
@staticmethod
def vector_projection(a:np.ndarray, b:np.ndarray):
''' Vector projection of a onto b '''
b_dot = np.dot(b,b)
return b * np.dot(a,b) / b_dot if b_dot != 0 else b
@staticmethod
def do_noncollinear_segments_intersect(a,b,c,d):
'''
Check if 2d line segment 'ab' intersects with noncollinear 2d line segment 'cd'
Explanation: https://www.geeksforgeeks.org/check-if-two-given-line-segments-intersect/
'''
ccw = lambda a,b,c: (c[1]-a[1]) * (b[0]-a[0]) > (b[1]-a[1]) * (c[0]-a[0])
return ccw(a,c,d) != ccw(b,c,d) and ccw(a,b,c) != ccw(a,b,d)
@staticmethod
def intersection_segment_opp_goal(a:np.ndarray, b:np.ndarray):
''' Computes the intersection point of 2d segment 'ab' and the opponents' goal (front line) '''
vec_x = b[0]-a[0]
# Collinear intersections are not accepted
if vec_x == 0: return None
k = (15.01-a[0])/vec_x
# No collision
if k < 0 or k > 1: return None
intersection_pt = a + (b-a) * k
if -1.01 <= intersection_pt[1] <= 1.01:
return intersection_pt
else:
return None
@staticmethod
def intersection_circle_opp_goal(p:np.ndarray, r):
'''
Computes the intersection segment of circle (center p, radius r) and the opponents' goal (front line)
Only the y coordinates are returned since the x coordinates are always equal to 15
'''
x_dev = abs(15-p[0])
if x_dev > r:
return None # no intersection with x=15
y_dev = sqrt(r*r - x_dev*x_dev)
p1 = max(p[1] - y_dev, -1.01)
p2 = min(p[1] + y_dev, 1.01)
if p1 == p2:
return p1 # return the y coordinate of a single intersection point
elif p2 < p1:
return None # no intersection
else:
return p1, p2 # return the y coordinates of the intersection segment
@staticmethod
def distance_point_to_opp_goal(p:np.ndarray):
''' Distance between point 'p' and the opponents' goal (front line) '''
if p[1] < -1.01:
return np.linalg.norm( p-(15,-1.01) )
elif p[1] > 1.01:
return np.linalg.norm( p-(15, 1.01) )
else:
return abs(15-p[0])
@staticmethod
def circle_line_segment_intersection(circle_center, circle_radius, pt1, pt2, full_line=True, tangent_tol=1e-9):
""" Find the points at which a circle intersects a line-segment. This can happen at 0, 1, or 2 points.
:param circle_center: The (x, y) location of the circle center
:param circle_radius: The radius of the circle
:param pt1: The (x, y) location of the first point of the segment
:param pt2: The (x, y) location of the second point of the segment
:param full_line: True to find intersections along full line - not just in the segment. False will just return intersections within the segment.
:param tangent_tol: Numerical tolerance at which we decide the intersections are close enough to consider it a tangent
:return Sequence[Tuple[float, float]]: A list of length 0, 1, or 2, where each element is a point at which the circle intercepts a line segment.
Note: We follow: http://mathworld.wolfram.com/Circle-LineIntersection.html
"""
(p1x, p1y), (p2x, p2y), (cx, cy) = pt1, pt2, circle_center
(x1, y1), (x2, y2) = (p1x - cx, p1y - cy), (p2x - cx, p2y - cy)
dx, dy = (x2 - x1), (y2 - y1)
dr = (dx ** 2 + dy ** 2)**.5
big_d = x1 * y2 - x2 * y1
discriminant = circle_radius ** 2 * dr ** 2 - big_d ** 2
if discriminant < 0: # No intersection between circle and line
return []
else: # There may be 0, 1, or 2 intersections with the segment
intersections = [
(cx + (big_d * dy + sign * (-1 if dy < 0 else 1) * dx * discriminant**.5) / dr ** 2,
cy + (-big_d * dx + sign * abs(dy) * discriminant**.5) / dr ** 2)
for sign in ((1, -1) if dy < 0 else (-1, 1))] # This makes sure the order along the segment is correct
if not full_line: # If only considering the segment, filter out intersections that do not fall within the segment
fraction_along_segment = [
(xi - p1x) / dx if abs(dx) > abs(dy) else (yi - p1y) / dy for xi, yi in intersections]
intersections = [pt for pt, frac in zip(
intersections, fraction_along_segment) if 0 <= frac <= 1]
# If line is tangent to circle, return just one point (as both intersections have same location)
if len(intersections) == 2 and abs(discriminant) <= tangent_tol:
return [intersections[0]]
else:
return intersections
# adapted from https://stackoverflow.com/questions/3252194/numpy-and-line-intersections
@staticmethod
def get_line_intersection(a1, a2, b1, b2):
"""
Returns the point of intersection of the lines passing through a2,a1 and b2,b1.
a1: [x, y] a point on the first line
a2: [x, y] another point on the first line
b1: [x, y] a point on the second line
b2: [x, y] another point on the second line
"""
s = np.vstack([a1,a2,b1,b2]) # s for stacked
h = np.hstack((s, np.ones((4, 1)))) # h for homogeneous
l1 = np.cross(h[0], h[1]) # get first line
l2 = np.cross(h[2], h[3]) # get second line
x, y, z = np.cross(l1, l2) # point of intersection
if z == 0: # lines are parallel
return np.array([float('inf'), float('inf')])
return np.array([x/z, y/z],float)
|
2302_81918214/robocup3D
|
FCPCodebase-main/math_ops/Math_Ops.py
|
Python
|
agpl-3.0
| 13,674
|
from math import asin, atan2, pi, sqrt
import numpy as np
class Matrix_3x3():
def __init__(self, matrix = None) -> None:
'''
Constructor examples:
a = Matrix_3x3( ) # create identity matrix
b = Matrix_3x3( [[1,1,1],[2,2,2],[3,3,3]] ) # manually initialize matrix
c = Matrix_3x3( [1,1,1,2,2,2,3,3,3] ) # manually initialize matrix
d = Matrix_3x3( b ) # copy constructor
'''
if matrix is None:
self.m = np.identity(3)
elif type(matrix) == Matrix_3x3:
self.m = np.copy(matrix.m)
else:
self.m = np.asarray(matrix)
self.m.shape = (3,3) #reshape if needed, throw error if impossible
self.rotation_shortcuts={(1,0,0):self.rotate_x_rad, (-1, 0, 0):self._rotate_x_neg_rad,
(0,1,0):self.rotate_y_rad, ( 0,-1, 0):self._rotate_y_neg_rad,
(0,0,1):self.rotate_z_rad, ( 0, 0,-1):self._rotate_z_neg_rad}
@classmethod
def from_rotation_deg(cls, euler_vec):
'''
Create rotation matrix from Euler angles, in degrees.
Rotation order: RotZ*RotY*RotX
Parameters
----------
euler_vec : array_like, length 3
vector with Euler angles (x,y,z) aka (roll, pitch, yaw)
Example
----------
Matrix_3x3.from_rotation_deg((roll,pitch,yaw)) # Creates: RotZ(yaw)*RotY(pitch)*RotX(roll)
'''
mat = cls().rotate_z_deg(euler_vec[2], True).rotate_y_deg(euler_vec[1], True).rotate_x_deg(euler_vec[0], True)
return mat
def get_roll_deg(self):
''' Get angle around the x-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''
if self.m[2,1] == 0 and self.m[2,2] == 0:
return 180
return atan2(self.m[2,1], self.m[2,2]) * 180 / pi
def get_pitch_deg(self):
''' Get angle around the y-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''
return atan2(-self.m[2,0], sqrt(self.m[2,1]*self.m[2,1] + self.m[2,2]*self.m[2,2])) * 180 / pi
def get_yaw_deg(self):
''' Get angle around the z-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''
if self.m[1,0] == 0 and self.m[0,0] == 0:
return atan2(self.m[0,1], self.m[1,1]) * 180 / pi
return atan2(self.m[1,0], self.m[0,0]) * 180 / pi
def get_inclination_deg(self):
''' Get inclination of z-axis in relation to reference z-axis '''
return 90 - (asin(self.m[2,2]) * 180 / pi)
def rotate_deg(self, rotation_vec, rotation_deg, in_place=False):
'''
Rotates the current rotation matrix
Parameters
----------
rotation_vec : array_like, length 3
rotation vector
rotation_rad : float
rotation in degrees
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_3x3
self is returned if in_place is True
'''
return self.rotate_rad(rotation_vec, rotation_deg * (pi/180) , in_place)
def rotate_rad(self, rotation_vec, rotation_rad, in_place=False):
'''
Rotates the current rotation matrix
Parameters
----------
rotation_vec : array_like, length 3
rotation vector
rotation_rad : float
rotation in radians
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_3x3
self is returned if in_place is True
'''
if rotation_rad == 0: return
shortcut = self.rotation_shortcuts.get(tuple(a for a in rotation_vec))
if shortcut:
return shortcut(rotation_rad, in_place)
c = np.math.cos(rotation_rad)
c1 = 1 - c
s = np.math.sin(rotation_rad)
x = rotation_vec[0]
y = rotation_vec[1]
z = rotation_vec[2]
xxc1 = x * x * c1
yyc1 = y * y * c1
zzc1 = z * z * c1
xyc1 = x * y * c1
xzc1 = x * z * c1
yzc1 = y * z * c1
xs = x * s
ys = y * s
zs = z * s
mat = np.array([
[xxc1 + c, xyc1 - zs, xzc1 + ys],
[xyc1 + zs, yyc1 + c, yzc1 - xs],
[xzc1 - ys, yzc1 + xs, zzc1 + c]])
return self.multiply(mat, in_place)
def _rotate_x_neg_rad(self, rotation_rad, in_place=False):
self.rotate_x_rad(-rotation_rad, in_place)
def _rotate_y_neg_rad(self, rotation_rad, in_place=False):
self.rotate_y_rad(-rotation_rad, in_place)
def _rotate_z_neg_rad(self, rotation_rad, in_place=False):
self.rotate_z_rad(-rotation_rad, in_place)
def rotate_x_rad(self, rotation_rad, in_place=False):
'''
Rotates the current rotation matrix around the x-axis
Parameters
----------
rotation_rad : float
rotation in radians
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_3x3
self is returned if in_place is True
'''
if rotation_rad == 0:
return self if in_place else Matrix_3x3(self)
c = np.math.cos(rotation_rad)
s = np.math.sin(rotation_rad)
mat = np.array([
[1, 0, 0],
[0, c,-s],
[0, s, c]])
return self.multiply(mat, in_place)
def rotate_y_rad(self, rotation_rad, in_place=False):
'''
Rotates the current rotation matrix around the y-axis
Parameters
----------
rotation_rad : float
rotation in radians
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_3x3
self is returned if in_place is True
'''
if rotation_rad == 0:
return self if in_place else Matrix_3x3(self)
c = np.math.cos(rotation_rad)
s = np.math.sin(rotation_rad)
mat = np.array([
[ c, 0, s],
[ 0, 1, 0],
[-s, 0, c]])
return self.multiply(mat, in_place)
def rotate_z_rad(self, rotation_rad, in_place=False):
'''
Rotates the current rotation matrix around the z-axis
Parameters
----------
rotation_rad : float
rotation in radians
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_3x3
self is returned if in_place is True
'''
if rotation_rad == 0:
return self if in_place else Matrix_3x3(self)
c = np.math.cos(rotation_rad)
s = np.math.sin(rotation_rad)
mat = np.array([
[ c,-s, 0],
[ s, c, 0],
[ 0, 0, 1]])
return self.multiply(mat, in_place)
def rotate_x_deg(self, rotation_deg, in_place=False):
'''
Rotates the current rotation matrix around the x-axis
Parameters
----------
rotation_rad : float
rotation in degrees
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_3x3
self is returned if in_place is True
'''
return self.rotate_x_rad(rotation_deg * (pi/180), in_place)
def rotate_y_deg(self, rotation_deg, in_place=False):
'''
Rotates the current rotation matrix around the y-axis
Parameters
----------
rotation_rad : float
rotation in degrees
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_3x3
self is returned if in_place is True
'''
return self.rotate_y_rad(rotation_deg * (pi/180), in_place)
def rotate_z_deg(self, rotation_deg, in_place=False):
'''
Rotates the current rotation matrix around the z-axis
Parameters
----------
rotation_rad : float
rotation in degrees
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_3x3
self is returned if in_place is True
'''
return self.rotate_z_rad(rotation_deg * (pi/180), in_place)
def invert(self, in_place=False):
'''
Inverts the current rotation matrix
Parameters
----------
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_3x3
self is returned if in_place is True
'''
if in_place:
self.m = np.linalg.inv(self.m)
return self
else:
return Matrix_3x3(np.linalg.inv(self.m))
def multiply(self,mat, in_place=False, reverse_order=False):
'''
Multiplies the current rotation matrix by mat
Parameters
----------
mat : Matrix_3x3 or array_like
multiplier matrix or 3D vector
in_place: bool, optional
- True: the internal matrix is changed in-place
- False: a new matrix is returned and the current one is not changed (default)
reverse_order: bool, optional
- False: self * mat
- True: mat * self
Returns
-------
result : Matrix_3x3 | array_like
Matrix_3x3 is returned if mat is a matrix (self is returned if in_place is True);
a 3D vector is returned if mat is a vector
'''
# get array from matrix object or convert to numpy array (if needed)
mat = mat.m if type(mat) == Matrix_3x3 else np.asarray(mat)
a,b = (mat, self.m) if reverse_order else (self.m, mat)
if mat.ndim == 1:
return np.matmul(a, b) # multiplication by 3D vector
elif in_place:
np.matmul(a, b, self.m) # multiplication by matrix, in place
return self
else: # multiplication by matrix, return new Matrix_3x3
return Matrix_3x3(np.matmul(a, b))
|
2302_81918214/robocup3D
|
FCPCodebase-main/math_ops/Matrix_3x3.py
|
Python
|
agpl-3.0
| 11,408
|
from math import asin, atan2, pi, sqrt
from math_ops.Math_Ops import Math_Ops as M
from math_ops.Matrix_3x3 import Matrix_3x3
import numpy as np
class Matrix_4x4():
def __init__(self, matrix = None) -> None:
'''
Constructor examples:
a = Matrix_4x4( ) # create identity matrix
b = Matrix_4x4( [[1,1,1,1],[2,2,2,2],[3,3,3,3],[4,4,4,4]] ) # manually initialize matrix
c = Matrix_4x4( [1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4] ) # manually initialize matrix
d = Matrix_4x4( b ) # copy constructor
'''
if matrix is None:
self.m = np.identity(4)
elif type(matrix) == Matrix_4x4:
self.m = np.copy(matrix.m)
elif type(matrix) == Matrix_3x3:
self.m = np.identity(4)
self.m[0:3,0:3] = matrix.m
else:
self.m = np.asarray(matrix)
self.m.shape = (4,4) #reshape if needed, throw error if impossible
@classmethod
def from_translation(cls, translation_vec):
'''
Create transformation matrix from translation_vec translation
e.g. Matrix_4x4.from_translation((a,b,c))
output: [[1,0,0,a],[0,1,0,b],[0,0,1,c],[0,0,0,1]]
'''
mat = np.identity(4)
mat[0:3,3] = translation_vec
return cls(mat)
@classmethod
def from_3x3_and_translation(cls, mat3x3:Matrix_3x3, translation_vec):
'''
Create transformation matrix from rotation matrix (3x3) and translation
e.g. Matrix_4x4.from_3x3_and_translation(r,(a,b,c))
output: [[r00,r01,r02,a],[r10,r11,r12,b],[r20,r21,r22,c],[0,0,0,1]]
'''
mat = np.identity(4)
mat[0:3,0:3] = mat3x3.m
mat[0:3,3] = translation_vec
return cls(mat)
def translate(self, translation_vec, in_place=False):
'''
Translates the current transformation matrix
Parameters
----------
translation_vec : array_like, length 3
translation vector
in_place: bool, optional
* True: the internal matrix is changed in-place
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_4x4
self is returned if in_place is True
'''
vec = np.array([*translation_vec,1])# conversion to 4D vector
np.matmul(self.m, vec, out=vec) # compute only 4th column
if in_place:
self.m[:,3] = vec
return self
else:
ret = Matrix_4x4(self.m)
ret.m[:,3] = vec
return ret
def get_translation(self):
''' Get translation vector (x,y,z) '''
return self.m[0:3,3] # return view
def get_x(self):
return self.m[0,3]
def get_y(self):
return self.m[1,3]
def get_z(self):
return self.m[2,3]
def get_rotation_4x4(self):
''' Get Matrix_4x4 without translation '''
mat = Matrix_4x4(self)
mat.m[0:3,3] = 0
return mat
def get_rotation(self):
''' Get rotation Matrix_3x3 '''
return Matrix_3x3(self.m[0:3,0:3])
def get_distance(self):
''' Get translation vector length '''
return np.linalg.norm(self.m[0:3,3])
def get_roll_deg(self):
''' Get angle around the x-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''
if self.m[2,1] == 0 and self.m[2,2] == 0:
return 180
return atan2(self.m[2,1], self.m[2,2]) * 180 / pi
def get_pitch_deg(self):
''' Get angle around the y-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''
return atan2(-self.m[2,0], sqrt(self.m[2,1]*self.m[2,1] + self.m[2,2]*self.m[2,2])) * 180 / pi
def get_yaw_deg(self):
''' Get angle around the z-axis in degrees, Rotation order: RotZ*RotY*RotX=Rot '''
if self.m[1,0] == 0 and self.m[0,0] == 0:
return atan2(self.m[0,1], self.m[1,1]) * 180 / pi
return atan2(self.m[1,0], self.m[0,0]) * 180 / pi
def get_inclination_deg(self):
''' Get inclination of z-axis in relation to reference z-axis '''
return 90 - (asin(np.clip(self.m[2,2],-1,1)) * 180 / pi)
def rotate_deg(self, rotation_vec, rotation_deg, in_place=False):
'''
Rotates the current transformation matrix
Parameters
----------
rotation_vec : array_like, length 3
rotation vector
rotation_rad : float
rotation in degrees
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_4x4
self is returned if in_place is True
'''
return self.rotate_rad(rotation_vec, rotation_deg * (pi/180) , in_place)
def rotate_rad(self, rotation_vec, rotation_rad, in_place=False):
'''
Rotates the current transformation matrix
Parameters
----------
rotation_vec : array_like, length 3
rotation vector
rotation_rad : float
rotation in radians
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_4x4
self is returned if in_place is True
'''
if rotation_rad == 0:
return self if in_place else Matrix_4x4(self)
# shortcuts for rotation around 1 axis
if rotation_vec[0]==0:
if rotation_vec[1]==0:
if rotation_vec[2]==1:
return self.rotate_z_rad(rotation_rad, in_place)
elif rotation_vec[2]==-1:
return self.rotate_z_rad(-rotation_rad, in_place)
elif rotation_vec[2]==0:
if rotation_vec[1]==1:
return self.rotate_y_rad(rotation_rad, in_place)
elif rotation_vec[1]==-1:
return self.rotate_y_rad(-rotation_rad, in_place)
elif rotation_vec[1]==0 and rotation_vec[2]==0:
if rotation_vec[0]==1:
return self.rotate_x_rad(rotation_rad, in_place)
elif rotation_vec[0]==-1:
return self.rotate_x_rad(-rotation_rad, in_place)
c = np.math.cos(rotation_rad)
c1 = 1 - c
s = np.math.sin(rotation_rad)
x = rotation_vec[0]
y = rotation_vec[1]
z = rotation_vec[2]
xxc1 = x * x * c1
yyc1 = y * y * c1
zzc1 = z * z * c1
xyc1 = x * y * c1
xzc1 = x * z * c1
yzc1 = y * z * c1
xs = x * s
ys = y * s
zs = z * s
mat = np.array([
[xxc1 + c, xyc1 - zs, xzc1 + ys, 0],
[xyc1 + zs, yyc1 + c, yzc1 - xs, 0],
[xzc1 - ys, yzc1 + xs, zzc1 + c, 0],
[0, 0, 0, 1]])
return self.multiply(mat, in_place)
def rotate_x_rad(self, rotation_rad, in_place=False):
'''
Rotates the current transformation matrix around the x-axis
Parameters
----------
rotation_rad : float
rotation in radians
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_4x4
self is returned if in_place is True
'''
if rotation_rad == 0:
return self if in_place else Matrix_4x4(self)
c = np.math.cos(rotation_rad)
s = np.math.sin(rotation_rad)
mat = np.array([
[1, 0, 0, 0],
[0, c,-s, 0],
[0, s, c, 0],
[0, 0, 0, 1]])
return self.multiply(mat, in_place)
def rotate_y_rad(self, rotation_rad, in_place=False):
'''
Rotates the current transformation matrix around the y-axis
Parameters
----------
rotation_rad : float
rotation in radians
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_4x4
self is returned if in_place is True
'''
if rotation_rad == 0:
return self if in_place else Matrix_4x4(self)
c = np.math.cos(rotation_rad)
s = np.math.sin(rotation_rad)
mat = np.array([
[ c, 0, s, 0],
[ 0, 1, 0, 0],
[-s, 0, c, 0],
[ 0, 0, 0, 1]])
return self.multiply(mat, in_place)
def rotate_z_rad(self, rotation_rad, in_place=False):
'''
Rotates the current transformation matrix around the z-axis
Parameters
----------
rotation_rad : float
rotation in radians
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_4x4
self is returned if in_place is True
'''
if rotation_rad == 0:
return self if in_place else Matrix_4x4(self)
c = np.math.cos(rotation_rad)
s = np.math.sin(rotation_rad)
mat = np.array([
[ c,-s, 0, 0],
[ s, c, 0, 0],
[ 0, 0, 1, 0],
[ 0, 0, 0, 1]])
return self.multiply(mat, in_place)
def rotate_x_deg(self, rotation_deg, in_place=False):
'''
Rotates the current transformation matrix around the x-axis
Parameters
----------
rotation_rad : float
rotation in degrees
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_4x4
self is returned if in_place is True
'''
return self.rotate_x_rad(rotation_deg * (pi/180), in_place)
def rotate_y_deg(self, rotation_deg, in_place=False):
'''
Rotates the current transformation matrix around the y-axis
Parameters
----------
rotation_rad : float
rotation in degrees
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_4x4
self is returned if in_place is True
'''
return self.rotate_y_rad(rotation_deg * (pi/180), in_place)
def rotate_z_deg(self, rotation_deg, in_place=False):
'''
Rotates the current transformation matrix around the z-axis
Parameters
----------
rotation_rad : float
rotation in degrees
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_4x4
self is returned if in_place is True
'''
return self.rotate_z_rad(rotation_deg * (pi/180), in_place)
def invert(self, in_place=False):
'''
Inverts the current transformation matrix
Parameters
----------
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed
Returns
-------
result : Matrix_4x4
self is returned if in_place is True
'''
if in_place:
self.m = np.linalg.inv(self.m)
return self
else:
return Matrix_4x4(np.linalg.inv(self.m))
def multiply(self,mat, in_place=False):
'''
Multiplies the current transformation matrix by mat
Parameters
----------
mat : Matrix_4x4 or array_like
multiplier matrix or 3D vector
in_place: bool, optional
* True: the internal matrix is changed in-place (default)
* False: a new matrix is returned and the current one is not changed (if mat is a 4x4 matrix)
Returns
-------
result : Matrix_4x4 | array_like
Matrix_4x4 is returned if mat is a matrix (self is returned if in_place is True);
a 3D vector is returned if mat is a vector
'''
if type(mat) == Matrix_4x4:
mat = mat.m
else:
mat = np.asarray(mat) # conversion to array, if needed
if mat.ndim == 1: # multiplication by 3D vector
vec = np.append(mat,1) # conversion to 4D vector
return np.matmul(self.m, vec)[0:3] # conversion to 3D vector
if in_place:
np.matmul(self.m, mat, self.m)
return self
else:
return Matrix_4x4(np.matmul(self.m, mat))
def __call__(self,mat, is_spherical=False):
'''
Multiplies the current transformation matrix by mat and returns a new matrix or vector
Parameters
----------
mat : Matrix_4x4 or array_like
multiplier matrix or 3D vector
is_spherical : bool
only relevant if mat is a 3D vector, True if it uses spherical coordinates
Returns
-------
result : Matrix_4x4 | array_like
Matrix_4x4 is returned if mat is a matrix;
a 3D vector is returned if mat is a vector
'''
if is_spherical and mat.ndim == 1: mat = M.deg_sph2cart(mat)
return self.multiply(mat,False)
|
2302_81918214/robocup3D
|
FCPCodebase-main/math_ops/Matrix_4x4.py
|
Python
|
agpl-3.0
| 14,338
|
import numpy as np
def run_mlp(obs, weights, activation_function="tanh"):
'''
Run multilayer perceptron using numpy
Parameters
----------
obs : ndarray
float32 array with neural network inputs
weights : list
list of MLP layers of type (bias, kernel)
activation_function : str
activation function for hidden layers
set to "none" to disable
'''
obs = obs.astype(np.float32, copy=False)
out = obs
for w in weights[:-1]: # for each hidden layer
out = np.matmul(w[1],out) + w[0]
if activation_function == "tanh":
np.tanh(out, out=out)
elif activation_function != "none":
raise NotImplementedError
return np.matmul(weights[-1][1],out) + weights[-1][0] # final layer
|
2302_81918214/robocup3D
|
FCPCodebase-main/math_ops/Neural_Network.py
|
Python
|
agpl-3.0
| 798
|
from os import path, listdir, getcwd, cpu_count
from os.path import join, realpath, dirname, isfile, isdir, getmtime
from scripts.commons.UI import UI
import __main__
import argparse,json,sys
import pickle
import subprocess
class Script():
ROOT_DIR = path.dirname(path.dirname(realpath( join(getcwd(), dirname(__file__))) )) # project root directory
def __init__(self, cpp_builder_unum=0) -> None:
'''
Arguments specification
-----------------------
- To add new arguments, edit the information below
- After changing information below, the config.json file must be manually deleted
- In other modules, these arguments can be accessed by their 1-letter ID
'''
# list of arguments: 1-letter ID, Description, Hardcoded default
self.options = {'i': ('Server Hostname/IP', 'localhost'),
'p': ('Agent Port', '3100'),
'm': ('Monitor Port', '3200'),
't': ('Team Name', 'FCPortugal'),
'u': ('Uniform Number', '1'),
'r': ('Robot Type', '1'),
'P': ('Penalty Shootout', '0'),
'F': ('magmaFatProxy', '0'),
'D': ('Debug Mode', '1')}
# list of arguments: 1-letter ID, data type, choices
self.op_types = {'i': (str, None),
'p': (int, None),
'm': (int, None),
't': (str, None),
'u': (int, range(1,12)),
'r': (int, [0,1,2,3,4]),
'P': (int, [0,1]),
'F': (int, [0,1]),
'D': (int, [0,1])}
'''
End of arguments specification
'''
self.read_or_create_config()
#advance help text position
formatter = lambda prog: argparse.HelpFormatter(prog,max_help_position=52)
parser = argparse.ArgumentParser(formatter_class=formatter)
o = self.options
t = self.op_types
for id in self.options: # shorter metavar for aesthetic reasons
parser.add_argument(f"-{id}", help=f"{o[id][0]:30}[{o[id][1]:20}]", type=t[id][0], nargs='?', default=o[id][1], metavar='X', choices=t[id][1])
self.args = parser.parse_args()
if getattr(sys, 'frozen', False): # disable debug mode when running from binary
self.args.D = 0
self.players = [] # list of created players
Script.build_cpp_modules(exit_on_build = (cpp_builder_unum != 0 and cpp_builder_unum != self.args.u))
if self.args.D:
try:
print(f"\nNOTE: for help run \"python {__main__.__file__} -h\"")
except:
pass
columns = [[],[],[]]
for key, value in vars(self.args).items():
columns[0].append(o[key][0])
columns[1].append(o[key][1])
columns[2].append(value)
UI.print_table(columns, ["Argument","Default at /config.json","Active"], alignment=["<","^","^"])
def read_or_create_config(self) -> None:
if not path.isfile('config.json'): # save hardcoded default values if file does not exist
with open("config.json", "w") as f:
json.dump(self.options, f, indent=4)
else: # load user-defined values (that can be overwritten by command-line arguments)
if path.getsize("config.json") == 0: # wait for possible write operation when launching multiple agents
from time import sleep
sleep(1)
if path.getsize("config.json") == 0: # abort after 1 second
print("Aborting: 'config.json' is empty. Manually verify and delete if still empty.")
exit()
with open("config.json", "r") as f:
self.options = json.loads(f.read())
@staticmethod
def build_cpp_modules(special_environment_prefix=[], exit_on_build=False):
'''
Build C++ modules in folder /cpp using Pybind11
Parameters
----------
special_environment_prefix : `list`
command prefix to run a given command in the desired environment
useful to compile C++ modules for different python interpreter versions (other than default version)
Conda Env. example: ['conda', 'run', '-n', 'myEnv']
If [] the default python interpreter is used as compilation target
exit_on_build : bool
exit if there is something to build (so that only 1 player per team builds c++ modules)
'''
cpp_path = Script.ROOT_DIR + "/cpp/"
exclusions = ["__pycache__"]
cpp_modules = [d for d in listdir(cpp_path) if isdir(join(cpp_path, d)) and d not in exclusions]
if not cpp_modules: return #no modules to build
python_cmd = f"python{sys.version_info.major}.{sys.version_info.minor}" # "python3" can select the wrong version, this prevents that
def init():
print("--------------------------\nC++ modules:",cpp_modules)
try:
process = subprocess.Popen(special_environment_prefix+[python_cmd, "-m", "pybind11", "--includes"], stdout=subprocess.PIPE)
(includes, err) = process.communicate()
process.wait()
except:
print(f"Error while executing child program: '{python_cmd} -m pybind11 --includes'")
exit()
includes = includes.decode().rstrip() # strip trailing newlines (and other whitespace chars)
print("Using Pybind11 includes: '",includes,"'",sep="")
return includes
nproc = str(cpu_count())
zero_modules = True
for module in cpp_modules:
module_path = join(cpp_path, module)
# skip module if there is no Makefile (typical distribution case)
if not isfile(join(module_path, "Makefile")):
continue
# skip module in certain conditions
if isfile(join(module_path, module+".so")) and isfile(join(module_path, module+".c_info")):
with open(join(module_path, module+".c_info"), 'rb') as f:
info = pickle.load(f)
if info == python_cmd:
code_mod_time = max(getmtime(join(module_path, f)) for f in listdir(module_path) if f.endswith(".cpp") or f.endswith(".h"))
bin_mod_time = getmtime(join(module_path, module+".so"))
if bin_mod_time + 30 > code_mod_time: # favor not building with a margin of 30s (scenario: we unzip the fcpy project, including the binaries, the modification times are all similar)
continue
# init: print stuff & get Pybind11 includes
if zero_modules:
if exit_on_build:
print("There are C++ modules to build. This player is not allowed to build. Aborting.")
exit()
zero_modules = False
includes = init()
# build module
print(f'{f"Building: {module}... ":40}',end='',flush=True)
process = subprocess.Popen(['make', '-j'+nproc, 'PYBIND_INCLUDES='+includes], stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=module_path)
(output, err) = process.communicate()
exit_code = process.wait()
if exit_code == 0:
print("success!")
with open(join(module_path, module+".c_info"),"wb") as f: # save python version
pickle.dump(python_cmd, f, protocol=4) # protocol 4 is backward compatible with Python 3.4
else:
print("Aborting! Building errors:")
print(output.decode(), err.decode())
exit()
if not zero_modules:
print("All modules were built successfully!\n--------------------------")
def batch_create(self, agent_cls, args_per_player):
''' Creates batch of agents '''
for a in args_per_player:
self.players.append( agent_cls(*a) )
def batch_execute_agent(self, index : slice = slice(None)):
'''
Executes agent normally (including commit & send)
Parameters
----------
index : slice
subset of agents
(e.g. index=slice(1,2) will select the second agent)
(e.g. index=slice(1,3) will select the second and third agents)
by default, all agents are selected
'''
for p in self.players[index]:
p.think_and_send()
def batch_execute_behavior(self, behavior, index : slice = slice(None)):
'''
Executes behavior
Parameters
----------
behavior : str
name of behavior to execute
index : slice
subset of agents
(e.g. index=slice(1,2) will select the second agent)
(e.g. index=slice(1,3) will select the second and third agents)
by default, all agents are selected
'''
for p in self.players[index]:
p.behavior.execute(behavior)
def batch_commit_and_send(self, index : slice = slice(None)):
'''
Commits & sends data to server
Parameters
----------
index : slice
subset of agents
(e.g. index=slice(1,2) will select the second agent)
(e.g. index=slice(1,3) will select the second and third agents)
by default, all agents are selected
'''
for p in self.players[index]:
p.scom.commit_and_send( p.world.robot.get_command() )
def batch_receive(self, index : slice = slice(None), update=True):
'''
Waits for server messages
Parameters
----------
index : slice
subset of agents
(e.g. index=slice(1,2) will select the second agent)
(e.g. index=slice(1,3) will select the second and third agents)
by default, all agents are selected
update : bool
update world state based on information received from server
if False, the agent becomes unaware of itself and its surroundings
which is useful for reducing cpu resources for dummy agents in demonstrations
'''
for p in self.players[index]:
p.scom.receive(update)
def batch_commit_beam(self, pos2d_and_rotation, index : slice = slice(None)):
'''
Beam all player to 2D position with a given rotation
Parameters
----------
pos2d_and_rotation : `list`
iterable of 2D positions and rotations e.g. [(0,0,45),(-5,0,90)]
index : slice
subset of agents
(e.g. index=slice(1,2) will select the second agent)
(e.g. index=slice(1,3) will select the second and third agents)
by default, all agents are selected
'''
for p, pos_rot in zip(self.players[index], pos2d_and_rotation):
p.scom.commit_beam(pos_rot[0:2],pos_rot[2])
def batch_unofficial_beam(self, pos3d_and_rotation, index : slice = slice(None)):
'''
Beam all player to 3D position with a given rotation
Parameters
----------
pos3d_and_rotation : `list`
iterable of 3D positions and rotations e.g. [(0,0,0.5,45),(-5,0,0.5,90)]
index : slice
subset of agents
(e.g. index=slice(1,2) will select the second agent)
(e.g. index=slice(1,3) will select the second and third agents)
by default, all agents are selected
'''
for p, pos_rot in zip(self.players[index], pos3d_and_rotation):
p.scom.unofficial_beam(pos_rot[0:3],pos_rot[3])
def batch_terminate(self, index : slice = slice(None)):
'''
Close all sockets connected to the agent port
For scripts where the agent lives until the application ends, this is not needed
Parameters
----------
index : slice
subset of agents
(e.g. index=slice(1,2) will select the second agent)
(e.g. index=slice(1,3) will select the second and third agents)
by default, all agents are selected
'''
for p in self.players[index]:
p.terminate()
del self.players[index] # delete selection
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/commons/Script.py
|
Python
|
agpl-3.0
| 12,715
|
import subprocess
class Server():
def __init__(self, first_server_p, first_monitor_p, n_servers) -> None:
try:
import psutil
self.check_running_servers(psutil, first_server_p, first_monitor_p, n_servers)
except ModuleNotFoundError:
print("Info: Cannot check if the server is already running, because the psutil module was not found")
self.first_server_p = first_server_p
self.n_servers = n_servers
self.rcss_processes = []
# makes it easier to kill test servers without affecting train servers
cmd = "simspark" if n_servers == 1 else "rcssserver3d"
for i in range(n_servers):
self.rcss_processes.append(
subprocess.Popen((f"{cmd} --agent-port {first_server_p+i} --server-port {first_monitor_p+i}").split(),
stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT, start_new_session=True)
)
def check_running_servers(self, psutil, first_server_p, first_monitor_p, n_servers):
''' Check if any server is running on chosen ports '''
found = False
p_list = [p for p in psutil.process_iter() if p.cmdline() and p.name() in ["rcssserver3d","simspark"]]
range1 = (first_server_p, first_server_p + n_servers)
range2 = (first_monitor_p,first_monitor_p + n_servers)
bad_processes = []
for p in p_list:
# currently ignoring remaining default port when only one of the ports is specified (uncommon scenario)
ports = [int(arg) for arg in p.cmdline()[1:] if arg.isdigit()]
if len(ports) == 0:
ports = [3100,3200] # default server ports (changing this is unlikely)
conflicts = [str(port) for port in ports if (
(range1[0] <= port < range1[1]) or (range2[0] <= port < range2[1]) )]
if len(conflicts)>0:
if not found:
print("\nThere are already servers running on the same port(s)!")
found = True
bad_processes.append(p)
print(f"Port(s) {','.join(conflicts)} already in use by \"{' '.join(p.cmdline())}\" (PID:{p.pid})")
if found:
print()
while True:
inp = input("Enter 'kill' to kill these processes or ctrl+c to abort. ")
if inp == "kill":
for p in bad_processes:
p.kill()
return
def kill(self):
for p in self.rcss_processes:
p.kill()
print(f"Killed {self.n_servers} rcssserver3d processes starting at {self.first_server_p}")
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/commons/Server.py
|
Python
|
agpl-3.0
| 2,704
|
from datetime import datetime, timedelta
from itertools import count
from os import listdir
from os.path import isdir, join, isfile
from scripts.commons.UI import UI
from shutil import copy
from stable_baselines3 import PPO
from stable_baselines3.common.base_class import BaseAlgorithm
from stable_baselines3.common.callbacks import EvalCallback, CheckpointCallback, CallbackList, BaseCallback
from typing import Callable
from world.World import World
from xml.dom import minidom
import numpy as np
import os, time, math, csv, select, sys
import pickle
import xml.etree.ElementTree as ET
class Train_Base():
def __init__(self, script) -> None:
'''
When training with multiple environments (multiprocessing):
The server port is incremented as follows:
self.server_p, self.server_p+1, self.server_p+2, ...
We add +1000 to the initial monitor port, so than we can have more than 100 environments:
self.monitor_p+1000, self.monitor_p+1001, self.monitor_p+1002, ...
When testing we use self.server_p and self.monitor_p
'''
args = script.args
self.script = script
self.ip = args.i
self.server_p = args.p # (initial) server port
self.monitor_p = args.m # monitor port when testing
self.monitor_p_1000 = args.m + 1000 # initial monitor port when training
self.robot_type = args.r
self.team = args.t
self.uniform = args.u
self.cf_last_time = 0
self.cf_delay = 0
self.cf_target_period = World.STEPTIME # target simulation speed while testing (default: real-time)
@staticmethod
def prompt_user_for_model():
gyms_logs_path = "./scripts/gyms/logs/"
folders = [f for f in listdir(gyms_logs_path) if isdir(join(gyms_logs_path, f))]
folders.sort(key=lambda f: os.path.getmtime(join(gyms_logs_path, f)), reverse=True) # sort by modification date
while True:
try:
folder_name = UI.print_list(folders,prompt="Choose folder (ctrl+c to return): ")[1]
except KeyboardInterrupt:
print()
return None # ctrl+c
folder_dir = os.path.join(gyms_logs_path, folder_name)
models = [m[:-4] for m in listdir(folder_dir) if isfile(join(folder_dir, m)) and m.endswith(".zip")]
if not models:
print("The chosen folder does not contain any .zip file!")
continue
models.sort(key=lambda m: os.path.getmtime(join(folder_dir, m+".zip")), reverse=True) # sort by modification date
try:
model_name = UI.print_list(models,prompt="Choose model (ctrl+c to return): ")[1]
break
except KeyboardInterrupt:
print()
return {"folder_dir":folder_dir, "folder_name":folder_name, "model_file":os.path.join(folder_dir, model_name+".zip")}
def control_fps(self, read_input = False):
''' Add delay to control simulation speed '''
if read_input:
speed = input()
if speed == '':
self.cf_target_period = 0
print(f"Changed simulation speed to MAX")
else:
if speed == '0':
inp = input("Paused. Set new speed or '' to use previous speed:")
if inp != '':
speed = inp
try:
speed = int(speed)
assert speed >= 0
self.cf_target_period = World.STEPTIME * 100 / speed
print(f"Changed simulation speed to {speed}%")
except:
print("""Train_Base.py:
Error: To control the simulation speed, enter a non-negative integer.
To disable this control module, use test_model(..., enable_FPS_control=False) in your gym environment.""")
now = time.time()
period = now - self.cf_last_time
self.cf_last_time = now
self.cf_delay += (self.cf_target_period - period)*0.9
if self.cf_delay > 0:
time.sleep(self.cf_delay)
else:
self.cf_delay = 0
def test_model(self, model:BaseAlgorithm, env, log_path:str=None, model_path:str=None, max_episodes=0, enable_FPS_control=True, verbose=1):
'''
Test model and log results
Parameters
----------
model : BaseAlgorithm
Trained model
env : Env
Gym-like environment
log_path : str
Folder where statistics file is saved, default is `None` (no file is saved)
model_path : str
Folder where it reads evaluations.npz to plot it and create evaluations.csv, default is `None` (no plot, no csv)
max_episodes : int
Run tests for this number of episodes
Default is 0 (run until user aborts)
verbose : int
0 - no output (except if enable_FPS_control=True)
1 - print episode statistics
'''
if model_path is not None:
assert os.path.isdir(model_path), f"{model_path} is not a valid path"
self.display_evaluations(model_path)
if log_path is not None:
assert os.path.isdir(log_path), f"{log_path} is not a valid path"
# If file already exists, don't overwrite
if os.path.isfile(log_path + "/test.csv"):
for i in range(1000):
p = f"{log_path}/test_{i:03}.csv"
if not os.path.isfile(p):
log_path = p
break
else:
log_path += "/test.csv"
with open(log_path, 'w') as f:
f.write("reward,ep. length,rew. cumulative avg., ep. len. cumulative avg.\n")
print("Train statistics are saved to:", log_path)
if enable_FPS_control: # control simulation speed (using non blocking user input)
print("\nThe simulation speed can be changed by sending a non-negative integer\n"
"(e.g. '50' sets speed to 50%, '0' pauses the simulation, '' sets speed to MAX)\n")
ep_reward = 0
ep_length = 0
rewards_sum = 0
reward_min = math.inf
reward_max = -math.inf
ep_lengths_sum = 0
ep_no = 0
obs = env.reset()
while True:
action, _states = model.predict(obs, deterministic=True)
obs, reward, done, info = env.step(action)
ep_reward += reward
ep_length += 1
if enable_FPS_control: # control simulation speed (using non blocking user input)
self.control_fps(select.select([sys.stdin], [], [], 0)[0])
if done:
obs = env.reset()
rewards_sum += ep_reward
ep_lengths_sum += ep_length
reward_max = max(ep_reward, reward_max)
reward_min = min(ep_reward, reward_min)
ep_no += 1
avg_ep_lengths = ep_lengths_sum/ep_no
avg_rewards = rewards_sum/ep_no
if verbose > 0:
print( f"\rEpisode: {ep_no:<3} Ep.Length: {ep_length:<4.0f} Reward: {ep_reward:<6.2f} \n",
end=f"--AVERAGE-- Ep.Length: {avg_ep_lengths:<4.0f} Reward: {avg_rewards:<6.2f} (Min: {reward_min:<6.2f} Max: {reward_max:<6.2f})", flush=True)
if log_path is not None:
with open(log_path, 'a') as f:
writer = csv.writer(f)
writer.writerow([ep_reward, ep_length, avg_rewards, avg_ep_lengths])
if ep_no == max_episodes:
return
ep_reward = 0
ep_length = 0
def learn_model(self, model:BaseAlgorithm, total_steps:int, path:str, eval_env=None, eval_freq=None, eval_eps=5, save_freq=None, backup_env_file=None, export_name=None):
'''
Learn Model for a specific number of time steps
Parameters
----------
model : BaseAlgorithm
Model to train
total_steps : int
The total number of samples (env steps) to train on
path : str
Path where the trained model is saved
If the path already exists, an incrementing number suffix is added
eval_env : Env
Environment to periodically test the model
Default is None (no periodical evaluation)
eval_freq : int
Evaluate the agent every X steps
Default is None (no periodical evaluation)
eval_eps : int
Evaluate the agent for X episodes (both eval_env and eval_freq must be defined)
Default is 5
save_freq : int
Saves model at every X steps
Default is None (no periodical checkpoint)
backup_gym_file : str
Generates backup of environment file in model's folder
Default is None (no backup)
export_name : str
If export_name and save_freq are defined, a model is exported every X steps
Default is None (no export)
Returns
-------
model_path : str
Directory where model was actually saved (considering incremental suffix)
Notes
-----
If `eval_env` and `eval_freq` were specified:
- The policy will be evaluated in `eval_env` every `eval_freq` steps
- Evaluation results will be saved in `path` and shown at the end of training
- Every time the results improve, the model is saved
'''
start = time.time()
start_date = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
# If path already exists, add suffix to avoid overwriting
if os.path.isdir(path):
for i in count():
p = path.rstrip("/")+f'_{i:03}/'
if not os.path.isdir(p):
path = p
break
os.makedirs(path)
# Backup environment file
if backup_env_file is not None:
backup_file = os.path.join(path, os.path.basename(backup_env_file))
copy(backup_env_file, backup_file)
evaluate = bool(eval_env is not None and eval_freq is not None)
# Create evaluation callback
eval_callback = None if not evaluate else EvalCallback(eval_env, n_eval_episodes=eval_eps, eval_freq=eval_freq, log_path=path,
best_model_save_path=path, deterministic=True, render=False)
# Create custom callback to display evaluations
custom_callback = None if not evaluate else Cyclic_Callback(eval_freq, lambda:self.display_evaluations(path,True))
# Create checkpoint callback
checkpoint_callback = None if save_freq is None else CheckpointCallback(save_freq=save_freq, save_path=path, name_prefix="model", verbose=1)
# Create custom callback to export checkpoint models
export_callback = None if save_freq is None or export_name is None else Export_Callback(save_freq, path, export_name)
callbacks = CallbackList([c for c in [eval_callback, custom_callback, checkpoint_callback, export_callback] if c is not None])
model.learn( total_timesteps=total_steps, callback=callbacks )
model.save( os.path.join(path, "last_model") )
# Display evaluations if they exist
if evaluate:
self.display_evaluations(path)
# Display timestamps + Model path
end_date = datetime.now().strftime('%d/%m/%Y %H:%M:%S')
duration = timedelta(seconds=int(time.time()-start))
print(f"Train start: {start_date}")
print(f"Train end: {end_date}")
print(f"Train duration: {duration}")
print(f"Model path: {path}")
# Append timestamps to backup environment file
if backup_env_file is not None:
with open(backup_file, 'a') as f:
f.write(f"\n# Train start: {start_date}\n")
f.write( f"# Train end: {end_date}\n")
f.write( f"# Train duration: {duration}")
return path
def display_evaluations(self, path, save_csv=False):
eval_npz = os.path.join(path, "evaluations.npz")
if not os.path.isfile(eval_npz):
return
console_width = 80
console_height = 18
symb_x = "\u2022"
symb_o = "\u007c"
symb_xo = "\u237f"
with np.load(eval_npz) as data:
time_steps = data["timesteps"]
results_raw = np.mean(data["results"],axis=1)
ep_lengths_raw = np.mean(data["ep_lengths"],axis=1)
sample_no = len(results_raw)
xvals = np.linspace(0, sample_no-1, 80)
results = np.interp(xvals, range(sample_no), results_raw)
ep_lengths = np.interp(xvals, range(sample_no), ep_lengths_raw)
results_limits = np.min(results), np.max(results)
ep_lengths_limits = np.min(ep_lengths), np.max(ep_lengths)
results_discrete = np.digitize(results, np.linspace(results_limits[0]-1e-5, results_limits[1]+1e-5, console_height+1))-1
ep_lengths_discrete = np.digitize(ep_lengths, np.linspace(0, ep_lengths_limits[1]+1e-5, console_height+1))-1
matrix = np.zeros((console_height, console_width, 2), int)
matrix[results_discrete[0] ][0][0] = 1 # draw 1st column
matrix[ep_lengths_discrete[0]][0][1] = 1 # draw 1st column
rng = [[results_discrete[0], results_discrete[0]], [ep_lengths_discrete[0], ep_lengths_discrete[0]]]
# Create continuous line for both plots
for k in range(2):
for i in range(1,console_width):
x = [results_discrete, ep_lengths_discrete][k][i]
if x > rng[k][1]:
rng[k] = [rng[k][1]+1, x]
elif x < rng[k][0]:
rng[k] = [x, rng[k][0]-1]
else:
rng[k] = [x,x]
for j in range(rng[k][0],rng[k][1]+1):
matrix[j][i][k] = 1
print(f'{"-"*console_width}')
for l in reversed(range(console_height)):
for c in range(console_width):
if np.all(matrix[l][c] == 0): print(end=" ")
elif np.all(matrix[l][c] == 1): print(end=symb_xo)
elif matrix[l][c][0] == 1: print(end=symb_x)
else: print(end=symb_o)
print()
print(f'{"-"*console_width}')
print(f"({symb_x})-reward min:{results_limits[0]:11.2f} max:{results_limits[1]:11.2f}")
print(f"({symb_o})-ep. length min:{ep_lengths_limits[0]:11.0f} max:{ep_lengths_limits[1]:11.0f} {time_steps[-1]/1000:15.0f}k steps")
print(f'{"-"*console_width}')
# save CSV
if save_csv:
eval_csv = os.path.join(path, "evaluations.csv")
with open(eval_csv, 'a+') as f:
writer = csv.writer(f)
if sample_no == 1:
writer.writerow(["time_steps", "reward ep.", "length"])
writer.writerow([time_steps[-1],results_raw[-1],ep_lengths_raw[-1]])
def generate_slot_behavior(self, path, slots, auto_head:bool, XML_name):
'''
Function that generates the XML file for the optimized slot behavior, overwriting previous files
'''
file = os.path.join( path, XML_name )
# create the file structure
auto_head = '1' if auto_head else '0'
EL_behavior = ET.Element('behavior',{'description':'Add description to XML file', "auto_head":auto_head})
for i,s in enumerate(slots):
EL_slot = ET.SubElement(EL_behavior, 'slot', {'delta':str(s[0]/1000)})
for j in s[1]: # go through all joint indices
ET.SubElement(EL_slot, 'move', {'id':str(j), 'angle':str(s[2][j])})
# create XML file
xml_rough = ET.tostring( EL_behavior, 'utf-8' )
xml_pretty = minidom.parseString(xml_rough).toprettyxml(indent=" ")
with open(file, "w") as x:
x.write(xml_pretty)
print(file, "was created!")
@staticmethod
def linear_schedule(initial_value: float) -> Callable[[float], float]:
'''
Linear learning rate schedule
Parameters
----------
initial_value : float
Initial learning rate
Returns
-------
schedule : Callable[[float], float]
schedule that computes current learning rate depending on remaining progress
'''
def func(progress_remaining: float) -> float:
'''
Compute learning rate according to current progress
Parameters
----------
progress_remaining : float
Progress will decrease from 1 (beginning) to 0
Returns
-------
learning_rate : float
Learning rate according to current progress
'''
return progress_remaining * initial_value
return func
@staticmethod
def export_model(input_file, output_file, add_sufix=True):
'''
Export model weights to binary file
Parameters
----------
input_file : str
Input file, compatible with algorithm
output_file : str
Output file, including directory
add_sufix : bool
If true, a suffix is appended to the file name: output_file + "_{index}.pkl"
'''
# If file already exists, don't overwrite
if add_sufix:
for i in count():
f = f"{output_file}_{i:03}.pkl"
if not os.path.isfile(f):
output_file = f
break
model = PPO.load(input_file)
weights = model.policy.state_dict() # dictionary containing network layers
w = lambda name : weights[name].detach().cpu().numpy() # extract weights from policy
var_list = []
for i in count(0,2): # add hidden layers (step=2 because that's how SB3 works)
if f"mlp_extractor.policy_net.{i}.bias" not in weights:
break
var_list.append([w(f"mlp_extractor.policy_net.{i}.bias"), w(f"mlp_extractor.policy_net.{i}.weight"), "tanh"])
var_list.append( [w("action_net.bias"), w("action_net.weight"), "none"] ) # add final layer
with open(output_file,"wb") as f:
pickle.dump(var_list, f, protocol=4) # protocol 4 is backward compatible with Python 3.4
class Cyclic_Callback(BaseCallback):
''' Stable baselines custom callback '''
def __init__(self, freq, function):
super(Cyclic_Callback, self).__init__(1)
self.freq = freq
self.function = function
def _on_step(self) -> bool:
if self.n_calls % self.freq == 0:
self.function()
return True # If the callback returns False, training is aborted early
class Export_Callback(BaseCallback):
''' Stable baselines custom callback '''
def __init__(self, freq, load_path, export_name):
super(Export_Callback, self).__init__(1)
self.freq = freq
self.load_path = load_path
self.export_name = export_name
def _on_step(self) -> bool:
if self.n_calls % self.freq == 0:
path = os.path.join(self.load_path, f"model_{self.num_timesteps}_steps.zip")
Train_Base.export_model(path, f"./scripts/gyms/export/{self.export_name}")
return True # If the callback returns False, training is aborted early
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/commons/Train_Base.py
|
Python
|
agpl-3.0
| 20,033
|
from itertools import zip_longest
from math import inf
import math
import numpy as np
import shutil
class UI():
console_width = 80
console_height = 24
@staticmethod
def read_particle(prompt, str_options, dtype=str, interval=[-inf,inf]):
'''
Read particle from user from a given dtype or from a str_options list
Parameters
----------
prompt : `str`
prompt to show user before reading input
str_options : `list`
list of str options (in addition to dtype if dtype is not str)
dtype : `class`
if dtype is str, then user must choose a value from str_options, otherwise it can also send a dtype value
interval : `list`
[>=min,<max] interval for numeric dtypes
Returns
-------
choice : `int` or dtype
index of str_options (int) or value (dtype)
is_str_option : `bool`
True if `choice` is an index from str_options
'''
# Check if user has no choice
if dtype is str and len(str_options) == 1:
print(prompt, str_options[0], sep="")
return 0, True
elif dtype is int and interval[0] == interval[1]-1:
print(prompt, interval[0], sep="")
return interval[0], False
while True:
inp = input(prompt)
if inp in str_options:
return str_options.index(inp), True
if dtype is not str:
try:
inp = dtype(inp)
if inp >= interval[0] and inp < interval[1]:
return inp, False
except:
pass
print("Error: illegal input! Options:", str_options, f" or {dtype}" if dtype != str else "")
@staticmethod
def read_int(prompt, min, max):
'''
Read int from user in a given interval
:param prompt: prompt to show user before reading input
:param min: minimum input (inclusive)
:param max: maximum input (exclusive)
:return: choice
'''
while True:
inp = input(prompt)
try:
inp = int(inp)
assert inp >= min and inp < max
return inp
except:
print(f"Error: illegal input! Choose number between {min} and {max-1}")
@staticmethod
def print_table(data, titles=None, alignment=None, cols_width=None, cols_per_title=None, margins=None, numbering=None, prompt=None):
'''
Print table
Parameters
----------
data : `list`
list of columns, where each column is a list of items
titles : `list`
list of titles for each column, default is `None` (no titles)
alignment : `list`
list of alignments per column (excluding titles), default is `None` (left alignment for all cols)
cols_width : `list`
list of widths per column, default is `None` (fit to content)
Positive values indicate a fixed column width
Zero indicates that the column will fit its content
cols_per_title : `list`
maximum number of subcolumns per title, default is `None` (1 subcolumn per title)
margins : `list`
number of added leading and trailing spaces per column, default is `None` (margin=2 for all columns)
numbering : `list`
list of booleans per columns, indicating whether to assign numbers to each option
prompt : `str`
the prompt string, if given, is printed after the table before reading input
Returns
-------
index : `int`
returns global index of selected item (relative to table)
col_index : `int`
returns local index of selected item (relative to column)
column : `int`
returns number of column of selected item (starts at 0)
* if `numbering` or `prompt` are `None`, `None` is returned
Example
-------
titles = ["Name","Age"]
data = [[John,Graciete], [30,50]]
alignment = ["<","^"] # 1st column is left-aligned, 2nd is centered
cols_width = [10,5] # 1st column's width=10, 2nd column's width=5
margins = [3,3]
numbering = [True,False] # prints: [0-John,1-Graciete][30,50]
prompt = "Choose a person:"
'''
#--------------------------------------------- parameters
cols_no = len(data)
if alignment is None:
alignment = ["<"]*cols_no
if cols_width is None:
cols_width = [0]*cols_no
if numbering is None:
numbering = [False]*cols_no
any_numbering = False
else:
any_numbering = True
if margins is None:
margins = [2]*cols_no
# Fit column to content + margin, if required
subcol = [] # subcolumn length and widths
for i in range(cols_no):
subcol.append([[],[]])
if cols_width[i] == 0:
numbering_width = 4 if numbering[i] else 0
if cols_per_title is None or cols_per_title[i] < 2:
cols_width[i] = max([len(str(item))+numbering_width for item in data[i]]) + margins[i]*2
else:
subcol[i][0] = math.ceil(len(data[i])/cols_per_title[i]) # subcolumn maximum length
cols_per_title[i] = math.ceil(len(data[i])/subcol[i][0]) # reduce number of columns as needed
cols_width[i] = margins[i]*(1+cols_per_title[i]) - (1 if numbering[i] else 0) # remove one if numbering, same as when printing
for j in range(cols_per_title[i]):
subcol_data_width = max([len(str(item))+numbering_width for item in data[i][j*subcol[i][0]:j*subcol[i][0]+subcol[i][0]]])
cols_width[i] += subcol_data_width # add subcolumn data width to column width
subcol[i][1].append(subcol_data_width) # save subcolumn data width
if titles is not None: # expand to acomodate titles if needed
cols_width[i] = max(cols_width[i], len(titles[i]) + margins[i]*2 )
if any_numbering:
no_of_items=0
cumulative_item_per_col=[0] # useful for getting the local index
for i in range(cols_no):
assert type(data[i]) == list, "In function 'print_table', 'data' must be a list of lists!"
if numbering[i]:
data[i] = [f"{n+no_of_items:3}-{d}" for n,d in enumerate(data[i])]
no_of_items+=len(data[i])
cumulative_item_per_col.append(no_of_items)
table_width = sum(cols_width)+cols_no-1
#--------------------------------------------- col titles
print(f'{"="*table_width}')
if titles is not None:
for i in range(cols_no):
print(f'{titles[i]:^{cols_width[i]}}', end='|' if i < cols_no - 1 else '')
print()
for i in range(cols_no):
print(f'{"-"*cols_width[i]}', end='+' if i < cols_no - 1 else '')
print()
#--------------------------------------------- merge subcolumns
if cols_per_title is not None:
for i,col in enumerate(data):
if cols_per_title[i] < 2:
continue
for k in range(subcol[i][0]): # create merged items
col[k] = (" "*margins[i]).join( f'{col[item]:{alignment[i]}{subcol[i][1][subcol_idx]}}'
for subcol_idx, item in enumerate(range(k,len(col),subcol[i][0])) )
del col[subcol[i][0]:] # delete repeated items
#--------------------------------------------- col items
for line in zip_longest(*data):
for i,item in enumerate(line):
l_margin = margins[i]-1 if numbering[i] else margins[i] # adjust margins when there are numbered options
item = "" if item is None else f'{" "*l_margin}{item}{" "*margins[i]}' # add margins
print(f'{item:{alignment[i]}{cols_width[i]}}', end='')
if i < cols_no - 1:
print(end='|')
print(end="\n")
print(f'{"="*table_width}')
#--------------------------------------------- prompt
if prompt is None:
return None
if not any_numbering:
print(prompt)
return None
index = UI.read_int(prompt, 0, no_of_items)
for i,n in enumerate(cumulative_item_per_col):
if index < n:
return index, index-cumulative_item_per_col[i-1], i-1
raise ValueError('Failed to catch illegal input')
@staticmethod
def print_list(data, numbering=True, prompt=None, divider=" | ", alignment="<", min_per_col=6):
'''
Print list - prints list, using as many columns as possible
Parameters
----------
data : `list`
list of items
numbering : `bool`
assigns number to each option
prompt : `str`
the prompt string, if given, is printed after the table before reading input
divider : `str`
string that divides columns
alignment : `str`
f-string style alignment ( '<', '>', '^' )
min_per_col : int
avoid splitting columns with fewer items
Returns
-------
item : `int`, item
returns tuple with global index of selected item and the item object,
or `None` (if `numbering` or `prompt` are `None`)
'''
WIDTH = shutil.get_terminal_size()[0]
data_size = len(data)
items = []
items_len = []
#--------------------------------------------- Add numbers, margins and divider
for i in range(data_size):
number = f"{i}-" if numbering else ""
items.append( f"{divider}{number}{data[i]}" )
items_len.append( len(items[-1]) )
max_cols = np.clip((WIDTH+len(divider)) // min(items_len),1,math.ceil(data_size/max(min_per_col,1))) # width + len(divider) because it is not needed in last col
#--------------------------------------------- Check maximum number of columns, considering content width (min:1)
for i in range(max_cols,0,-1):
cols_width = []
cols_items = []
table_width = 0
a,b = divmod(data_size,i)
for col in range(i):
start = a*col + min(b,col)
end = start+a+(1 if col<b else 0)
cols_items.append( items[start:end] )
col_width = max(items_len[start:end])
cols_width.append( col_width )
table_width += col_width
if table_width <= WIDTH+len(divider):
break
table_width -= len(divider)
#--------------------------------------------- Print columns
print("="*table_width)
for row in range(math.ceil(data_size / i)):
for col in range(i):
content = cols_items[col][row] if len(cols_items[col]) > row else divider # print divider when there are no items
if col == 0:
l = len(divider)
print(end=f"{content[l:]:{alignment}{cols_width[col]-l}}") # remove divider from 1st col
else:
print(end=f"{content :{alignment}{cols_width[col] }}")
print()
print("="*table_width)
#--------------------------------------------- Prompt
if prompt is None:
return None
if numbering is None:
return None
else:
idx = UI.read_int( prompt, 0, data_size )
return idx, data[idx]
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/commons/UI.py
|
Python
|
agpl-3.0
| 12,182
|
from agent.Base_Agent import Base_Agent as Agent
from behaviors.custom.Step.Step import Step
from world.commons.Draw import Draw
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import SubprocVecEnv
from scripts.commons.Server import Server
from scripts.commons.Train_Base import Train_Base
from time import sleep
import os, gym
import numpy as np
'''
Objective:
Learn how to run forward using step primitive
----------
- class Basic_Run: implements an OpenAI custom gym
- class Train: implements algorithms to train a new model or test an existing model
'''
class Basic_Run(gym.Env):
def __init__(self, ip, server_p, monitor_p, r_type, enable_draw) -> None:
self.robot_type = r_type
# Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
self.player = Agent(ip, server_p, monitor_p, 1, self.robot_type, "Gym", True, enable_draw)
self.step_counter = 0 # to limit episode size
self.step_obj : Step = self.player.behavior.get_custom_behavior_object("Step") # Step behavior object
# State space
obs_size = 70
self.obs = np.zeros(obs_size, np.float32)
self.observation_space = gym.spaces.Box(low=np.full(obs_size,-np.inf,np.float32), high=np.full(obs_size,np.inf,np.float32), dtype=np.float32)
# Action space
MAX = np.finfo(np.float32).max
self.no_of_actions = act_size = 22
self.action_space = gym.spaces.Box(low=np.full(act_size,-MAX,np.float32), high=np.full(act_size,MAX,np.float32), dtype=np.float32)
# Step behavior defaults
self.step_default_dur = 7
self.step_default_z_span = 0.035
self.step_default_z_max = 0.70
# Place ball far away to keep landmarks in FoV (head follows ball while using Step behavior)
self.player.scom.unofficial_move_ball((14, 0, 0.042))
def observe(self, init=False):
r = self.player.world.robot
# index observation naive normalization
self.obs[0] = self.step_counter /100 # simple counter: 0,1,2,3...
self.obs[1] = r.loc_head_z *3 # z coordinate (torso)
self.obs[2] = r.loc_head_z_vel /2 # z velocity (torso)
self.obs[3] = r.imu_torso_orientation /50 # absolute orientation in deg
self.obs[4] = r.imu_torso_roll /15 # absolute torso roll in deg
self.obs[5] = r.imu_torso_pitch /15 # absolute torso pitch in deg
self.obs[6:9] = r.gyro /100 # gyroscope
self.obs[9:12] = r.acc /10 # accelerometer
self.obs[12:18] = r.frp.get('lf', (0,0,0,0,0,0)) # left foot: relative point of origin (p) and force vector (f) -> (px,py,pz,fx,fy,fz)*
self.obs[18:24] = r.frp.get('rf', (0,0,0,0,0,0)) # right foot: relative point of origin (p) and force vector (f) -> (px,py,pz,fx,fy,fz)*
self.obs[15:18] /= 100 # naive normalization of force vector
self.obs[21:24] /= 100 # naive normalization of force vector
self.obs[24:44] = r.joints_position[2:22] /100 # position of all joints except head & toes (for robot type 4)
self.obs[44:64] = r.joints_speed[2:22] /6.1395 # speed of all joints except head & toes (for robot type 4)
# *if foot is not touching the ground, then (px=0,py=0,pz=0,fx=0,fy=0,fz=0)
if init: # the walking parameters refer to the last parameters in effect (after a reset, they are pointless)
self.obs[64] = self.step_default_dur /10 # step duration in time steps
self.obs[65] = self.step_default_z_span *20 # vertical movement span
self.obs[66] = self.step_default_z_max # relative extension of support leg
self.obs[67] = 1 # step progress
self.obs[68] = 1 # 1 if left leg is active
self.obs[69] = 0 # 1 if right leg is active
else:
self.obs[64] = self.step_obj.step_generator.ts_per_step /10 # step duration in time steps
self.obs[65] = self.step_obj.step_generator.swing_height *20 # vertical movement span
self.obs[66] = self.step_obj.step_generator.max_leg_extension / self.step_obj.leg_length # relative extension of support leg
self.obs[67] = self.step_obj.step_generator.external_progress # step progress
self.obs[68] = float(self.step_obj.step_generator.state_is_left_active) # 1 if left leg is active
self.obs[69] = float(not self.step_obj.step_generator.state_is_left_active) # 1 if right leg is active
'''
Expected observations for walking parameters/state (example):
Time step R 0 1 2 0 1 2 3 4
Progress 1 0 .5 1 0 .25 .5 .75 1
Left leg active T F F F T T T T T
Parameters A A A B B B B B C
Example note: (A) has a step duration of 3ts, (B) has a step duration of 5ts
'''
return self.obs
def sync(self):
''' Run a single simulation step '''
r = self.player.world.robot
self.player.scom.commit_and_send( r.get_command() )
self.player.scom.receive()
def reset(self):
'''
Reset and stabilize the robot
Note: for some behaviors it would be better to reduce stabilization or add noise
'''
self.step_counter = 0
r = self.player.world.robot
for _ in range(25):
self.player.scom.unofficial_beam((-14,0,0.50),0) # beam player continuously (floating above ground)
self.player.behavior.execute("Zero_Bent_Knees")
self.sync()
# beam player to ground
self.player.scom.unofficial_beam((-14,0,r.beam_height),0)
r.joints_target_speed[0] = 0.01 # move head to trigger physics update (rcssserver3d bug when no joint is moving)
self.sync()
# stabilize on ground
for _ in range(7):
self.player.behavior.execute("Zero_Bent_Knees")
self.sync()
# memory variables
self.lastx = r.cheat_abs_pos[0]
self.act = np.zeros(self.no_of_actions,np.float32)
return self.observe(True)
def render(self, mode='human', close=False):
return
def close(self):
Draw.clear_all()
self.player.terminate()
def step(self, action):
r = self.player.world.robot
# exponential moving average
self.act = 0.4 * self.act + 0.6 * action
# execute Step behavior to extract the target positions of each leg (we will override these targets)
if self.step_counter == 0:
'''
The first time step will change the parameters of the next footstep
It uses default parameters so that the agent can anticipate the next generated pose
Reason: the agent decides the parameters during the previous footstep
'''
self.player.behavior.execute("Step", self.step_default_dur, self.step_default_z_span, self.step_default_z_max)
else:
step_zsp = np.clip(self.step_default_z_span + self.act[20]/300, 0, 0.07)
step_zmx = np.clip(self.step_default_z_max + self.act[21]/30, 0.6, 0.9)
self.player.behavior.execute("Step", self.step_default_dur, step_zsp, step_zmx)
# add action as residuals to Step behavior (the index of these actions is not the typical index because both head joints are excluded)
new_action = self.act[:20] * 2 # scale up actions to motivate exploration
new_action[[0,2,4,6,8,10]] += self.step_obj.values_l
new_action[[1,3,5,7,9,11]] += self.step_obj.values_r
new_action[12] -= 90 # arms down
new_action[13] -= 90 # arms down
new_action[16] += 90 # untwist arms
new_action[17] += 90 # untwist arms
new_action[18] += 90 # elbows at 90 deg
new_action[19] += 90 # elbows at 90 deg
r.set_joints_target_position_direct( # commit actions:
slice(2,22), # act on all joints except head & toes (for robot type 4)
new_action, # target joint positions
harmonize=False # there is no point in harmonizing actions if the targets change at every step
)
self.sync() # run simulation step
self.step_counter += 1
reward = r.cheat_abs_pos[0] - self.lastx
self.lastx = r.cheat_abs_pos[0]
# terminal state: the robot is falling or timeout
terminal = r.cheat_abs_pos[2] < 0.3 or self.step_counter > 300
return self.observe(), reward, terminal, {}
class Train(Train_Base):
def __init__(self, script) -> None:
super().__init__(script)
def train(self, args):
#--------------------------------------- Learning parameters
n_envs = min(16, os.cpu_count())
n_steps_per_env = 1024 # RolloutBuffer is of size (n_steps_per_env * n_envs)
minibatch_size = 64 # should be a factor of (n_steps_per_env * n_envs)
total_steps = 30000000
learning_rate = 3e-4
folder_name = f'Basic_Run_R{self.robot_type}'
model_path = f'./scripts/gyms/logs/{folder_name}/'
print("Model path:", model_path)
#--------------------------------------- Run algorithm
def init_env(i_env):
def thunk():
return Basic_Run( self.ip , self.server_p + i_env, self.monitor_p_1000 + i_env, self.robot_type, False )
return thunk
servers = Server( self.server_p, self.monitor_p_1000, n_envs+1 ) #include 1 extra server for testing
env = SubprocVecEnv( [init_env(i) for i in range(n_envs)] )
eval_env = SubprocVecEnv( [init_env(n_envs)] )
try:
if "model_file" in args: # retrain
model = PPO.load( args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env, batch_size=minibatch_size, learning_rate=learning_rate )
else: # train new model
model = PPO( "MlpPolicy", env=env, verbose=1, n_steps=n_steps_per_env, batch_size=minibatch_size, learning_rate=learning_rate, device="cpu" )
model_path = self.learn_model( model, total_steps, model_path, eval_env=eval_env, eval_freq=n_steps_per_env*20, save_freq=n_steps_per_env*200, backup_env_file=__file__ )
except KeyboardInterrupt:
sleep(1) # wait for child processes
print("\nctrl+c pressed, aborting...\n")
servers.kill()
return
env.close()
eval_env.close()
servers.kill()
def test(self, args):
# Uses different server and monitor ports
server = Server( self.server_p-1, self.monitor_p, 1 )
env = Basic_Run( self.ip, self.server_p-1, self.monitor_p, self.robot_type, True )
model = PPO.load( args["model_file"], env=env )
try:
self.export_model( args["model_file"], args["model_file"]+".pkl", False ) # Export to pkl to create custom behavior
self.test_model( model, env, log_path=args["folder_dir"], model_path=args["folder_dir"] )
except KeyboardInterrupt:
print()
env.close()
server.kill()
'''
The learning process takes several hours.
A video with the results can be seen at:
https://imgur.com/a/dC2V6Et
Stats:
- Avg. reward: 7.7
- Avg. ep. length: 5.5s (episode is limited to 6s)
- Max. reward: 9.3 (speed: 1.55m/s)
State space:
- Composed of all joint positions + torso height
- Stage of the underlying Step behavior
Reward:
- Displacement in the x-axis (it can be negative)
- Note that cheat and visual data is only updated every 3 steps
'''
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/gyms/Basic_Run.py
|
Python
|
agpl-3.0
| 11,861
|
from agent.Base_Agent import Base_Agent as Agent
from world.commons.Draw import Draw
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import SubprocVecEnv
from scripts.commons.Server import Server
from scripts.commons.Train_Base import Train_Base
from time import sleep
import os, gym
import numpy as np
'''
Objective:
Learn how to fall (simplest example)
----------
- class Fall: implements an OpenAI custom gym
- class Train: implements algorithms to train a new model or test an existing model
'''
class Fall(gym.Env):
def __init__(self, ip, server_p, monitor_p, r_type, enable_draw) -> None:
self.robot_type = r_type
# Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
self.player = Agent(ip, server_p, monitor_p, 1, self.robot_type, "Gym", True, enable_draw)
self.step_counter = 0 # to limit episode size
# State space
self.no_of_joints = self.player.world.robot.no_of_joints
self.obs = np.zeros(self.no_of_joints + 1, np.float32) # joints + torso height
self.observation_space = gym.spaces.Box(low=np.full(len(self.obs),-np.inf,np.float32), high=np.full(len(self.obs),np.inf,np.float32), dtype=np.float32)
# Action space
MAX = np.finfo(np.float32).max
no_of_actions = self.no_of_joints
self.action_space = gym.spaces.Box(low=np.full(no_of_actions,-MAX,np.float32), high=np.full(no_of_actions,MAX,np.float32), dtype=np.float32)
# Check if cheats are enabled
assert np.any(self.player.world.robot.cheat_abs_pos), "Cheats are not enabled! Run_Utils.py -> Server -> Cheats"
def observe(self):
r = self.player.world.robot
for i in range(self.no_of_joints):
self.obs[i] = r.joints_position[i] / 100 # naive scale normalization
self.obs[self.no_of_joints] = r.cheat_abs_pos[2] # head.z (alternative: r.loc_head_z)
return self.obs
def sync(self):
''' Run a single simulation step '''
r = self.player.world.robot
self.player.scom.commit_and_send( r.get_command() )
self.player.scom.receive()
def reset(self):
'''
Reset and stabilize the robot
Note: for some behaviors it would be better to reduce stabilization or add noise
'''
self.step_counter = 0
r = self.player.world.robot
for _ in range(25):
self.player.scom.unofficial_beam((-3,0,0.50),0) # beam player continuously (floating above ground)
self.player.behavior.execute("Zero")
self.sync()
# beam player to ground
self.player.scom.unofficial_beam((-3,0,r.beam_height),0)
r.joints_target_speed[0] = 0.01 # move head to trigger physics update (rcssserver3d bug when no joint is moving)
self.sync()
# stabilize on ground
for _ in range(7):
self.player.behavior.execute("Zero")
self.sync()
return self.observe()
def render(self, mode='human', close=False):
return
def close(self):
Draw.clear_all()
self.player.terminate()
def step(self, action):
r = self.player.world.robot
r.set_joints_target_position_direct( # commit actions:
slice(self.no_of_joints), # act on all available joints
action*10, # scale actions up to motivate early exploration
harmonize=False # there is no point in harmonizing actions if the targets change at every step
)
self.sync() # run simulation step
self.step_counter += 1
self.observe()
if self.obs[-1] < 0.15: # terminal state: the robot has fallen successfully
return self.obs, 1, True, {} # Reward: 1 (this reward will motivate a fast reaction if the return is discounted)
elif self.step_counter > 150: # terminal state: 3s passed and robot has not fallen (may be stuck)
return self.obs, 0, True, {}
else:
return self.obs, 0, False, {} # Reward: 0
class Train(Train_Base):
def __init__(self, script) -> None:
super().__init__(script)
def train(self, args):
#--------------------------------------- Learning parameters
n_envs = min(4, os.cpu_count())
n_steps_per_env = 128 # RolloutBuffer is of size (n_steps_per_env * n_envs) (*RV: >=2048)
minibatch_size = 64 # should be a factor of (n_steps_per_env * n_envs)
total_steps = 50000 # (*RV: >=10M)
learning_rate = 30e-4 # (*RV: 3e-4)
# *RV -> Recommended value for more complex environments
folder_name = f'Fall_R{self.robot_type}'
model_path = f'./scripts/gyms/logs/{folder_name}/'
print("Model path:", model_path)
#--------------------------------------- Run algorithm
def init_env(i_env):
def thunk():
return Fall( self.ip , self.server_p + i_env, self.monitor_p_1000 + i_env, self.robot_type, False )
return thunk
servers = Server( self.server_p, self.monitor_p_1000, n_envs+1 ) #include 1 extra server for testing
env = SubprocVecEnv( [init_env(i) for i in range(n_envs)] )
eval_env = SubprocVecEnv( [init_env(n_envs)] )
try:
if "model_file" in args: # retrain
model = PPO.load( args["model_file"], env=env, n_envs=n_envs, n_steps=n_steps_per_env, batch_size=minibatch_size, learning_rate=learning_rate )
else: # train new model
model = PPO( "MlpPolicy", env=env, verbose=1, n_steps=n_steps_per_env, batch_size=minibatch_size, learning_rate=learning_rate )
model_path = self.learn_model( model, total_steps, model_path, eval_env=eval_env, eval_freq=n_steps_per_env*10, save_freq=n_steps_per_env*20, backup_env_file=__file__ )
except KeyboardInterrupt:
sleep(1) # wait for child processes
print("\nctrl+c pressed, aborting...\n")
servers.kill()
return
env.close()
eval_env.close()
servers.kill()
def test(self, args):
# Uses different server and monitor ports
server = Server( self.server_p-1, self.monitor_p, 1 )
env = Fall( self.ip, self.server_p-1, self.monitor_p, self.robot_type, True )
model = PPO.load( args["model_file"], env=env )
try:
self.export_model( args["model_file"], args["model_file"]+".pkl", False ) # Export to pkl to create custom behavior
self.test_model( model, env, log_path=args["folder_dir"], model_path=args["folder_dir"] )
except KeyboardInterrupt:
print()
env.close()
server.kill()
'''
The learning process takes about 5 minutes.
A video with the results can be seen at:
https://imgur.com/a/KvpXS41
State space:
- Composed of all joint positions + torso height
- The number of joint positions is different for robot type 4, so the models are not interchangeable
- For this example, this problem can be avoided by using only the first 22 joints and actuators
Reward:
- The reward for falling is 1, which means that after a while every episode will have a r=1.
- What is the incetive for the robot to fall faster? Discounted return.
In every state, the algorithm will seek short-term rewards.
- During training, the best model is saved according to the average return, which is almost always 1.
Therefore, the last model will typically be superior for this example.
Expected evolution of episode length:
3s|o
|o
| o
| o
| oo
| ooooo
0.4s| oooooooooooooooo
|------------------------------> time
This example scales poorly with the number of CPUs because:
- It uses a small rollout buffer (n_steps_per_env * n_envs)
- The simulation workload is light
- For these reasons, the IPC overhead is significant
'''
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/gyms/Fall.py
|
Python
|
agpl-3.0
| 8,069
|
from agent.Base_Agent import Base_Agent as Agent
from pathlib import Path
from scripts.commons.Server import Server
from scripts.commons.Train_Base import Train_Base
from stable_baselines3 import PPO
from stable_baselines3.common.base_class import BaseAlgorithm
from stable_baselines3.common.vec_env import SubprocVecEnv
from time import sleep
from world.commons.Draw import Draw
import gym
import numpy as np
import os
'''
Objective:
Learn how to get up (4 variants, see line 157)
Optimize each keyframe of existing slot behaviors
----------
- class Get_Up: implements an OpenAI custom gym
- class Train: implements algorithms to train a new model or test an existing model
'''
class Get_Up(gym.Env):
def __init__(self, ip, server_p, monitor_p, r_type, fall_direction, enable_draw) -> None:
self.robot_type = r_type
self.fall_direction = fall_direction # 0:front, 1:left side, 2:right side, 3:back
self.player = Agent(ip, server_p, monitor_p, 1, self.robot_type, "Gym", True, enable_draw, [])
self.get_up_names = {0:"Get_Up_Front", 1:"Get_Up_Side_Left", 2:"Get_Up_Side_Right", 3:"Get_Up_Back"}
# Backup original slots
self.original_slots = []
for delta_ms, indices, angles in self.player.behavior.slot_engine.behaviors[self.get_up_names[self.fall_direction]]:
self.original_slots.append((delta_ms, indices, np.array(angles)))
self.obs = np.identity(len(self.original_slots)) # one-hot encoding for each slot
self.current_slot = 0
MAX = np.finfo(np.float32).max
self.action_space = gym.spaces.Box(low=np.full(11,-MAX,np.float32), high=np.full(11,MAX,np.float32), dtype=np.float32)
self.observation_space = gym.spaces.Box(low=np.zeros(len(self.obs),np.float32), high=np.ones(len(self.obs),np.float32), dtype=np.float32)
def fall(self):
r = self.player.world.robot
joint_indices = [r.J_LFOOT_PITCH,
r.J_RFOOT_PITCH,
r.J_LLEG_ROLL,
r.J_RLEG_ROLL]
if self.fall_direction == 0:
r.set_joints_target_position_direct(joint_indices, np.array([50,50,0,0]))
elif self.fall_direction == 1:
r.set_joints_target_position_direct(joint_indices, np.array([0,0,-20,20]))
elif self.fall_direction == 2:
r.set_joints_target_position_direct(joint_indices, np.array([0,0,20,-20]))
elif self.fall_direction == 3:
r.set_joints_target_position_direct(joint_indices, np.array([-20,-20,0,0]))
else:
raise ValueError
self.player.scom.commit_and_send( r.get_command() )
self.player.scom.receive()
def get_up(self):
r = self.player.world.robot
finished = self.player.behavior.execute(self.get_up_names[self.fall_direction])
self.player.scom.commit_and_send( r.get_command() )
self.player.scom.receive()
return finished
def other(self, behavior_name):
r = self.player.world.robot
self.player.behavior.execute(behavior_name)
self.player.scom.commit_and_send( r.get_command() )
self.player.scom.receive()
def reset(self):
self.player.scom.commit_beam((-3,0),0)
for _ in range(30): self.fall()
while self.player.world.robot.cheat_abs_pos[2] > 0.32: self.fall()
import random
t = random.randint(7,17) if self.fall_direction==0 else random.randint(10,20)
for _ in range(t): self.other("Zero")
self.current_slot = 0
return self.obs[self.current_slot]
def render(self, mode='human', close=False):
return
def close(self):
Draw.clear_all()
self.player.scom.close()
@staticmethod
def scale_action(action : np.ndarray):
new_action = np.zeros(len(action)*2-1,action.dtype)
new_action[0] = action[0] * 10
new_action[1:] = np.repeat(action[1:] * 3,2) # expand symmetrical actions
return new_action
@staticmethod
def get_22_angles(angles, indices):
new_angles = np.zeros(22, np.float32) # all joints except for toes
new_angles[indices] = angles # get all joints that are defined in the XML (otherwise, assume 0)
return new_angles
def step(self, action):
#action: 1 delta + 10 joints
r = self.player.world.robot
action = Get_Up.scale_action(action)
delta, indices, angles = self.original_slots[self.current_slot]
angles = Get_Up.get_22_angles(angles, indices)
angles[2:] += action[1:] # exclude head
new_delta = max((delta + action[0])//20*20, 20)
self.player.behavior.slot_engine.behaviors[self.get_up_names[self.fall_direction]][self.current_slot] = ( new_delta, slice(0,22), angles )
self.current_slot += 1
terminal = bool(self.current_slot == len(self.obs))
reward = 0
if terminal: # network has set values for all keyframes, now run behavior and evaluate
while not self.get_up():
reward -= 0.05
for _ in range(50):
self.other("Zero_Bent_Knees")
reward += r.cheat_abs_pos[2] * 0.95**abs(r.gyro[1])
print("rew:", reward)
obs = self.obs[0] # dummy observation
else:
obs = self.obs[self.current_slot]
return obs, reward, terminal, {}
class Train(Train_Base):
def __init__(self, script) -> None:
super().__init__(script)
self.fall_direction = 0 # 0:front, 1:left side, 2:right side, 3:back
def train(self, args):
n_envs = min(15, os.cpu_count())
n_steps_per_env = 72
minibatch_size = 72 # should be a factor of n_steps_per_env * n_envs
total_steps = 1000
learning_rate = 2e-4
folder_name = f'GetUp_R{self.robot_type}_Direction{self.fall_direction}'
model_path = f'./scripts/gyms/logs/{folder_name}/'
print("Model path:", model_path)
def init_env(i_env):
def thunk():
return Get_Up( self.ip , self.server_p + i_env, self.monitor_p_1000 + i_env, self.robot_type, self.fall_direction, False )
return thunk
servers = Server( self.server_p, self.monitor_p_1000, n_envs+1 ) #include 1 extra server for testing
env = SubprocVecEnv( [init_env(i) for i in range(n_envs)] )
eval_env = SubprocVecEnv( [init_env(n_envs)] )
try:
if "model_file" in args:
model = PPO.load( args["model_file"], env=env, n_envs=n_envs, n_steps=n_steps_per_env, batch_size=minibatch_size, learning_rate=learning_rate )
else:
model = PPO( "MlpPolicy", env=env, verbose=1, n_steps=n_steps_per_env, batch_size=minibatch_size, learning_rate=learning_rate )
model_path = self.learn_model( model, total_steps, model_path, eval_env=eval_env, eval_freq=n_steps_per_env*10, backup_env_file=__file__ )
except KeyboardInterrupt:
sleep(1) # wait for child processes
print("\nctrl+c pressed, aborting...\n")
servers.kill()
return
# Generate slot behavior XML
self.generate_get_up_behavior(model, model_path, eval_env.get_attr('original_slots')[0], "last_model.xml")
env.close()
eval_env.close()
servers.kill()
def test(self, args):
# Uses different server and monitor ports
server = Server( self.server_p-1, self.monitor_p, 1 )
env = Get_Up( self.ip, self.server_p-1, self.monitor_p, self.robot_type, self.fall_direction, True )
model = PPO.load( args["model_file"], env=env )
# Generate slot behavior XML
XML_name = Path(args["model_file"]).stem + ".xml"
if not os.path.isfile(os.path.join( args["folder_dir"], XML_name )):
self.generate_get_up_behavior(model, args["folder_dir"], env.original_slots, XML_name )
self.test_model( model, env, log_path=args["folder_dir"], model_path=args["folder_dir"] )
env.close()
server.kill()
def generate_get_up_behavior(self, model : BaseAlgorithm, folder_dir, original_slots, XML_name):
predictions = model.predict(np.identity(len(original_slots)),deterministic=True)[0]
slots = []
for i in range(len(predictions)):
pred = Get_Up.scale_action(predictions[i])
delta = max((original_slots[i][0] + pred[0])//20*20, 20)
angles = Get_Up.get_22_angles(original_slots[i][2], original_slots[i][1])
angles[2:] += pred[1:]
slots.append((delta, range(22), angles))
self.generate_slot_behavior( folder_dir, slots, False, XML_name )
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/gyms/Get_Up.py
|
Python
|
agpl-3.0
| 8,825
|
from agent.Base_Agent import Base_Agent as Agent
from scripts.commons.Script import Script
from time import sleep
class Beam():
def __init__(self, script:Script) -> None:
self.script = script
def ask_for_input(self,prompt, default):
try:
inp=input(prompt)
return float(inp)
except ValueError:
if inp != '':
print("Illegal input:", inp, "\n")
return default
def beam_and_update(self,x,y,rot):
r = self.player.world.robot
d = self.player.world.draw
d.annotation((x,y,0.7), f"x:{x} y:{y} r:{rot}", d.Color.yellow, "pos_label")
self.player.scom.unofficial_beam((x,y,r.beam_height),rot)
for _ in range(10): # run multiple times to beam and then simulate eventual collisions (e.g. goal posts)
sleep(0.03)
self.player.behavior.execute("Zero")
self.player.scom.commit_and_send( r.get_command() )
self.player.scom.receive()
def execute(self):
a = self.script.args
self.player = Agent(a.i, a.p, a.m, a.u, a.r, a.t) # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name
d = self.player.world.draw
self.player.scom.unofficial_set_play_mode("PlayOn")
# Draw grid
for x in range(-15,16):
for y in range(-10,11):
d.point((x,y), 6, d.Color.red, "grid", False)
d.flush("grid")
for _ in range(10): # Initialize
self.player.scom.send()
self.player.scom.receive()
print("\nBeam player to coordinates + orientation:")
x=y=a=0
while True: # Beam self.player to given position
x = self.ask_for_input(f"\nInput x coordinate ('' to send {x:5} again, ctrl+c to return): ",x)
self.beam_and_update(x,y,a)
y = self.ask_for_input( f"Input y coordinate ('' to send {y:5} again, ctrl+c to return): ",y)
self.beam_and_update(x,y,a)
a = self.ask_for_input( f"Orientation -180 to 180 ('' to send {a:5} again, ctrl+c to return): ",a)
self.beam_and_update(x,y,a)
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Beam.py
|
Python
|
agpl-3.0
| 2,197
|
from agent.Base_Agent import Base_Agent as Agent
from scripts.commons.Script import Script
from scripts.commons.UI import UI
class Behaviors():
def __init__(self,script:Script) -> None:
self.script = script
self.player : Agent = None
def ask_for_behavior(self):
names, descriptions = self.player.behavior.get_all_behaviors()
UI.print_table( [names,descriptions], ["Behavior Name","Description"], numbering=[True,False])
choice, is_str_opt = UI.read_particle('Choose behavior ("" to skip 2 time steps, "b" to beam, ctrl+c to return): ',["","b"],int,[0,len(names)])
if is_str_opt: return choice #skip 2 time steps or quit
return names[choice]
def sync(self):
self.player.scom.commit_and_send( self.player.world.robot.get_command() )
self.player.scom.receive()
def beam(self):
self.player.scom.unofficial_beam((-2.5,0,self.player.world.robot.beam_height),0)
for _ in range(5):
self.sync()
def execute(self):
a = self.script.args
self.player = Agent(a.i, a.p, a.m, a.u, a.r, a.t) # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name
behavior = self.player.behavior
self.beam()
self.player.scom.unofficial_set_play_mode("PlayOn")
# Special behaviors
special_behaviors = {"Step":(), "Basic_Kick":(0,), "Walk":((0.5,0),False,0,False,None), "Dribble":(None,None)}
while True:
behavior_name = self.ask_for_behavior()
if behavior_name == 0: # skip 2 time steps (user request)
self.sync()
self.sync()
elif behavior_name == 1: # beam
self.beam()
else:
if behavior_name in special_behaviors: # not using execute_to_completion to abort behavior after a timeout
duration = UI.read_int("For how many time steps [1,1000]? ", 1, 1001)
for _ in range(duration):
if behavior.execute(behavior_name, *special_behaviors[behavior_name]):
break # break if behavior ends
self.sync()
else:
behavior.execute_to_completion(behavior_name)
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Behaviors.py
|
Python
|
agpl-3.0
| 2,301
|
from time import sleep
from world.commons.Draw import Draw
class Drawings():
def __init__(self,script) -> None:
self.script = script
def execute(self):
# Creating a draw object is done automatically for each agent
# This is a shortcut to draw shapes without creating an agent
# Usually, we can access the object through player.world.draw
a = self.script.args
draw = Draw(True, 0, a.i, 32769)
print("\nPress ctrl+c to return.")
while True:
for i in range(100):
sleep(0.02)
draw.circle( (0,0),i/10,2, Draw.Color.green_light,"green")
draw.circle( (0,0),i/9,2, Draw.Color.red,"red")
draw.sphere( (0,0,5-i/20),0.2, Draw.Color.red,"ball" )
draw.annotation((0,0,1), "Hello!", Draw.Color.cyan, "text")
draw.arrow( (0,0,5), (0,0,5-i/25), 0.5, 4, Draw.Color.get(127,50,255), "my_arrow")
#draw pyramid
draw.polygon(((2,0,0),(3,0,0),(3,1,0),(2,1,0)), Draw.Color.blue, 255, "solid", False)
draw.line( (2,0,0), (2.5,0.5,1), 2, Draw.Color.cyan, "solid", False)
draw.line( (3,0,0), (2.5,0.5,1), 2, Draw.Color.cyan, "solid", False)
draw.line( (2,1,0), (2.5,0.5,1), 2, Draw.Color.cyan, "solid", False)
draw.line( (3,1,0), (2.5,0.5,1), 2, Draw.Color.cyan, "solid", True)
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Drawings.py
|
Python
|
agpl-3.0
| 1,485
|
from agent.Agent import Agent
from agent.Base_Agent import Base_Agent
from scripts.commons.Script import Script
import numpy as np
'''
Objective:
----------
Dribble and score
'''
class Dribble():
def __init__(self, script:Script) -> None:
self.script = script
def execute(self):
a = self.script.args
# Args: Server IP, Agent Port, Monitor Port, Uniform No., [Robot Type] (for Base_Agent), Team name, Enable Log, Enable Draw
self.script.batch_create(Base_Agent, ((a.i,a.p,a.m,a.u,a.r,a.t,True,True),)) # one dribbler
self.script.batch_create(Agent, ((a.i,a.p,a.m,u,"Opponent",False,False) for u in range(1,2))) # 1 opponent (normal agent)
p : Base_Agent = self.script.players[0]
p.path_manager.draw_options(enable_obstacles=True, enable_path=True)
behavior = p.behavior
w = p.world
r = w.robot
d = w.draw
p.scom.unofficial_beam((-3,0,r.beam_height),0)
p.scom.unofficial_set_play_mode("PlayOn")
print("\nPress ctrl+c to return.")
while True:
if w.play_mode == w.M_THEIR_KICKOFF:
p.scom.unofficial_set_play_mode("PlayOn")
# execute dribbler
if behavior.is_ready("Get_Up") or w.play_mode_group in [w.MG_ACTIVE_BEAM, w.MG_PASSIVE_BEAM]:
p.scom.unofficial_beam((*(w.ball_abs_pos[:2]-(1,0)),r.beam_height),0)
behavior.execute("Zero_Bent_Knees")
else:
behavior.execute("Dribble",None,None)
d.annotation(r.loc_head_position+(0,0,0.2),f"{np.linalg.norm(r.get_head_abs_vel(40)[:2]):.2f}",d.Color.white,"vel_annotation")
p.scom.commit_and_send( r.get_command() )
# execute opponents as normal agents
self.script.batch_execute_agent(slice(1,None))
# all players wait for server to send feedback
self.script.batch_receive()
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Dribble.py
|
Python
|
agpl-3.0
| 1,982
|
from agent.Base_Agent import Base_Agent as Agent
from scripts.commons.Script import Script
from world.commons.Draw import Draw
import numpy as np
class Fwd_Kinematics():
def __init__(self,script:Script) -> None:
self.script = script
self.cycle_duration = 200 #steps
def draw_cycle(self):
#Draw position of body parts
for _ in range(self.cycle_duration):
self.script.batch_execute_behavior("Squat")
self.script.batch_commit_and_send()
self.script.batch_receive()
p : Agent
for p in self.script.players:
if p.world.vision_is_up_to_date and not p.world.robot.loc_is_up_to_date:
p.world.draw.annotation(p.world.robot.cheat_abs_pos, "Not enough visual data! Using IMU", Draw.Color.red,"localization")
for key, val in p.world.robot.body_parts.items():
rp = val.transform.get_translation()
pos = p.world.robot.loc_head_to_field_transform(rp,False)
label_rp = np.array([rp[0]-0.0001,rp[1]*0.5,0])
label_rp /= np.linalg.norm(label_rp) / 0.4 #labels at 0.4m from body part
label = p.world.robot.loc_head_to_field_transform(rp+label_rp,False)
p.world.draw.line(pos,label,2,Draw.Color.green_light,key,False)
p.world.draw.annotation(label,key,Draw.Color.red,key)
rp = p.world.robot.body_parts['lfoot'].transform((0.08,0,0))
ap = p.world.robot.loc_head_to_field_transform(rp,False)
p.world.draw.line(ap,ap+(0,0,0.1),1,Draw.Color.red,"soup",False)
rp = p.world.robot.body_parts['lfoot'].transform((-0.08,0,0))
ap = p.world.robot.loc_head_to_field_transform(rp,False)
p.world.draw.line(ap,ap+(0,0,0.1),1,Draw.Color.red,"soup",False)
rp = p.world.robot.body_parts['lfoot'].transform((0,0.04,0))
ap = p.world.robot.loc_head_to_field_transform(rp,False)
p.world.draw.line(ap,ap+(0,0,0.1),1,Draw.Color.red,"soup",False)
rp = p.world.robot.body_parts['lfoot'].transform((0,-0.04,0))
ap = p.world.robot.loc_head_to_field_transform(rp,False)
p.world.draw.line(ap,ap+(0,0,0.1),1,Draw.Color.red,"soup",True)
Draw.clear_all()
#Draw position of joints
for _ in range(self.cycle_duration):
self.script.batch_execute_behavior("Squat")
self.script.batch_commit_and_send()
self.script.batch_receive()
for p in self.script.players:
if p.world.vision_is_up_to_date and not p.world.robot.loc_is_up_to_date:
p.world.draw.annotation(p.world.robot.cheat_abs_pos, "Not enough visual data! Using IMU", Draw.Color.red,"localization")
zstep = 0.05
label_z = [0,0,0,0,zstep,zstep,2*zstep,2*zstep,0,0,0,0,zstep,zstep,0,0,zstep,zstep,2*zstep,2*zstep,3*zstep,3*zstep,0,0]
for j, transf in enumerate(p.world.robot.joints_transform):
rp = transf.get_translation()
pos = p.world.robot.loc_head_to_field_transform(rp,False)
j_name = str(j)
label_rp = np.array([rp[0]-0.0001,rp[1]*0.5,0])
label_rp /= np.linalg.norm(label_rp) / 0.4 #labels at 0.4m from body part
label_rp += (0,0,label_z[j])
label = p.world.robot.loc_head_to_field_transform(rp+label_rp,False)
p.world.draw.line( pos,label,2,Draw.Color.green_light,j_name,False)
p.world.draw.annotation( label,j_name,Draw.Color.cyan,j_name)
Draw.clear_all()
#Draw orientation of body parts
for _ in range(self.cycle_duration):
self.script.batch_execute_behavior("Squat")
self.script.batch_commit_and_send()
self.script.batch_receive()
p : Agent
for p in self.script.players:
if p.world.vision_is_up_to_date and not p.world.robot.loc_is_up_to_date:
p.world.draw.annotation(p.world.robot.cheat_abs_pos, "Not enough visual data! Using IMU", Draw.Color.red,"localization")
for key in p.world.robot.body_parts:
#Select only some body parts
if key not in ['head', 'torso', 'llowerarm', 'rlowerarm', 'lthigh', 'rthigh', 'lshank', 'rshank', 'lfoot', 'rfoot']: continue
bpart_abs_pos = p.world.robot.get_body_part_to_field_transform(key).translate((0.1,0,0)) #10cm in front of body part
x_axis = bpart_abs_pos((0.05,0,0),False)
y_axis = bpart_abs_pos((0,0.05,0),False)
z_axis = bpart_abs_pos((0,0,0.05),False)
axes_0 = bpart_abs_pos.get_translation()
p.world.draw.line( axes_0,x_axis,2,Draw.Color.green_light,key,False)
p.world.draw.line( axes_0,y_axis,2,Draw.Color.blue,key,False)
p.world.draw.line( axes_0,z_axis,2,Draw.Color.red,key)
Draw.clear_all()
def execute(self):
a = self.script.args
# Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
self.script.batch_create(Agent, ((a.i,a.p,a.m,u,u-1,a.t,True,True) for u in range(1,6)) )
#Beam players
self.script.batch_unofficial_beam( [(-2,i*4-10,0.5,i*45) for i in range(5)] )
print("\nPress ctrl+c to return.")
while True:
self.draw_cycle()
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Fwd_Kinematics.py
|
Python
|
agpl-3.0
| 5,742
|
from agent.Base_Agent import Base_Agent as Agent
from itertools import count
from scripts.commons.Script import Script
import numpy as np
'''
Objective:
----------
Fall and get up
'''
class Get_Up():
def __init__(self, script:Script) -> None:
self.script = script
self.player : Agent = None
def sync(self):
r = self.player.world.robot
self.player.scom.commit_and_send( r.get_command() )
self.player.scom.receive()
def execute(self):
a = self.script.args
player = self.player = Agent(a.i, a.p, a.m, a.u, a.r, a.t) # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name
behavior = player.behavior
r = player.world.robot
player.scom.commit_beam((-3,0),0)
print("\nPress ctrl+c to return.")
for i in count():
rnd = np.random.uniform(-6,6,r.no_of_joints)
# Fall
while r.loc_head_z > 0.3 and r.imu_torso_inclination < 50:
if i < 4:
behavior.execute(["Fall_Front","Fall_Back","Fall_Left","Fall_Right"][i % 4]) # First, fall deterministically
else:
r.joints_target_speed[:] = rnd # Second, fall randomly
self.sync()
# Get up
behavior.execute_to_completion("Get_Up")
behavior.execute_to_completion("Zero_Bent_Knees")
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Get_Up.py
|
Python
|
agpl-3.0
| 1,424
|
from agent.Base_Agent import Base_Agent as Agent
from math_ops.Matrix_3x3 import Matrix_3x3
from math_ops.Matrix_4x4 import Matrix_4x4
from scripts.commons.Script import Script
from world.commons.Draw import Draw
from world.Robot import Robot
import numpy as np
'''
Objective:
----------
Demonstrate the accuracy of the IMU
Robot.imu_(...) variables are based on the visual localizer algorithm and the IMU when no visual data is available.
If visual data is not available for longer than 0.2 seconds, the robot's position is frozen and the velocity decays to zero.
The rotation computed by the IMU is so accurate that it is never frozen, no matter how long the robot goes without visual data.
It is almost always safe to use IMU data for rotation.
Known issues: the accelerometer is not reliable in the presence of "instant" acceleration peaks, due to its low sample rate (50Hz)
this limitation impacts the translation estimation during crashes (e.g. falling, crashing against other players)
'''
class IMU():
def __init__(self,script:Script) -> None:
self.script = script
self.player : Agent = None
self.cycle = 0
self.imu_torso_to_field_rotation = [Matrix_3x3() for _ in range(3)]
self.imu_torso_to_field_transform = [Matrix_4x4() for _ in range(3)]
self.imu_head_to_field_transform = [Matrix_4x4() for _ in range(3)]
self.imu_torso_position = np.zeros((3,3))
self.imu_torso_velocity = np.zeros((3,3))
self.imu_torso_acceleration = np.zeros((3,3))
self.imu_torso_next_position = np.zeros((3,3))
self.imu_torso_next_velocity = np.zeros((3,3))
self.imu_CoM_position = np.zeros((3,3))
self.colors = [Draw.Color.green_light, Draw.Color.yellow, Draw.Color.red]
def act(self):
r = self.player.world.robot
joint_indices = [r.J_LLEG_PITCH,
r.J_LKNEE,
r.J_LFOOT_PITCH,
r.J_LARM_ROLL,
r.J_RLEG_PITCH,
r.J_RKNEE,
r.J_RFOOT_PITCH,
r.J_RARM_ROLL]
amplitude = [1,0.93,1,1,1][r.type]
self.cycle += 1
if self.cycle < 50:
r.set_joints_target_position_direct(joint_indices, np.array([32+10,-64,32, 45, 40+10,-80,40, 0])*amplitude)
elif self.cycle < 100:
r.set_joints_target_position_direct(joint_indices, np.array([ -10, 0, 0, 0, -10, 0, 0, 0])*amplitude)
elif self.cycle < 150:
r.set_joints_target_position_direct(joint_indices, np.array([40+10,-80,40, 0, 32+10,-64,32, 45])*amplitude)
elif self.cycle < 200:
r.set_joints_target_position_direct(joint_indices, np.array([ -10, 0, 0, 0, -10, 0, 0, 0])*amplitude)
else:
self.cycle = 0
self.player.scom.commit_and_send( r.get_command() )
self.player.scom.receive()
def act2(self):
r = self.player.world.robot
self.player.behavior.execute("Walk", (0.2,0), False, 5, False, None ) # Args: target, is_target_abs, ori, is_ori_abs, distance
self.player.scom.commit_and_send( r.get_command() )
self.player.scom.receive()
def draw_player_reference_frame(self,i):
pos = self.imu_torso_position[i]
xvec = self.imu_torso_to_field_rotation[i].multiply((1,0,0)) + pos
yvec = self.imu_torso_to_field_rotation[i].multiply((0,1,0)) + pos
zvec = self.imu_torso_to_field_rotation[i].multiply((0,0,1)) + pos
self.player.world.draw.arrow(pos, xvec, 0.2, 2, self.colors[i], "IMU"+str(i), False)
self.player.world.draw.arrow(pos, yvec, 0.2, 2, self.colors[i], "IMU"+str(i), False)
self.player.world.draw.arrow(pos, zvec, 0.2, 2, self.colors[i], "IMU"+str(i), False)
self.player.world.draw.annotation(xvec, "x", Draw.Color.white, "IMU"+str(i), False)
self.player.world.draw.annotation(yvec, "y", Draw.Color.white, "IMU"+str(i), False)
self.player.world.draw.annotation(zvec, "z", Draw.Color.white, "IMU"+str(i), False)
self.player.world.draw.sphere(self.imu_CoM_position[i],0.04,self.colors[i],"IMU"+str(i), True)
def compute_local_IMU(self):
r = self.player.world.robot
g = r.gyro / 50 # convert degrees per second to degrees per step
self.imu_torso_to_field_rotation[2].multiply( Matrix_3x3.from_rotation_deg(g), in_place=True, reverse_order=True)
self.imu_torso_position[2][:] = self.imu_torso_next_position[2]
if self.imu_torso_position[2][2] < 0: self.imu_torso_position[2][2] = 0 #limit z coordinate to positive values
self.imu_torso_velocity[2][:] = self.imu_torso_next_velocity[2]
# convert proper acceleration to coordinate acceleration and fix rounding bias
self.imu_torso_acceleration[2] = self.imu_torso_to_field_rotation[2].multiply(r.acc) + Robot.GRAVITY
self.imu_torso_to_field_transform[2] = Matrix_4x4.from_3x3_and_translation(self.imu_torso_to_field_rotation[2],self.imu_torso_position[2])
self.imu_head_to_field_transform[2] = self.imu_torso_to_field_transform[2].multiply(r.body_parts["torso"].transform.invert())
self.imu_CoM_position[2][:] = self.imu_head_to_field_transform[2](r.rel_cart_CoM_position)
# Next Position = x0 + v0*t + 0.5*a*t^2, Next velocity = v0 + a*t
self.imu_torso_next_position[2] = self.imu_torso_position[2] + self.imu_torso_velocity[2] * 0.02 + self.imu_torso_acceleration[2] * 0.0002
self.imu_torso_next_velocity[2] = self.imu_torso_velocity[2] + self.imu_torso_acceleration[2] * 0.02
self.imu_torso_next_velocity[2] *= Robot.IMU_DECAY #stability tradeoff
def compute_local_IMU_rotation_only(self):
r = self.player.world.robot
g = r.gyro / 50 # convert degrees per second to degrees per step
self.imu_torso_to_field_rotation[1].multiply( Matrix_3x3.from_rotation_deg(g), in_place=True, reverse_order=True)
self.imu_torso_position[1][:] = r.loc_torso_position
self.imu_torso_to_field_transform[1] = Matrix_4x4.from_3x3_and_translation(self.imu_torso_to_field_rotation[1],self.imu_torso_position[1])
self.imu_head_to_field_transform[1] = self.imu_torso_to_field_transform[1].multiply(r.body_parts["torso"].transform.invert())
self.imu_CoM_position[1][:] = self.imu_head_to_field_transform[1](r.rel_cart_CoM_position)
def update_local_IMU(self, i):
r = self.player.world.robot
self.imu_torso_to_field_rotation[i].m[:] = r.imu_torso_to_field_rotation.m
self.imu_torso_to_field_transform[i].m[:] = r.imu_weak_torso_to_field_transform.m
self.imu_head_to_field_transform[i].m[:] = r.imu_weak_head_to_field_transform.m
self.imu_torso_position[i][:] = r.imu_weak_torso_position
self.imu_torso_velocity[i][:] = r.imu_weak_torso_velocity
self.imu_torso_acceleration[i][:] = r.imu_weak_torso_acceleration
self.imu_torso_next_position[i] = self.imu_torso_position[i] + self.imu_torso_velocity[i] * 0.02 + self.imu_torso_acceleration[i] * 0.0002
self.imu_torso_next_velocity[i] = self.imu_torso_velocity[i] + self.imu_torso_acceleration[i] * 0.02
self.imu_CoM_position[i][:] = r.imu_weak_CoM_position
def execute(self):
a = self.script.args
self.player = Agent(a.i, a.p, a.m, a.u, a.r, a.t) # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name
self.player.scom.unofficial_beam((-3,0,self.player.world.robot.beam_height),15)
for _ in range(10): #beam to place
self.player.scom.commit_and_send()
self.player.scom.receive()
self.player.world.draw.annotation((-3,1,1.1), "IMU + Localizer", self.colors[0], "note_IMU_1", True)
for _ in range(150):
self.act()
self.update_local_IMU(0)
self.draw_player_reference_frame(0)
self.player.world.draw.annotation((-3,1,0.9), "IMU for rotation", self.colors[1], "note_IMU_2", True)
self.update_local_IMU(1)
for _ in range(200):
self.act()
self.update_local_IMU(0)
self.draw_player_reference_frame(0)
self.compute_local_IMU_rotation_only()
self.draw_player_reference_frame(1)
self.player.world.draw.annotation((-3,1,0.7), "IMU for rotation & position", self.colors[2], "note_IMU_3", True)
self.update_local_IMU(2)
for _ in range(200):
self.act()
self.update_local_IMU(0)
self.draw_player_reference_frame(0)
self.compute_local_IMU_rotation_only()
self.draw_player_reference_frame(1)
self.compute_local_IMU()
self.draw_player_reference_frame(2)
print("\nPress ctrl+c to return.")
# Still "IMU for rotation & position" but now start walking
self.update_local_IMU(2)
while True:
self.act2()
self.update_local_IMU(0)
self.draw_player_reference_frame(0)
self.compute_local_IMU_rotation_only()
self.draw_player_reference_frame(1)
self.compute_local_IMU()
self.draw_player_reference_frame(2)
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/IMU.py
|
Python
|
agpl-3.0
| 9,349
|
from agent.Base_Agent import Base_Agent as Agent
from itertools import count
from math_ops.Inverse_Kinematics import Inverse_Kinematics
from scripts.commons.Script import Script
from world.commons.Draw import Draw
import numpy as np
class Inv_Kinematics():
def __init__(self, script:Script) -> None:
self.args = script.args
self.last_action = (0,0,0)
self.gravity = True
# Initial pose is a neutral pose where all angles are 0
leg_y_dev, upper_leg_height, upper_leg_depth, lower_leg_len, _, _ = Inverse_Kinematics.NAO_SPECS_PER_ROBOT[self.args.r]
leg_height = upper_leg_height + lower_leg_len
self.feet_pose = [ [[upper_leg_depth,leg_y_dev,-leg_height],[0,0,0]], [[upper_leg_depth,-leg_y_dev,-leg_height], [0,0,0]] ]
def _user_control(self):
while True:
inp = input("Command:")
if inp == "": return 2
elif inp == ".": return 1
elif inp == "h": self.print_help(); continue
elif inp == "g":
self.gravity = not self.gravity
print("Using gravity:",self.gravity)
if self.gravity:
return 6 # extra steps for beam to take effect
else:
return 1
#Check if user input is a value
try:
val = float(inp)
self.feet_pose[self.last_action[0]][self.last_action[1]][self.last_action[2]] = val
continue
except:
pass
if inp[0] not in ['l','r'] or inp[1] not in ['x','y','z','X','Y','Z']:
print("Illegal command!")
continue
side = 0 if inp[0]=='l' else 1
pos_rot = 0 if inp[1].islower() else 1
axis = {'x':0,'y':1,'z':2}[inp[1].lower()]
self.last_action = (side,pos_rot,axis)
try:
val = float(inp[2:])
self.feet_pose[side][pos_rot][axis] = val
except:
print("Illegal value conversion!")
def _draw_labels(self, player:Agent):
r = player.world.robot
robot_pos = r.loc_head_position
for i, body_part in enumerate(['lankle','rankle']):
pos = r.get_body_part_abs_position(body_part)
label_rel_pos = np.array([-0.2,(0.5-i),0])
label_rel_pos /= np.linalg.norm(label_rel_pos) / 1.0 #labels at 1.0m from body part
player.world.draw.line( pos,pos+label_rel_pos,2,Draw.Color.green_light,body_part,False)
p = self.feet_pose[i]
pose_text = f"x:{p[0][0]:.4f} y:{p[0][1]:.4f} z:{p[0][2]:.4f}", f"rol:{p[1][0]:.2f} (bias) pit:{p[1][1]:.2f} (bias) yaw:{p[1][2]:.2f} "
player.world.draw.annotation( pos+label_rel_pos+[0,0,0.2], pose_text[0], Draw.Color.cyan,body_part,False)
player.world.draw.annotation( pos+label_rel_pos+[0,0,0.1], pose_text[1], Draw.Color.cyan,body_part,False)
# Draw forward kinematics (ankles positions + feet rotation)
p = player.inv_kinematics.get_body_part_pos_relative_to_hip(body_part) # ankle relative to center of both hip joints
foot_rel_torso = r.head_to_body_part_transform("torso", r.body_parts[['lfoot','rfoot'][i]].transform )
w = foot_rel_torso.get_roll_deg(), foot_rel_torso.get_pitch_deg(), foot_rel_torso.get_yaw_deg()
pose_text = f"x:{p[0]:.4f} y:{p[1]:.4f} z:{p[2]:.4f}", f"rol:{w[0]:.4f} pit:{w[1]:.4f} yaw:{w[2]:.4f}"
player.world.draw.annotation( pos+label_rel_pos+[0,0,-0.2], pose_text[0], Draw.Color.red,body_part,False)
player.world.draw.annotation( pos+label_rel_pos+[0,0,-0.3], pose_text[1], Draw.Color.red,body_part,False)
player.world.draw.annotation( pos+label_rel_pos+[0,0,-0.4], "(forward kinematics data)", Draw.Color.red,body_part,True)
note = f"Torso roll: {r.imu_torso_roll:.2f} Torso pitch: {r.imu_torso_pitch:.2f}"
player.world.draw.annotation( robot_pos+[0,0,0.10],note,Draw.Color.red,"Torso")
def print_help(self):
print("""
---------------- Inverse kinematics demonstration ----------------
INPUT: ankle positions + feet rotations (relative coordinates)
OUTPUT: angular positions of both legs' joints
------------------------------------------------------------------
Command: {action/option}
action: [side:{l/r} axis*:{x/y/z/X/Y/Z}] value
*for position use x/y/z, for rotation use X/Y/Z
option: {"",.,g,h}
Examples:
"lz-0.12" - move left ankle to -0.1m in the z-axis
"rX30.5" - rotate right foot to 30.5 deg in the x-axis (roll)
"20" - repeat last action but change value to 20
"" - advance 2 simulation step
"." - advance 1 simulation step
"g" - toggle gravity
"h" - help, display this message
"ctrl+c" - quit demonstration
------------------------------------------------------------------""")
def execute(self):
self.state = 0
a = self.args
self.print_help()
player = Agent(a.i, a.p, a.m, a.u, a.r, a.t) # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name
player.scom.unofficial_beam((-3,0,0.42),0)
next_control_step = 20
for i in count():
if self.gravity:
player.scom.unofficial_beam((-3,0,0.42),0)
self._draw_labels(player)
if i == next_control_step:
next_control_step += self._user_control()
for i in range(2): #left and right legs
indices, values, error_codes = player.inv_kinematics.leg(self.feet_pose[i][0], self.feet_pose[i][1], bool(i==0), False)
if -1 in error_codes:
print("Position is out of reach!")
error_codes.remove(-1)
for j in error_codes:
print(f"Joint {j} is out of range!")
player.world.robot.set_joints_target_position_direct(indices,values)
player.scom.commit_and_send( player.world.robot.get_command() )
player.scom.receive()
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Inv_Kinematics.py
|
Python
|
agpl-3.0
| 6,211
|
from agent.Base_Agent import Base_Agent as Agent
from scripts.commons.Script import Script
from world.commons.Draw import Draw
import numpy as np
class Joints():
def __init__(self,script:Script) -> None:
self.script = script
self.agent_pos = (-3,0,0.45)
self.enable_pos = True
self.enable_gravity = False
self.enable_labels = True
self.enable_harmonize = True
self.active_joint = 0
self.joints_value = None #position or speed
def _draw_joints(self,player:Agent):
zstep = 0.05
label_z = [3*zstep,5*zstep,0,0,zstep,zstep,2*zstep,2*zstep,0,0,0,0,zstep,zstep,0,0,zstep,zstep,4*zstep,4*zstep,5*zstep,5*zstep,0,0]
for j, transf in enumerate(player.world.robot.joints_transform):
rp = transf.get_translation()
pos = player.world.robot.loc_head_to_field_transform(rp,False)
j_id = f"{j}"
j_name = f"{j}"
color = Draw.Color.cyan
if player.world.robot.joints_position[j] != 0:
j_name += f" ({int(player.world.robot.joints_position[j])})"
color = Draw.Color.red
label_rp = np.array([rp[0]-0.0001,rp[1]*0.5,0])
label_rp /= np.linalg.norm(label_rp) / 0.5 #labels at 0.5m from body part
label_rp += (0,0,label_z[j])
label = player.world.robot.loc_head_to_field_transform(rp+label_rp,False)
player.world.draw.line( pos,label,2,Draw.Color.green_light,j_id,False)
player.world.draw.annotation( label,j_name,color,j_id)
def print_help(self):
print(f"""
---------------------- Joints demonstration ----------------------
Command: {{action/actions/option}}
action : [joint:{{int}}] value
actions: value0,value1,...,valueN
e.g. if N=10, you control all joints from j0 to j10
option: {{h,s,g,l,w,r,"",.}}
Examples:
"6 90" - move joint 6 to 90deg or move joint 6 at 90deg/step
"4" - move last joint to 4deg or apply speed of 4deg/step
"1,9,-35"- move joints 0,1,2 to 1deg, 9deg, -35deg (or speed)
"h" - help, display this message
"s" - toggle position/speed control ({"Posi" if self.enable_pos else "Spee"})
"g" - toggle gravity ({self.enable_gravity})
"l" - toggle labels ({self.enable_labels})
"w" - toggle harmonize* ({self.enable_harmonize})
"r" - reset (position mode + reset joints)
"" - advance 2 simulation step
"." - advance 1 simulation step
"ctrl+c" - quit demonstration
*all joints end moving at the same time when harmonize is True
------------------------------------------------------------------""")
def _user_control_step(self,player:Agent):
while True:
inp = input("Command: ")
if inp == "s":
self.enable_pos = not self.enable_pos
print("Using", "position" if self.enable_pos else "velocity", "control.")
if self.enable_pos:
self.joints_value[:] = player.world.robot.joints_position
else:
self.joints_value.fill(0)
continue
elif inp == "g":
self.enable_gravity = not self.enable_gravity
print("Using gravity:",self.enable_gravity)
continue
elif inp == "l":
self.enable_labels = not self.enable_labels
print("Using labels:",self.enable_labels)
continue
elif inp == "w":
self.enable_harmonize = not self.enable_harmonize
print("Using harmonize:",self.enable_harmonize)
continue
elif inp == "r":
self.enable_pos = True
self.joints_value.fill(0)
print("Using position control. All joints are set to zero.")
continue
elif inp == "h":
self.print_help(); continue
elif inp == "": return 1
elif inp == ".": return 0
try:
if " " in inp:
self.active_joint, value = map(float, inp.split())
self.joints_value[int(self.active_joint)] = value
elif "," in inp:
values = inp.split(",")
self.joints_value[0:len(values)] = values
else:
self.joints_value[self.active_joint] = float(inp)
except:
print("Illegal command!")
continue
def execute(self):
a = self.script.args
player = Agent(a.i, a.p, a.m, a.u, a.r, a.t) # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name
self.joints_no = player.world.robot.no_of_joints
self.joints_value = np.zeros(self.joints_no) # initialize
player.scom.commit_beam(self.agent_pos[0:2],0)
self.print_help()
# initialize (+beam)
for _ in range(8):
player.scom.commit_and_send()
player.scom.receive()
self._draw_joints(player)
skip_next = 0 # variable to advance more than 1 step
while True:
if skip_next == 0:
skip_next = self._user_control_step(player)
else:
skip_next -= 1
if self.enable_labels:
self._draw_joints(player)
if self.enable_pos:
player.world.robot.set_joints_target_position_direct(slice(self.joints_no), self.joints_value, harmonize=self.enable_harmonize)
else:
player.world.robot.joints_target_speed[:]=self.joints_value * 0.87266463 #deg/step to rad/s
if not self.enable_gravity: player.scom.unofficial_beam(self.agent_pos,0)
player.scom.commit_and_send( player.world.robot.get_command() )
player.scom.receive()
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Joints.py
|
Python
|
agpl-3.0
| 6,032
|
from agent.Base_Agent import Base_Agent as Agent
from math_ops.Math_Ops import Math_Ops as M
from scripts.commons.Script import Script
import numpy as np
'''
Objective:
----------
Demonstrate kick
'''
class Kick():
def __init__(self, script:Script) -> None:
self.script = script
def execute(self):
a = self.script.args
player = Agent(a.i, a.p, a.m, a.u, a.r, a.t) # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name
player.path_manager.draw_options(enable_obstacles=True, enable_path=True) # enable drawings of obstacles and path to ball
behavior = player.behavior
w = player.world
r = w.robot
print("\nThe robot will kick towards the center of the field")
print("Try to manually relocate the ball")
print("Press ctrl+c to return\n")
player.scom.unofficial_set_play_mode("PlayOn")
player.scom.unofficial_beam((-3,0,r.beam_height),0)
vec = (1,0)
while True:
player.scom.unofficial_set_game_time(0)
b = w.ball_abs_pos[:2]
if 0 < np.linalg.norm(w.get_ball_abs_vel(6)) < 0.02: # speed of zero is likely to indicate prolongued inability to see the ball
if np.linalg.norm(w.ball_rel_head_cart_pos[:2]) > 0.5: # update kick if ball is further than 0.5 m
if max(abs(b)) < 0.5:
vec = np.array([6,0])
else:
vec = M.normalize_vec((0,0)-b) * 6
w.draw.point(b+vec, 8, w.draw.Color.pink, "target")
behavior.execute("Basic_Kick", M.vector_angle(vec))
player.scom.commit_and_send( r.get_command() )
player.scom.receive()
if behavior.is_ready("Get_Up"):
player.scom.unofficial_beam((*r.loc_head_position[0:2],r.beam_height),0)
behavior.execute_to_completion("Zero_Bent_Knees")
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Kick.py
|
Python
|
agpl-3.0
| 1,991
|
from agent.Agent import Agent as Agent
from cpp.localization import localization
from math_ops.Math_Ops import Math_Ops as M
from scripts.commons.Script import Script
from world.commons.Draw import Draw
from world.commons.Other_Robot import Other_Robot
class Localization():
def __init__(self,script:Script) -> None:
self.script = script
def execute(self):
a = self.script.args
d = self.draw = Draw(True, 0, a.i, 32769) # using independent draw object so that the internal agent drawings can be disabled
# Args: Server IP, Agent Port, Monitor Port, Uniform No., Team name, Enable Log, Enable Draw
self.script.batch_create(Agent, ((a.i,a.p,a.m,1,a.t,False,False),)) # one teammate (dummy goalkeeper without communication)
self.script.batch_create(Agent, ((a.i,a.p,a.m,5,"Opponent",False,False),)) # one opponent
self.script.batch_create(Agent, ((a.i,a.p,a.m,9,a.t,False,False),)) # one main agent (the one who draws its world)
# Beam dummy goalkeeper
self.script.batch_unofficial_beam( ((-14,0,0.5,0),), slice(0,1))
p : Agent = self.script.players[-1] # p identifies the main agent
p.scom.unofficial_set_play_mode("PlayOn")
# Execute
while True:
self.script.batch_commit_and_send(slice(0,1)) # dummy agent does not think
self.script.batch_execute_agent(slice(1,None)) # execute normal agents
self.script.batch_receive(slice(0,1), False) # receive & don't update dummy's world state (to save cpu resources)
self.script.batch_receive(slice(1,None)) # receive & update world state
if p.world.vision_is_up_to_date:
if p.world.robot.loc_is_up_to_date: # localization will draw the world of the last agent to be executed
localization.print_python_data() # print data received by the localization module
localization.draw_visible_elements(not p.world.team_side_is_left) # draw visible elements
localization.print_report() # print report with stats
print("\nPress ctrl+c to return.")
d.circle( p.world.ball_abs_pos, 0.1,6,Draw.Color.purple_magenta,"world", False)
else:
d.annotation( p.world.robot.cheat_abs_pos, "Not enough visual data!", Draw.Color.red,"world", False)
for o in p.world.teammates:
if o.state_last_update != 0 and not o.is_self: # skip if other robot was not yet seen
self._draw_other_robot(p, o, Draw.Color.white)
for o in p.world.opponents:
if o.state_last_update != 0: # skip if other robot was not yet seen
self._draw_other_robot(p, o, Draw.Color.red)
d.flush("world")
def _draw_other_robot(self, p:Agent, o:Other_Robot, team_color):
#p - player that sees
#o - other robot (player that is seen)
d = self.draw
white = Draw.Color.white
green = Draw.Color.green_light
gray = Draw.Color.gray_20
time_diff = p.world.time_local_ms - o.state_last_update
if time_diff > 0:
white = Draw.Color.gray_40
green = Draw.Color.get(107, 139, 107)
gray = Draw.Color.gray_50
#orientation
if len(o.state_abs_pos)==3:
line_tip = o.state_abs_pos + (0.5*M.deg_cos(o.state_orientation),0.5*M.deg_sin(o.state_orientation),0)
d.line( o.state_abs_pos, line_tip, 3, white, "world", False)
else:
temp_pos = M.to_3d(o.state_abs_pos, 0.3)
line_tip = temp_pos + (0.5*M.deg_cos(o.state_orientation),0.5*M.deg_sin(o.state_orientation),0)
d.line( temp_pos, line_tip, 3, Draw.Color.yellow, "world", False)
#body parts
for pos in o.state_body_parts_abs_pos.values():
d.sphere( pos, 0.07, green,"world", False)
#player ground area
d.circle( o.state_ground_area[0], o.state_ground_area[1], 6, team_color,"world", False)
#distance
midpoint = (o.state_abs_pos[0:2] + p.world.robot.loc_head_position[0:2])/2
d.line( o.state_abs_pos[0:2], p.world.robot.loc_head_position[0:2], 1, gray, "world", False)
d.annotation( midpoint, f'{o.state_horizontal_dist:.2f}m', white, "world", False)
#velocity
arrow_tip = o.state_abs_pos[0:2] + o.state_filtered_velocity[0:2]
d.arrow( o.state_abs_pos[0:2], arrow_tip, 0.2, 4, green, "world", False)
#state
state_color = white if not o.state_fallen else Draw.Color.yellow
d.annotation( (o.state_abs_pos[0],o.state_abs_pos[1],1),
f"({o.unum}) {'Fallen' if o.state_fallen else 'Normal'}", state_color, "world", False)
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Localization.py
|
Python
|
agpl-3.0
| 4,947
|
from agent.Base_Agent import Base_Agent as Agent
from cpp.a_star import a_star
from scripts.commons.Script import Script
import numpy as np
import time
'''
::::::::::::::::::::::::::::::::::::::::::
::::::::a_star.compute(param_vec):::::::::
::::::::::::::::::::::::::::::::::::::::::
param_vec (numpy array, float32)
param_vec[0] - start x
param_vec[1] - start y
param_vec[2] - allow path to go out of bounds? (useful when player does not have the ball)
param_vec[3] - go to opposite goal? (path goes to the most efficient part of the goal)
param_vec[4] - target x (only used if param_vec[3]==0)
param_vec[5] - target y (only used if param_vec[3]==0)
param_vec[6] - timeout in us (maximum execution time)
-------------- [optional] ----------------
param_vec[ 7-11] - obstacle 1: x, y, hard radius (max:5m), soft radius (max:5m), repulsive force for soft radius (min:0)
param_vec[12-16] - obstacle 2: x, y, hard radius (max:5m), soft radius (max:5m), repulsive force for soft radius (min:0)
... - obstacle n: x, y, hard radius (max:5m), soft radius (max:5m), repulsive force for soft radius (min:0)
---------------- return ------------------
path_ret : numpy array (float32)
path_ret[:-2]
contains path from start to target (up to a maximum of 1024 positions)
each position is composed of x,y coordinates (so, up to 2048 coordinates)
the return vector is flat (1 dimension) (e.g. [x1,y1,x2,y2,x3,y3,...])
reasons why path may not end in target:
- path is longer than 1024 positions (which is at least 102 meters!)
- reaching target is impossible or timeout (in which case, the path ends in the closest position to target found)
path_ret[-2]
number indicating the path status
0 - success
1 - timeout before the target was reached (may be impossible)
2 - impossible to reach target (all options were tested)
3 - no obstacles between start and target (path_ret[:-2] contains only 2 points: the start and target)
path_ret[-1]
A* path cost
::::::::::::::::::::::::::::::::::::::::::
::::::::::::::::::Notes:::::::::::::::::::
::::::::::::::::::::::::::::::::::::::::::
Map of field:
- The algorithm has a 32m by 22m map with a precision of 10cm (same dimension as field +1 meter border)
- The map contains information about field lines, goal posts and goal net
- The path may go outside the field (out of bounds) if the user allows it,
but it may never go through goal posts or the goal net (these are considered static inaccessible obstacles)
- The user must only specify dynamic obstacles through the arguments
Repulsive force:
- The repulsive force is implemented as an extra cost for the A* algorithm
- The cost for walking 10cm is 1, and the cost for walking diagonally is sqrt(2)
- The extra cost of stepping on a position with a repulsive force f=1 is 1
- For any given position on the field, the repulsive force of >=2 objects is combined with the max function, max(f1,f2), NOT f1+f2!
- If path starts on inaccessible position, it can go to a neighbor inaccessible position but there is a cost of 100 (to avoid inaccessible paths)
Example:
Map 1 Map 2 Map 3
..x.. ..o.. ..o..
..1.. ..o.. .o1..
..o.. ..o.. ..o..
Consider 'Map 1' where 'x' is the target, 'o' is the player, and '1' is a repulsive force of 1
In 'Map 2', the player chooses to go forward, the total cost of this path is: 1+(extra=1)+1 = 3
In 'Map 3', the player avoids the repulsive force, the total cost of this path is: sqrt(2)+sqrt(2) = 2.83 (optimal solution)
Map 1 Map 2 Map 3 Map 4
...x... ..oo... ...o... ...o...
..123.. .o123.. ..o23.. ..1o3..
...o... ..oo... ...o... ...o...
Consider 'Map 1' with 3 positions with 3 distinct repulsive forces, going from 1 to 3.
In 'Map 2', the player avoids all repulsive forces, total cost: 1+sqrt(2)+sqrt(2)+1 = 4.83
In 'Map 3', the player goes through the smallest repulsive force, total cost: sqrt(2)+(extra=1)+sqrt(2) = 3.83 (optimal solution)
In 'Map 4', the player chooses to go forward, total cost: 1+(extra=2)+1 = 4.00
Obstacles:
hard radius: inaccessible obstacle radius (infinite repulsive force)
soft radius: accessible obstacle radius with user-defined repulsive force (fades with distance) (disabled if <= hard radius)
Example:
obstacle(0,0,1,3,5) -> obstacle at pos(0,0) with hard radius of 1m, soft radius of 3m with repulsive force 5
- the path cannot be at <=1m from this obstacle, unless the path were to start inside that radius
- the soft radius force is maximum at the center (5) and fades with distance until (0) at 3m from the obstacle
- so to sum up, at a distance of [0,1]m the force is infinite, [1,3]m the force goes from 3.333 to 0
obstacle(-2.1,3,0,0,0) -> obstacle at pos(-2.1,3) with hard radius of 0m, soft radius of 0m with repulsive force 0
- the path cannot go through (-2.1,3)
obstacle(-2.16,3,0,0,8) -> obstacle at pos(-2.2,3) with hard radius of 0m, soft radius of 0m with repulsive force 8
- the path cannot go through (-2.2,3), the map has a precision of 10cm, so the obstacle is placed at the nearest valid position
- the repulsive force is ignored because (soft radius <= hard radius)
'''
class Pathfinding():
def __init__(self, script:Script) -> None:
self.script = script
a_star.compute(np.zeros(6, np.float32)) # Initialize (not needed, but the first run takes a bit more time)
def draw_grid(self):
d = self.player.world.draw
MAX_RAW_COST = 0.6 # dribble cushion
for x in np.arange(-16,16.01,0.1):
for y in np.arange(-11,11.01,0.1):
s_in, cost_in = a_star.compute(np.array([x, y, 0, 0, x, y, 5000], np.float32))[-2:] # do not allow out of bounds
s_out, cost_out = a_star.compute(np.array([x, y, 1, 0, x, y, 5000], np.float32))[-2:] # allow out of bounds
#print(path_cost_in, path_cost_out)
if s_out != 3:
d.point((x,y), 5, d.Color.red, "grid", False)
elif s_in != 3:
d.point((x,y), 4, d.Color.blue_pale, "grid", False)
elif 0 < cost_in < MAX_RAW_COST + 1e-6:
d.point((x,y), 4, d.Color.get(255,(1-cost_in/MAX_RAW_COST)*255,0), "grid", False)
elif cost_in > MAX_RAW_COST:
d.point((x,y), 4, d.Color.black, "grid", False)
#else:
# d.point((x,y), 4, d.Color.white, "grid", False)
d.flush("grid")
def sync(self):
r = self.player.world.robot
self.player.behavior.head.execute()
self.player.scom.commit_and_send( r.get_command() )
self.player.scom.receive()
def draw_path_and_obstacles(self, obst, path_ret_pb, path_ret_bp):
w = self.player.world
# draw obstacles
for i in range(0,len(obst[0]),5):
w.draw.circle(obst[0][i:i+2], obst[0][i+2], 2, w.draw.Color.red, "obstacles", False)
w.draw.circle(obst[0][i:i+2], obst[0][i+3], 2, w.draw.Color.orange, "obstacles", False)
# draw path
path_pb = path_ret_pb[:-2] # create view without status
path_status_pb = path_ret_pb[-2] # extract status
path_cost_pb = path_ret_pb[-1] # extract A* cost
path_bp = path_ret_bp[:-2] # create view without status
path_status_bp = path_ret_bp[-2] # extract status
path_cost_bp = path_ret_bp[-1] # extract A* cost
c_pb = {0: w.draw.Color.green_lime, 1: w.draw.Color.yellow, 2: w.draw.Color.red, 3: w.draw.Color.blue_light}[path_status_pb]
c_bp = {0: w.draw.Color.green_pale, 1: w.draw.Color.yellow_light, 2: w.draw.Color.red_salmon, 3: w.draw.Color.blue_pale}[path_status_bp]
for i in range(2,len(path_pb)-2,2):
w.draw.line(path_pb[i-2:i],path_pb[i:i+2], 5, c_pb, "path_player_ball", False)
if len(path_pb)>=4:
w.draw.arrow(path_pb[-4:-2],path_pb[-2:],0.4, 5, c_pb, "path_player_ball", False)
for i in range(2,len(path_bp)-2,2):
w.draw.line(path_bp[i-2:i],path_bp[i:i+2], 5, c_bp, "path_ball_player", False)
if len(path_bp)>=4:
w.draw.arrow(path_bp[-4:-2],path_bp[-2:],0.4, 5, c_bp, "path_ball_player", False)
w.draw.flush("obstacles")
w.draw.flush("path_player_ball")
w.draw.flush("path_ball_player")
def move_obstacles(self, obst):
for i in range(len(obst[0])//5):
obst[0][i*5] +=obst[1][i,0]
obst[0][i*5+1]+=obst[1][i,1]
if not -16<obst[0][i*5] <16: obst[1][i,0] *=-1
if not -11<obst[0][i*5+1]<11: obst[1][i,1] *=-1
def execute(self):
a = self.script.args
self.player = Agent(a.i, a.p, a.m, a.u, a.r, a.t) # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name
w = self.player.world
r = self.player.world.robot
timeout = 5000
go_to_goal = 0
obst_no = 50
obst = [[0,0,0.5,1,1]*obst_no, np.random.uniform(-0.01,0.01,(obst_no,2))] # obst[x,y,h,s,f] + random velocity
print("\nMove player/ball around using RoboViz!")
print("Press ctrl+c to return.")
print("\nPathfinding timeout set to", timeout, "us.")
print("Pathfinding execution time:")
self.draw_grid()
while True:
ball = w.ball_abs_pos[:2]
rpos = r.loc_head_position[:2]
self.move_obstacles(obst)
param_vec_pb = np.array([*rpos, 1, go_to_goal, *ball, timeout, *obst[0]], np.float32) # allow out of bounds (player->ball)
param_vec_bp = np.array([*ball, 0, go_to_goal, *rpos, timeout, *obst[0]], np.float32) # don't allow (ball->player)
t1 = time.time()
path_ret_pb = a_star.compute(param_vec_pb)
t2 = time.time()
path_ret_bp = a_star.compute(param_vec_bp)
t3 = time.time()
print(end=f"\rplayer->ball {int((t2-t1)*1000000):5}us (len:{len(path_ret_pb[:-2])//2:4}) ball->player {int((t3-t2)*1000000):5}us (len:{len(path_ret_bp[:-2])//2:4}) ")
self.draw_path_and_obstacles( obst, path_ret_pb, path_ret_bp )
self.sync()
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Pathfinding.py
|
Python
|
agpl-3.0
| 10,603
|
from agent.Agent import Agent
from itertools import count
from scripts.commons.Script import Script
from typing import List
from world.commons.Draw import Draw
class Radio_Localization():
def __init__(self,script:Script) -> None:
self.script = script
def draw_objects(self, p:Agent, pos, is_down, was_seen, last_update, is_self=False):
w = p.world
me = w.robot.loc_head_position
# get draw object from same player to always overwrite previous drawing
# could also use team channel but with this approach we could draw for both teams
d:Draw = self.script.players[0].world.draw
# VISUALSTEP_MS is the time it takes to get a visual update
is_current = last_update > w.time_local_ms - w.VISUALSTEP_MS
# 0.12s is the time it takes to do a full broadcast with all positions if every group is completely visible
# here we use >= instead of > because the radio message comes with a delay of 20ms
is_recent = last_update >= w.time_local_ms - 120
if is_current and was_seen:
c = d.Color.green_light # I've seen this object in the current or previous time step
elif is_recent and was_seen:
c = d.Color.green # I've seen this object in the last 0.12s
elif is_current:
c = d.Color.yellow # I've heard about this object in the current or previous time step (and it was not seen in the same period)
elif is_recent:
c = d.Color.yellow_light # I've heard about this object in the last 0.12s (the last available info was not obtained from vision)
else:
c = d.Color.red # I haven't seen or heard about this object in the last 0.12s
if is_self:
if w.robot.radio_fallen_state:
d.annotation(me, "Fallen (radio)", d.Color.yellow, "objects", False) # I heard I've fallen (but I missed the last 2 visual steps)
elif w.robot.loc_head_z < 0.3:
d.annotation(me, "Fallen (internal)", d.Color.white, "objects", False) # I have detected I've fallen
d.sphere(me, 0.06, c, "objects", False)
else:
if is_down:
d.annotation((me[:2]+pos[:2])/2,"Fallen",d.Color.white,"objects",False)
d.arrow(me, pos, 0.1, 3, c, "objects", False)
def draw(self,p:Agent):
w = p.world
others = w.teammates + w.opponents
#----------------------------------------------------------- draw other players
for o in others:
if o.is_self or o.state_last_update==0: # do not draw self or never before seen players
continue
pos = o.state_abs_pos
is_down = o.state_fallen
# 3D head position means head is visible, 2D means some body parts are visible but not the head, or the head position comes from radio
is_3D = pos is not None and len(pos)==3
self.draw_objects(p, pos, is_down, is_3D, o.state_last_update)
#----------------------------------------------------------- draw self
is_pos_from_vision = w.robot.loc_head_position_last_update == w.robot.loc_last_update
self.draw_objects(p, None, None, is_pos_from_vision, w.robot.loc_head_position_last_update, True)
#----------------------------------------------------------- draw ball and flush drawings
self.draw_objects(p, w.ball_abs_pos, False, w.is_ball_abs_pos_from_vision, w.ball_abs_pos_last_update)
self.script.players[0].world.draw.flush("objects")
def execute(self):
a = self.script.args
# Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
self.script.batch_create(Agent, ((a.i,a.p,a.m,u,a.t, False,u==1) for u in range(1,12)))
self.script.batch_create(Agent, ((a.i,a.p,a.m,u,"Opponent",False,False) for u in range(1,12)))
players : List[Agent] = self.script.players
# Beam opponents
beam_pos = [(-(i//2)-3,(i%2*2-1)*(i//2+1),0) for i in range(11)]
self.script.batch_commit_beam( beam_pos, slice(11,None) )
print("\nPress ctrl+c to return.")
# Execute
for j in count():
self.script.batch_execute_agent( slice(11) ) # run our agents (think and send)
self.script.batch_commit_and_send( slice(11,None) ) # run their agents (don't think, just send)
self.draw(players[j//15%11]) # draw knowledge, iterate through our team, 15 time steps per player
self.script.batch_receive(slice(11)) # receive & update our team's world state
self.script.batch_receive(slice(11,None), False) # receive & don't update opponent's world state (to save cpu resources)
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Radio_Localization.py
|
Python
|
agpl-3.0
| 4,838
|
import os
class Server():
def __init__(self,script) -> None:
if os.path.isdir("/usr/local/share/rcssserver3d/"):
self.source = "/usr/local/share/rcssserver3d/"
elif os.path.isdir("/usr/share/rcssserver3d/"):
self.source = "/usr/share/rcssserver3d/"
else:
raise FileNotFoundError("The server configuration files were not found!")
# To add options: insert into options & explations with same index, read respective value from file or from other values, add edit entry
self.options = ["Official Config", "Penalty Shootout", "Soccer Rules", "Sync Mode", "Real Time", "Cheats", "Full Vision", "Add Noise", "25Hz Monitor"]
self.descriptions = ["Configuration used in official matches", "Server's Penalty Shootout mode", "Play modes, automatic referee, etc.",
"Synchronous communication between agents and server", "Real Time (or maximum server speed)",
"Agent position & orientation, ball position", "See 360 deg instead of 120 deg (vertically & horizontally)",
"Noise added to the position of visible objects", "25Hz Monitor (or 50Hz but RoboViz will show 2x the actual speed)"]
spark_f = os.path.expanduser("~/.simspark/spark.rb")
naoneckhead_f = self.source+"rsg/agent/nao/naoneckhead.rsg"
self.files = {"Penalty Shootout" : self.source + "naosoccersim.rb",
"Soccer Rules" : self.source + "naosoccersim.rb",
"Sync Mode" : spark_f,
"Real Time" : self.source+"rcssserver3d.rb",
"Cheats" : naoneckhead_f,
"Full Vision" : naoneckhead_f,
"Add Noise" : naoneckhead_f,
"25Hz Monitor" : spark_f}
def label(self, setting_name, t_on, t_off):
with open(self.files[setting_name], "r") as sources:
content = sources.read()
if t_on in content:
self.values[setting_name] = "On"
elif t_off in content:
self.values[setting_name] = "Off"
else:
self.values[setting_name] = "Error"
def read_config(self):
v = self.values = dict()
print("Reading server configuration files...")
self.label("Penalty Shootout", "addSoccerVar('PenaltyShootout', true)", "addSoccerVar('PenaltyShootout', false)")
self.label("Soccer Rules", " gameControlServer.initControlAspect('SoccerRuleAspect')", "#gameControlServer.initControlAspect('SoccerRuleAspect')")
self.label("Real Time", "enableRealTimeMode = true", "enableRealTimeMode = false")
self.label("Cheats", "setSenseMyPos true", "setSenseMyPos false")
self.label("Full Vision", "setViewCones 360 360", "setViewCones 120 120")
self.label("Add Noise", "addNoise true", "addNoise false")
self.label("Sync Mode", "agentSyncMode = true", "agentSyncMode = false")
self.label("25Hz Monitor", "monitorStep = 0.04", "monitorStep = 0.02")
is_official_config = (v["Penalty Shootout"] == "Off" and v["Soccer Rules"] == "On" and v["Real Time"] == "On" and v["Cheats"] == "Off" and v["Full Vision"] == "Off" and
v["Add Noise"] == "On" and v["Sync Mode"] == "Off" and v["25Hz Monitor"] == "On")
v["Official Config"] = "On" if is_official_config else "Off"
def change_config(self, setting_name, t_on, t_off, current_value=None, file=None):
if current_value is None:
current_value = self.values[setting_name]
if file is None:
file = self.files[setting_name]
with open(file, "r") as sources:
t = sources.read()
if current_value == "On":
t = t.replace(t_on, t_off, 1)
print(f"Replacing '{t_on}' with '{t_off}' in '{file}'")
elif current_value == "Off":
t = t.replace(t_off, t_on, 1)
print(f"Replacing '{t_off}' with '{t_on}' in '{file}'")
else:
print(setting_name, "was not changed because the value is unknown!")
with open(file, "w") as sources:
sources.write(t)
def execute(self):
while True:
self.read_config()
# Convert convenient values dict to list
values_list = [self.values[o] for o in self.options]
print()
UI.print_table( [self.options, values_list, self.descriptions], ["Setting", "Value", "Description"], numbering=[True, False, False])
choice = UI.read_int('Choose setting (ctrl+c to return): ',0,len(self.options))
opt = self.options[choice]
prefix = ['sudo', 'python3', 'scripts/utils/Server.py', opt]
if opt in self.files:
suffix = [self.values[opt], self.files[opt]]
if opt == "Penalty Shootout":
subprocess.call([*prefix, "addSoccerVar('PenaltyShootout', true)", "addSoccerVar('PenaltyShootout', false)", *suffix])
elif opt == "Soccer Rules":
subprocess.call([*prefix, "gameControlServer.initControlAspect('SoccerRuleAspect')", "#gameControlServer.initControlAspect('SoccerRuleAspect')", *suffix])
elif opt == "Sync Mode":
self.change_config(opt, "agentSyncMode = true", "agentSyncMode = false") # doesn't need sudo privileges
elif opt == "Real Time":
subprocess.call([*prefix, "enableRealTimeMode = true", "enableRealTimeMode = false", *suffix])
elif opt == "Cheats":
subprocess.call([*prefix, "setSenseMyPos true", "setSenseMyPos false", *suffix,
opt, "setSenseMyOrien true", "setSenseMyOrien false", *suffix,
opt, "setSenseBallPos true", "setSenseBallPos false", *suffix])
elif opt == "Full Vision":
subprocess.call([*prefix, "setViewCones 360 360", "setViewCones 120 120", *suffix])
elif opt == "Add Noise":
subprocess.call([*prefix, "addNoise true", "addNoise false", *suffix])
elif opt == "25Hz Monitor":
self.change_config(opt, "monitorStep = 0.04", "monitorStep = 0.02") # doesn't need sudo privileges
elif opt == "Official Config":
if self.values[opt] == "On":
print("The official configuration is already On!")
else: # here, the first option is the official value
subprocess.call([*prefix[:3],
"Penalty Shootout", "addSoccerVar('PenaltyShootout', false)", "addSoccerVar('PenaltyShootout', true)", "Off", self.files["Penalty Shootout"],
"Soccer Rules", "gameControlServer.initControlAspect('SoccerRuleAspect')", "#gameControlServer.initControlAspect('SoccerRuleAspect')", "Off", self.files["Soccer Rules"],
"Sync Mode", "agentSyncMode = false", "agentSyncMode = true", "Off", self.files["Sync Mode"],
"Real Time", "enableRealTimeMode = true", "enableRealTimeMode = false", "Off", self.files["Real Time"],
"Cheats", "setSenseMyPos false", "setSenseMyPos true", "Off", self.files["Cheats"],
"Cheats", "setSenseMyOrien false", "setSenseMyOrien true", "Off", self.files["Cheats"],
"Cheats", "setSenseBallPos false", "setSenseBallPos true", "Off", self.files["Cheats"],
"Full Vision", "setViewCones 120 120", "setViewCones 360 360", "Off", self.files["Full Vision"],
"Add Noise", "addNoise true", "addNoise false", "Off", self.files["Add Noise"],
"25Hz Monitor", "monitorStep = 0.04", "monitorStep = 0.02", "Off", self.files["25Hz Monitor"]])
# process with sudo privileges to change the configuration files
if __name__ == "__main__":
import sys
s = Server(None)
for i in range(1,len(sys.argv),5):
s.change_config( *sys.argv[i:i+5] )
else:
import subprocess
from scripts.commons.UI import UI
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Server.py
|
Python
|
agpl-3.0
| 8,179
|
from agent.Base_Agent import Base_Agent as Agent
from itertools import count
from scripts.commons.Script import Script
'''
How does communication work?
The say command allows a player to broadcast a message to everyone on the field
Message range: 50m (the field is 36m diagonally, so ignore this limitation)
The hear perceptor indicates 3 things:
- the message
- the origin team
- the origin absolute angle (set to "self" if the message was sent by oneself)
Messages are heard in the next step.
Messages are only sent every 2 steps (0.04s).
Messages sent in muted steps are only heard by oneself.
In one time step, a player can only hear one other player besides itself.
If two other players say something, only the first message is heard.
This ability exists independetly for messages from both teams.
In theory, a player can hear its own message + the 1st teammate to speak + the 1st opponent to speak
In practice, the opponent doesn't matter because our team's parser ignores messages from other teams
Message characteristics:
Maximum 20 characters, ascii between 0x20, 0x7E except ' ', '(', ')'
Accepted: letters+numbers+symbols: !"#$%&'*+,-./:;<=>?@[\]^_`{|}~
However, due to a server bug, sending ' or " ends the message sooner
'''
class Team_Communication():
def __init__(self,script:Script) -> None:
self.script = script
def player1_hear(self, msg:bytes, direction, timestamp:float) -> None:
print(f"Player 1 heard: {msg.decode():20} from:{direction:7} timestamp:{timestamp}")
def player2_hear(self, msg:bytes, direction, timestamp:float) -> None:
print(f"Player 2 heard: {msg.decode():20} from:{direction:7} timestamp:{timestamp}")
def player3_hear(self, msg:bytes, direction, timestamp:float) -> None:
print(f"Player 3 heard: {msg.decode():20} from:{direction:7} timestamp:{timestamp}")
def execute(self):
a = self.script.args
hear_callbacks = (self.player1_hear, self.player2_hear, self.player3_hear)
# Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw, Play Mode Correction, Wait for Server, Hear Callback
self.script.batch_create(Agent, ((a.i,a.p,a.m,i+1,0,a.t,True,True,False,True,clbk) for i,clbk in enumerate(hear_callbacks)))
p1:Agent = self.script.players[0]
p2:Agent = self.script.players[1]
p3:Agent = self.script.players[2]
# Beam players
self.script.batch_commit_beam( [(-2,i,45) for i in range(3)] )
for i in count():
msg1 = b"I_am_p1!_no:"+str(i).encode()
msg2 = b"I_am_p2!_no:"+str(i).encode()
msg3 = b"I_am_p3!_no:"+str(i).encode()
p1.scom.commit_announcement(msg1) # commit message
p2.scom.commit_announcement(msg2) # commit message
p3.scom.commit_announcement(msg3) # commit message
self.script.batch_commit_and_send() # send message
print(f"Player 1 sent: {msg1.decode()} HEX: {' '.join([f'{m:02X}' for m in msg1])}")
print(f"Player 2 sent: {msg2.decode()} HEX: {' '.join([f'{m:02X}' for m in msg2])}")
print(f"Player 3 sent: {msg3.decode()} HEX: {' '.join([f'{m:02X}' for m in msg3])}")
self.script.batch_receive()
input("Press enter to continue or ctrl+c to return.")
|
2302_81918214/robocup3D
|
FCPCodebase-main/scripts/utils/Team_Communication.py
|
Python
|
agpl-3.0
| 3,585
|
#!/bin/bash
export OMP_NUM_THREADS=1
host=${1:-localhost}
port=${2:-3100}
for i in {1..11}; do
python3 ./Run_Player.py -i $host -p $port -u $i -t FCPortugal -P 0 -D 0 &
done
|
2302_81918214/robocup3D
|
FCPCodebase-main/start.sh
|
Shell
|
agpl-3.0
| 177
|
#!/bin/bash
export OMP_NUM_THREADS=1
host=${1:-localhost}
port=${2:-3100}
for i in {1..11}; do
python3 ./Run_Player.py -i $host -p $port -u $i -t FCP-debug -P 0 -D 1 &
done
|
2302_81918214/robocup3D
|
FCPCodebase-main/start_debug.sh
|
Shell
|
agpl-3.0
| 176
|
#!/bin/bash
export OMP_NUM_THREADS=1
host=${1:-localhost}
port=${2:-3100}
for i in {1..11}; do
python3 ./Run_Player.py -i $host -p $port -u $i -t FCPortugal -F 1 -D 0 &
done
|
2302_81918214/robocup3D
|
FCPCodebase-main/start_fat_proxy.sh
|
Shell
|
agpl-3.0
| 177
|
#!/bin/bash
export OMP_NUM_THREADS=1
host=${1:-localhost}
port=${2:-3100}
for i in {1..11}; do
python3 ./Run_Player.py -i $host -p $port -u $i -t FCP-debug -F 1 -D 1 &
done
|
2302_81918214/robocup3D
|
FCPCodebase-main/start_fat_proxy_debug.sh
|
Shell
|
agpl-3.0
| 176
|
#!/bin/bash
export OMP_NUM_THREADS=1
host=${1:-localhost}
port=${2:-3100}
python3 ./Run_Player.py -i $host -p $port -u 1 -t FCPortugal -P 1 -D 0 &
python3 ./Run_Player.py -i $host -p $port -u 11 -t FCPortugal -P 1 -D 0 &
|
2302_81918214/robocup3D
|
FCPCodebase-main/start_penalty.sh
|
Shell
|
agpl-3.0
| 223
|
#!/bin/bash
export OMP_NUM_THREADS=1
host=${1:-localhost}
port=${2:-3100}
python3 ./Run_Player.py -i $host -p $port -u 1 -t FCP-debug -P 1 -D 1 &
python3 ./Run_Player.py -i $host -p $port -u 11 -t FCP-debug -P 1 -D 1 &
|
2302_81918214/robocup3D
|
FCPCodebase-main/start_penalty_debug.sh
|
Shell
|
agpl-3.0
| 221
|
from collections import deque
from math import atan, pi, sqrt, tan
from math_ops.Math_Ops import Math_Ops as M
from math_ops.Matrix_3x3 import Matrix_3x3
from math_ops.Matrix_4x4 import Matrix_4x4
from world.commons.Body_Part import Body_Part
from world.commons.Joint_Info import Joint_Info
import numpy as np
import xml.etree.ElementTree as xmlp
class Robot():
STEPTIME = 0.02 # Fixed step time
VISUALSTEP = 0.04 # Fixed visual step time
SQ_STEPTIME = STEPTIME * STEPTIME
GRAVITY = np.array([0,0,-9.81])
IMU_DECAY = 0.996 #IMU's velocity decay
#------------------ constants to force symmetry in joints/effectors
MAP_PERCEPTOR_TO_INDEX = {"hj1":0, "hj2":1, "llj1":2, "rlj1":3,
"llj2":4, "rlj2":5, "llj3":6, "rlj3":7,
"llj4":8, "rlj4":9, "llj5":10,"rlj5":11,
"llj6":12,"rlj6":13,"laj1":14,"raj1":15,
"laj2":16,"raj2":17,"laj3":18,"raj3":19,
"laj4":20,"raj4":21,"llj7":22,"rlj7":23 }
# Fix symmetry issues 1a/4 (identification)
FIX_PERCEPTOR_SET = {'rlj2','rlj6','raj2','laj3','laj4'}
FIX_INDICES_LIST = [5,13,17,18,20]
# Recommended height for unofficial beam (near ground)
BEAM_HEIGHTS = [0.4, 0.43, 0.4, 0.46, 0.4]
def __init__(self, unum:int, robot_type:int) -> None:
robot_xml = "nao"+str(robot_type)+".xml" # Typical NAO file name
self.type = robot_type
self.beam_height = Robot.BEAM_HEIGHTS[robot_type]
self.no_of_joints = 24 if robot_type == 4 else 22
#Fix symmetry issues 1b/4 (identification)
self.FIX_EFFECTOR_MASK = np.ones(self.no_of_joints)
self.FIX_EFFECTOR_MASK[Robot.FIX_INDICES_LIST] = -1
self.body_parts = dict() # keys='body part names' (given by the robot's XML), values='Body_Part objects'
self.unum = unum # Robot's uniform number
self.gyro = np.zeros(3) # Angular velocity along the three axes of freedom of the robot's torso (deg/s)
self.acc = np.zeros(3) # Proper acceleration along the three axes of freedom of the robot's torso (m/s2)
self.frp = dict() # foot "lf"/"rf", toe "lf1"/"rf1" resistance perceptor (relative [p]oint of origin + [f]orce vector) e.g. {"lf":(px,py,pz,fx,fy,fz)}
self.feet_toes_last_touch = {"lf":0,"rf":0,"lf1":0,"rf1":0} # foot "lf"/"rf", toe "lf1"/"rf1" World.time_local_ms when foot/toe last touched any surface
self.feet_toes_are_touching = {"lf":False,"rf":False,"lf1":False,"rf1":False} # foot "lf"/"rf", toe "lf1"/"rf1" True if touching in last received server message
self.fwd_kinematics_list = None # List of body parts, ordered according to dependencies
self.rel_cart_CoM_position = np.zeros(3) # Center of Mass position, relative to head, in cartesian coordinates (m)
# Joint variables are optimized for performance / array operations
self.joints_position = np.zeros(self.no_of_joints) # Joints' angular position (deg)
self.joints_speed = np.zeros(self.no_of_joints) # Joints' angular speed (rad/s)
self.joints_target_speed = np.zeros(self.no_of_joints) # Joints' target speed (rad/s) (max: 6.1395 rad/s, see rcssserver3d/data/rsg/agent/nao/hingejoint.rsg)
self.joints_target_last_speed = np.zeros(self.no_of_joints) # Joints' last target speed (rad/s) (max: 6.1395 rad/s, see rcssserver3d/data/rsg/agent/nao/hingejoint.rsg)
self.joints_info = [None] * self.no_of_joints # Joints' constant information (see class Joint_Info)
self.joints_transform = [Matrix_4x4() for _ in range(self.no_of_joints)] # Joints' transformation matrix
# Localization variables relative to head
self.loc_head_to_field_transform = Matrix_4x4() # Transformation matrix from head to field
self.loc_field_to_head_transform = Matrix_4x4() # Transformation matrix from field to head
self.loc_rotation_head_to_field = Matrix_3x3() # Rotation matrix from head to field
self.loc_rotation_field_to_head = Matrix_3x3() # Rotation matrix from field to head
self.loc_head_position = np.zeros(3) # Absolute head position (m)
self.loc_head_position_history = deque(maxlen=40)# Absolute head position history (queue with up to 40 old positions at intervals of 0.04s, where index 0 is the previous position)
self.loc_head_velocity = np.zeros(3) # Absolute head velocity (m/s) (Warning: possibly noisy)
self.loc_head_orientation = 0 # Head orientation (deg)
self.loc_is_up_to_date = False # False if this is not a visual step, or not enough elements are visible
self.loc_last_update = 0 # World.time_local_ms when the localization was last updated
self.loc_head_position_last_update = 0 # World.time_local_ms when loc_head_position was last updated by vision or radio
self.radio_fallen_state = False # True if (radio says we fell) and (radio is significantly more recent than loc)
self.radio_last_update = 0 # World.time_local_ms when radio_fallen_state was last updated (and possibly loc_head_position)
# Localization variables relative to torso
self.loc_torso_to_field_rotation = Matrix_3x3() # Rotation matrix from torso to field
self.loc_torso_to_field_transform = Matrix_4x4() # Transformation matrix from torso to field
self.loc_torso_roll = 0 # Torso roll (deg)
self.loc_torso_pitch = 0 # Torso pitch (deg)
self.loc_torso_orientation = 0 # Torso orientation (deg)
self.loc_torso_inclination = 0 # Torso inclination (deg) (inclination of z-axis in relation to field z-axis)
self.loc_torso_position = np.zeros(3) # Absolute torso position (m)
self.loc_torso_velocity = np.zeros(3) # Absolute torso velocity (m/s)
self.loc_torso_acceleration = np.zeros(3) # Absolute Coordinate acceleration (m/s2)
# Other localization variables
self.cheat_abs_pos = np.zeros(3) # Absolute head position provided by the server as cheat (m)
self.cheat_ori = 0.0 # Absolute head orientation provided by the server as cheat (deg)
self.loc_CoM_position = np.zeros(3) # Absolute CoM position (m)
self.loc_CoM_velocity = np.zeros(3) # Absolute CoM velocity (m/s)
# Localization special variables
'''
self.loc_head_z is often equivalent to self.loc_head_position[2], but sometimes it differs.
There are situations in which the rotation and translation cannot be computed,
but the z-coordinate can still be found through vision, in which case:
self.loc_is_up_to_date is False
self.loc_head_z_is_up_to_date is True
It should be used in applications which rely on z as an independent coordinate, such
as detecting if the robot has fallen, or as an observation for machine learning.
It should NEVER be used for 3D transformations.
'''
self.loc_head_z = 0 # Absolute head position (z) - see above for explanation (m)
self.loc_head_z_is_up_to_date = False # False if this is not a visual step, or not enough elements are visible
self.loc_head_z_last_update = 0 # World.time_local_ms when loc_head_z was last computed
self.loc_head_z_vel = 0 # Absolute head velocity (z) (m/s)
# Localization + Gyroscope
# These variables are reliable. The gyroscope is used to update the rotation when waiting for the next visual cycle
self.imu_torso_roll = 0 # Torso roll (deg) (src: Localization + Gyro)
self.imu_torso_pitch = 0 # Torso pitch (deg) (src: Localization + Gyro)
self.imu_torso_orientation = 0 # Torso orientation (deg) (src: Localization + Gyro)
self.imu_torso_inclination = 0 # Torso inclination (deg) (src: Localization + Gyro)
self.imu_torso_to_field_rotation = Matrix_3x3() # Rotation matrix from torso to field (src: Localization + Gyro)
self.imu_last_visual_update = 0 # World.time_local_ms when the IMU data was last updated with visual information
# Localization + Gyroscope + Accelerometer
# Warning: these variables are unreliable, since small errors in the Localization Orientation lead to
# wrong acceleration -> wrong velocity -> wrong position
self.imu_weak_torso_to_field_transform = Matrix_4x4() # Transformation matrix from torso to field (src: Localization + Gyro + Acc)
self.imu_weak_head_to_field_transform = Matrix_4x4() # Transformation matrix from head to field (src: Localization + Gyro + Acc)
self.imu_weak_field_to_head_transform = Matrix_4x4() # Transformation matrix from field to head (src: Localization + Gyro + Acc)
self.imu_weak_torso_position = np.zeros(3) # Absolute torso position (m) (src: Localization + Gyro + Acc)
self.imu_weak_torso_velocity = np.zeros(3) # Absolute torso velocity (m/s) (src: Localization + Gyro + Acc)
self.imu_weak_torso_acceleration = np.zeros(3) # Absolute torso acceleration (m/s2) (src: Localization + Gyro + Acc)
self.imu_weak_torso_next_position = np.zeros(3) # Absolute position in next step estimate (m) (src: Localization + Gyro + Acc)
self.imu_weak_torso_next_velocity = np.zeros(3) # Absolute velocity in next step estimate (m/s) (src: Localization + Gyro + Acc)
self.imu_weak_CoM_position = np.zeros(3) # Absolute CoM position (m) (src: Localization + Gyro + Acc)
self.imu_weak_CoM_velocity = np.zeros(3) # Absolute CoM velocity (m/s) (src: Localization + Gyro + Acc)
#Using explicit variables to enable IDE suggestions
self.J_HEAD_YAW = 0
self.J_HEAD_PITCH = 1
self.J_LLEG_YAW_PITCH = 2
self.J_RLEG_YAW_PITCH = 3
self.J_LLEG_ROLL = 4
self.J_RLEG_ROLL = 5
self.J_LLEG_PITCH = 6
self.J_RLEG_PITCH = 7
self.J_LKNEE = 8
self.J_RKNEE = 9
self.J_LFOOT_PITCH = 10
self.J_RFOOT_PITCH = 11
self.J_LFOOT_ROLL = 12
self.J_RFOOT_ROLL = 13
self.J_LARM_PITCH = 14
self.J_RARM_PITCH = 15
self.J_LARM_ROLL = 16
self.J_RARM_ROLL = 17
self.J_LELBOW_YAW = 18
self.J_RELBOW_YAW = 19
self.J_LELBOW_ROLL = 20
self.J_RELBOW_ROLL = 21
self.J_LTOE_PITCH = 22
self.J_RTOE_PITCH = 23
#------------------ parse robot xml
dir = M.get_active_directory("/world/commons/robots/")
robot_xml_root = xmlp.parse(dir + robot_xml).getroot()
joint_no = 0
for child in robot_xml_root:
if child.tag == "bodypart":
self.body_parts[child.attrib['name']] = Body_Part(child.attrib['mass'])
elif child.tag == "joint":
self.joints_info[joint_no] = Joint_Info(child)
self.joints_position[joint_no] = 0.0
ji = self.joints_info[joint_no]
#save joint if body part is 1st anchor (to simplify model traversal in a single direction)
self.body_parts[ji.anchor0_part].joints.append(Robot.MAP_PERCEPTOR_TO_INDEX[ji.perceptor])
joint_no += 1
if joint_no == self.no_of_joints: break #ignore extra joints
else:
raise NotImplementedError
assert joint_no == self.no_of_joints, "The Robot XML and the robot type don't match!"
def get_head_abs_vel(self, history_steps:int):
'''
Get robot's head absolute velocity (m/s)
Parameters
----------
history_steps : int
number of history steps to consider [1,40]
Examples
--------
get_head_abs_vel(1) is equivalent to (current abs pos - last abs pos) / 0.04
get_head_abs_vel(2) is equivalent to (current abs pos - abs pos 0.08s ago) / 0.08
get_head_abs_vel(3) is equivalent to (current abs pos - abs pos 0.12s ago) / 0.12
'''
assert 1 <= history_steps <= 40, "Argument 'history_steps' must be in range [1,40]"
if len(self.loc_head_position_history) == 0:
return np.zeros(3)
h_step = min(history_steps, len(self.loc_head_position_history))
t = h_step * Robot.VISUALSTEP
return (self.loc_head_position - self.loc_head_position_history[h_step-1]) / t
def _initialize_kinematics(self):
#starting with head
parts={"head"}
sequential_body_parts = ["head"]
while len(parts) > 0:
part = parts.pop()
for j in self.body_parts[part].joints:
p = self.joints_info[j].anchor1_part
if len(self.body_parts[p].joints) > 0: #add body part if it is the 1st anchor of some joint
parts.add(p)
sequential_body_parts.append(p)
self.fwd_kinematics_list = [(self.body_parts[part],j, self.body_parts[self.joints_info[j].anchor1_part] )
for part in sequential_body_parts for j in self.body_parts[part].joints]
#Fix symmetry issues 4/4 (kinematics)
for i in Robot.FIX_INDICES_LIST:
self.joints_info[i].axes *= -1
aux = self.joints_info[i].min
self.joints_info[i].min = -self.joints_info[i].max
self.joints_info[i].max = -aux
def update_localization(self, localization_raw, time_local_ms):
# parse raw data
loc = localization_raw.astype(float) #32bits to 64bits for consistency
self.loc_is_up_to_date = bool(loc[32])
self.loc_head_z_is_up_to_date = bool(loc[34])
if self.loc_head_z_is_up_to_date:
time_diff = (time_local_ms - self.loc_head_z_last_update) / 1000
self.loc_head_z_vel = (loc[33] - self.loc_head_z) / time_diff
self.loc_head_z = loc[33]
self.loc_head_z_last_update = time_local_ms
# Save last position to history at every vision cycle (even if not up to date) (update_localization is only called at vision cycles)
self.loc_head_position_history.appendleft(np.copy(self.loc_head_position))
if self.loc_is_up_to_date:
time_diff = (time_local_ms - self.loc_last_update) / 1000
self.loc_last_update = time_local_ms
self.loc_head_to_field_transform.m[:] = loc[0:16].reshape((4,4))
self.loc_field_to_head_transform.m[:] = loc[16:32].reshape((4,4))
# extract data (related to the robot's head)
self.loc_rotation_head_to_field = self.loc_head_to_field_transform.get_rotation()
self.loc_rotation_field_to_head = self.loc_field_to_head_transform.get_rotation()
p = self.loc_head_to_field_transform.get_translation()
self.loc_head_velocity = (p - self.loc_head_position) / time_diff
self.loc_head_position = p
self.loc_head_position_last_update = time_local_ms
self.loc_head_orientation = self.loc_head_to_field_transform.get_yaw_deg()
self.radio_fallen_state = False
# extract data (related to the center of mass)
p = self.loc_head_to_field_transform(self.rel_cart_CoM_position)
self.loc_CoM_velocity = (p - self.loc_CoM_position) / time_diff
self.loc_CoM_position = p
# extract data (related to the robot's torso)
t = self.get_body_part_to_field_transform('torso')
self.loc_torso_to_field_transform = t
self.loc_torso_to_field_rotation = t.get_rotation()
self.loc_torso_orientation = t.get_yaw_deg()
self.loc_torso_pitch = t.get_pitch_deg()
self.loc_torso_roll = t.get_roll_deg()
self.loc_torso_inclination = t.get_inclination_deg()
p = t.get_translation()
self.loc_torso_velocity = (p - self.loc_torso_position) / time_diff
self.loc_torso_position = p
self.loc_torso_acceleration = self.loc_torso_to_field_rotation.multiply(self.acc) + Robot.GRAVITY
def head_to_body_part_transform(self, body_part_name, coords, is_batch=False):
'''
If coord is a vector or list of vectors:
Convert cartesian coordinates that are relative to head to coordinates that are relative to a body part
If coord is a Matrix_4x4 or a list of Matrix_4x4:
Convert pose that is relative to head to a pose that is relative to a body part
Parameters
----------
body_part_name : `str`
name of body part (given by the robot's XML)
coords : array_like
One 3D position or list of 3D positions
is_batch : `bool`
Indicates if coords is a batch of 3D positions
Returns
-------
coord : `list` or ndarray
A numpy array is returned if is_batch is False, otherwise, a list of arrays is returned
'''
head_to_bp_transform : Matrix_4x4 = self.body_parts[body_part_name].transform.invert()
if is_batch:
return [head_to_bp_transform(c) for c in coords]
else:
return head_to_bp_transform(coords)
def get_body_part_to_field_transform(self, body_part_name) -> Matrix_4x4:
'''
Computes the transformation matrix from body part to field, from which we can extract its absolute position and rotation.
For best results, use this method when self.loc_is_up_to_date is True. Otherwise, the forward kinematics
will not be synced with the localization data and strange results may occur.
'''
return self.loc_head_to_field_transform.multiply(self.body_parts[body_part_name].transform)
def get_body_part_abs_position(self, body_part_name) -> np.ndarray:
'''
Computes the absolute position of a body part considering the localization data and forward kinematics.
For best results, use this method when self.loc_is_up_to_date is True. Otherwise, the forward kinematics
will not be synced with the localization data and strange results may occur.
'''
return self.get_body_part_to_field_transform(body_part_name).get_translation()
def get_joint_to_field_transform(self, joint_index) -> Matrix_4x4:
'''
Computes the transformation matrix from joint to field, from which we can extract its absolute position and rotation.
For best results, use this method when self.loc_is_up_to_date is True. Otherwise, the forward kinematics
will not be synced with the localization data and strange results may occur.
'''
return self.loc_head_to_field_transform.multiply(self.joints_transform[joint_index])
def get_joint_abs_position(self, joint_index) -> np.ndarray:
'''
Computes the absolute position of a joint considering the localization data and forward kinematics.
For best results, use this method when self.loc_is_up_to_date is True. Otherwise, the forward kinematics
will not be synced with the localization data and strange results may occur.
'''
return self.get_joint_to_field_transform(joint_index).get_translation()
def update_pose(self):
if self.fwd_kinematics_list is None:
self._initialize_kinematics()
for body_part, j, child_body_part in self.fwd_kinematics_list:
ji = self.joints_info[j]
self.joints_transform[j].m[:] = body_part.transform.m
self.joints_transform[j].translate(ji.anchor0_axes, True)
child_body_part.transform.m[:] = self.joints_transform[j].m
child_body_part.transform.rotate_deg(ji.axes, self.joints_position[j], True)
child_body_part.transform.translate(ji.anchor1_axes_neg, True)
self.rel_cart_CoM_position = np.average([b.transform.get_translation() for b in self.body_parts.values()], 0,
[b.mass for b in self.body_parts.values()])
def update_imu(self, time_local_ms):
# update IMU
if self.loc_is_up_to_date:
self.imu_torso_roll = self.loc_torso_roll
self.imu_torso_pitch = self.loc_torso_pitch
self.imu_torso_orientation = self.loc_torso_orientation
self.imu_torso_inclination = self.loc_torso_inclination
self.imu_torso_to_field_rotation.m[:] = self.loc_torso_to_field_rotation.m
self.imu_weak_torso_to_field_transform.m[:] = self.loc_torso_to_field_transform.m
self.imu_weak_head_to_field_transform.m[:] = self.loc_head_to_field_transform.m
self.imu_weak_field_to_head_transform.m[:] = self.loc_field_to_head_transform.m
self.imu_weak_torso_position[:] = self.loc_torso_position
self.imu_weak_torso_velocity[:] = self.loc_torso_velocity
self.imu_weak_torso_acceleration[:] = self.loc_torso_acceleration
self.imu_weak_torso_next_position = self.loc_torso_position + self.loc_torso_velocity * Robot.STEPTIME + self.loc_torso_acceleration * (0.5 * Robot.SQ_STEPTIME)
self.imu_weak_torso_next_velocity = self.loc_torso_velocity + self.loc_torso_acceleration * Robot.STEPTIME
self.imu_weak_CoM_position[:] = self.loc_CoM_position
self.imu_weak_CoM_velocity[:] = self.loc_CoM_velocity
self.imu_last_visual_update = time_local_ms
else:
g = self.gyro / 50 # convert degrees per second to degrees per step
self.imu_torso_to_field_rotation.multiply( Matrix_3x3.from_rotation_deg(g), in_place=True, reverse_order=True)
self.imu_torso_orientation = self.imu_torso_to_field_rotation.get_yaw_deg()
self.imu_torso_pitch = self.imu_torso_to_field_rotation.get_pitch_deg()
self.imu_torso_roll = self.imu_torso_to_field_rotation.get_roll_deg()
self.imu_torso_inclination = atan(sqrt(tan(self.imu_torso_roll/180*pi)**2+tan(self.imu_torso_pitch/180*pi)**2))*180/pi
# Update position and velocity until 0.2 seconds has passed since last visual update
if time_local_ms < self.imu_last_visual_update + 200:
self.imu_weak_torso_position[:] = self.imu_weak_torso_next_position
if self.imu_weak_torso_position[2] < 0: self.imu_weak_torso_position[2] = 0 # limit z coordinate to positive values
self.imu_weak_torso_velocity[:] = self.imu_weak_torso_next_velocity * Robot.IMU_DECAY # stability tradeoff
else:
self.imu_weak_torso_velocity *= 0.97 # without visual updates for 0.2s, the position is locked, and the velocity decays to zero
# convert proper acceleration to coordinate acceleration and fix rounding bias
self.imu_weak_torso_acceleration = self.imu_torso_to_field_rotation.multiply(self.acc) + Robot.GRAVITY
self.imu_weak_torso_to_field_transform = Matrix_4x4.from_3x3_and_translation(self.imu_torso_to_field_rotation,self.imu_weak_torso_position)
self.imu_weak_head_to_field_transform = self.imu_weak_torso_to_field_transform.multiply(self.body_parts["torso"].transform.invert())
self.imu_weak_field_to_head_transform = self.imu_weak_head_to_field_transform.invert()
p = self.imu_weak_head_to_field_transform(self.rel_cart_CoM_position)
self.imu_weak_CoM_velocity = (p-self.imu_weak_CoM_position)/Robot.STEPTIME
self.imu_weak_CoM_position = p
# Next Position = x0 + v0*t + 0.5*a*t^2, Next velocity = v0 + a*t
self.imu_weak_torso_next_position = self.imu_weak_torso_position + self.imu_weak_torso_velocity * Robot.STEPTIME + self.imu_weak_torso_acceleration * (0.5 * Robot.SQ_STEPTIME)
self.imu_weak_torso_next_velocity = self.imu_weak_torso_velocity + self.imu_weak_torso_acceleration * Robot.STEPTIME
def set_joints_target_position_direct(self,indices,values:np.ndarray,harmonize=True,max_speed=7.03,tolerance=0.012,limit_joints=True) -> int:
'''
Computes the speed of a list of joints, taking as argument the target position
Parameters
----------
indices : `int`/`list`/`slice`/numpy array
joint indices
values : numpy array
target position for each listed joint index
harmonize : `bool`
if True, all joints reach target at same time
max_speed : `float`
max. speed for all joints in deg/step
Most joints have a maximum speed of 351.77 deg/s according to rcssserver3d/data/rsg/agent/nao/hingejoint.rsg
That translates as 7.0354 deg/step or 6.1395 rad/s
tolerance : `float`
angle error tolerance (in degrees) to return that target was reached (returns -1)
limit_joints : `bool`
limit values to the joints' range of motion
Returns
-------
remaining_steps : `int`
predicted number of remaining steps or -1 if target was already reached
Examples
-------
(let p[tx] be the joint position at t=x)
Example for return value: moving joint[0] from 0deg to 10deg
pos[t0]: 0, speed[t0]: 7deg/step, ret=2 # target will predictedly be reached in 2 steps
pos[t1]: 7, speed[t1]: 3deg/step, ret=1 # target will predictedly be reached in 1 step (send final action)
pos[t2]: 10, speed[t2]: 0deg/step, ret=0 # target was predictedly already reached
pos[t3]: 10, speed[t3]: 0deg/step, ret=-1 # (best case scenario) server reported with delay, that target was reached (see tolerance)
pos[t?]: 10, speed[t?]: 0deg/step, ret=-1 # if there is friction, it may take some additional steps
If everything worked as predicted we could stop calling this function when ret==1
If we need precision, it is recommended to wait for ret==-1
Example 1:
set_joints_target_position_direct(range(2,4),np.array([10.0,5.0]),harmonize=True)
Joint[2] p[t0]: 0 target pos: 10 -> p[t1]=5, p[t2]=10
Joint[3] p[t0]: 0 target pos: 5 -> p[t1]=2.5, p[t2]=5
Example 2:
set_joints_target_position_direct([2,3],np.array([10.0,5.0]),harmonize=False)
Joint[2] p[t0]: 0 target pos: 10 -> p[t1]=7, p[t2]=10
Joint[3] p[t0]: 0 target pos: 5 -> p[t1]=5, p[t2]=5
'''
assert type(values) == np.ndarray, "'values' argument must be a numpy array"
np.nan_to_num(values, copy=False) # Replace NaN with zero and infinity with large finite numbers
# limit range of joints
if limit_joints:
if type(indices) == list or type(indices) == np.ndarray:
for i in range(len(indices)):
values[i] = np.clip(values[i], self.joints_info[indices[i]].min, self.joints_info[indices[i]].max)
elif type(indices) == slice:
info = self.joints_info[indices]
for i in range(len(info)):
values[i] = np.clip(values[i], info[i].min, info[i].max)
else: # int
values[0] = np.clip(values[0], self.joints_info[indices].min, self.joints_info[indices].max)
#predicted_diff: predicted difference between reported position and actual position
predicted_diff = self.joints_target_last_speed[indices] * 1.1459156 #rad/s to deg/step
predicted_diff = np.asarray(predicted_diff)
np.clip(predicted_diff,-7.03,7.03,out=predicted_diff) #saturate predicted movement in-place
#reported_dist: difference between reported position and target position
reported_dist = values - self.joints_position[indices]
if np.all((np.abs(reported_dist) < tolerance)) and np.all((np.abs(predicted_diff) < tolerance)):
self.joints_target_speed[indices] = 0
return -1
deg_per_step = reported_dist - predicted_diff
relative_max = np.max( np.abs(deg_per_step) ) / max_speed
remaining_steps = np.ceil( relative_max )
if remaining_steps == 0:
self.joints_target_speed[indices] = 0
return 0
if harmonize:
deg_per_step /= remaining_steps
else:
np.clip(deg_per_step,-max_speed,max_speed,out=deg_per_step) #limit maximum speed
self.joints_target_speed[indices] = deg_per_step * 0.87266463 #convert to rad/s
return remaining_steps
def get_command(self) -> bytes:
'''
Builds commands string from self.joints_target_speed
'''
j_speed = self.joints_target_speed * self.FIX_EFFECTOR_MASK #Fix symmetry issues 3/4 (effectors)
cmd = "".join(f"({self.joints_info[i].effector} {j_speed[i]:.5f})" for i in range(self.no_of_joints)).encode('utf-8')
self.joints_target_last_speed = self.joints_target_speed #1. both point to the same array
self.joints_target_speed = np.zeros_like(self.joints_target_speed) #2. create new array for joints_target_speed
return cmd
|
2302_81918214/robocup3D
|
FCPCodebase-main/world/Robot.py
|
Python
|
agpl-3.0
| 30,248
|
from collections import deque
from cpp.ball_predictor import ball_predictor
from cpp.localization import localization
from logs.Logger import Logger
from math import atan2, pi
from math_ops.Matrix_4x4 import Matrix_4x4
from world.commons.Draw import Draw
from world.commons.Other_Robot import Other_Robot
from world.Robot import Robot
import numpy as np
class World():
STEPTIME = 0.02 # Fixed step time
STEPTIME_MS = 20 # Fixed step time in milliseconds
VISUALSTEP = 0.04 # Fixed visual step time
VISUALSTEP_MS = 40 # Fixed visual step time in milliseconds
# play modes in our favor
M_OUR_KICKOFF = 0
M_OUR_KICK_IN = 1
M_OUR_CORNER_KICK = 2
M_OUR_GOAL_KICK = 3
M_OUR_FREE_KICK = 4
M_OUR_PASS = 5
M_OUR_DIR_FREE_KICK = 6
M_OUR_GOAL = 7
M_OUR_OFFSIDE = 8
# play modes in their favor
M_THEIR_KICKOFF = 9
M_THEIR_KICK_IN = 10
M_THEIR_CORNER_KICK = 11
M_THEIR_GOAL_KICK = 12
M_THEIR_FREE_KICK = 13
M_THEIR_PASS = 14
M_THEIR_DIR_FREE_KICK = 15
M_THEIR_GOAL = 16
M_THEIR_OFFSIDE = 17
# neutral play modes
M_BEFORE_KICKOFF = 18
M_GAME_OVER = 19
M_PLAY_ON = 20
# play mode groups
MG_OUR_KICK = 0
MG_THEIR_KICK = 1
MG_ACTIVE_BEAM = 2
MG_PASSIVE_BEAM = 3
MG_OTHER = 4 # play on, game over
FLAGS_CORNERS_POS = ((-15,-10,0), (-15,+10,0), (+15,-10,0), (+15,+10,0))
FLAGS_POSTS_POS = ((-15,-1.05,0.8),(-15,+1.05,0.8),(+15,-1.05,0.8),(+15,+1.05,0.8))
def __init__(self,robot_type:int, team_name:str, unum:int, apply_play_mode_correction:bool,
enable_draw:bool, logger:Logger, host:str) -> None:
self.team_name = team_name # Name of our team
self.team_name_opponent : str = None # Name of opponent team
self.apply_play_mode_correction = apply_play_mode_correction # True to adjust ball position according to play mode
self.step = 0 # Total number of received simulation steps (always in sync with self.time_local_ms)
self.time_server = 0.0 # Time, in seconds, as indicated by the server (this time is NOT reliable, use only for synchronization between agents)
self.time_local_ms = 0 # Reliable simulation time in milliseconds, use this when possible (it is incremented 20ms for every TCP message)
self.time_game = 0.0 # Game time, in seconds, as indicated by the server
self.goals_scored = 0 # Goals score by our team
self.goals_conceded = 0 # Goals conceded by our team
self.team_side_is_left : bool = None # True if our team plays on the left side (this value is later changed by the world parser)
self.play_mode = None # Play mode of the soccer game, provided by the server
self.play_mode_group = None # Certain play modes share characteristics, so it makes sense to group them
self.flags_corners : dict = None # corner flags, key=(x,y,z), always assume we play on the left side
self.flags_posts : dict = None # goal posts, key=(x,y,z), always assume we play on the left side
self.ball_rel_head_sph_pos = np.zeros(3) # Ball position relative to head (spherical coordinates) (m, deg, deg)
self.ball_rel_head_cart_pos = np.zeros(3) # Ball position relative to head (cartesian coordinates) (m)
self.ball_rel_torso_cart_pos = np.zeros(3) # Ball position relative to torso (cartesian coordinates) (m)
self.ball_rel_torso_cart_pos_history = deque(maxlen=20) # Ball position relative to torso history (queue with up to 20 old positions at intervals of 0.04s, where index 0 is the previous position)
self.ball_abs_pos = np.zeros(3) # Ball absolute position (up to date if self.ball_is_visible and self.robot.loc_is_up_to_date) (m)
self.ball_abs_pos_history = deque(maxlen=20) # Ball absolute position history (queue with up to 20 old positions at intervals of 0.04s, where index 0 is the previous position)
self.ball_abs_pos_last_update = 0 # World.time_local_ms when self.ball_abs_pos was last updated by vision or radio
self.ball_abs_vel = np.zeros(3) # Ball velocity vector based on the last 2 known values of self.ball_abs_pos (m/s) (Warning: noisy if ball is distant, use instead get_ball_abs_vel)
self.ball_abs_speed = 0 # Ball scalar speed based on the last 2 known values of self.ball_abs_pos (m/s) (Warning: noisy if ball is distant, use instead ||get_ball_abs_vel||)
self.ball_is_visible = False # True if the last server message contained vision information related to the ball
self.is_ball_abs_pos_from_vision = False # True if ball_abs_pos originated from vision, False if it originated from radio
self.ball_last_seen = 0 # World.time_local_ms when ball was last seen (note: may be different from self.ball_abs_pos_last_update)
self.ball_cheat_abs_pos = np.zeros(3) # Absolute ball position provided by the server as cheat (m)
self.ball_cheat_abs_vel = np.zeros(3) # Absolute velocity vector based on the last 2 values of self.ball_cheat_abs_pos (m/s)
self.ball_2d_pred_pos = np.zeros((1,2)) # prediction of current and future 2D ball positions*
self.ball_2d_pred_vel = np.zeros((1,2)) # prediction of current and future 2D ball velocities*
self.ball_2d_pred_spd = np.zeros(1) # prediction of current and future 2D ball linear speeds*
# *at intervals of 0.02 s until ball comes to a stop or gets out of bounds (according to prediction)
self.lines = np.zeros((30,6)) # Position of visible lines, relative to head, start_pos+end_pos (spherical coordinates) (m, deg, deg, m, deg, deg)
self.line_count = 0 # Number of visible lines
self.vision_last_update = 0 # World.time_local_ms when last vision update was received
self.vision_is_up_to_date = False # True if the last server message contained vision information
self.teammates = [Other_Robot(i, True ) for i in range(1,12)] # List of teammates, ordered by unum
self.opponents = [Other_Robot(i, False) for i in range(1,12)] # List of opponents, ordered by unum
self.teammates[unum-1].is_self = True # This teammate is self
self.draw = Draw(enable_draw, unum, host, 32769) # Draw object for current player
self.team_draw = Draw(enable_draw, 0, host, 32769) # Draw object shared with teammates
self.logger = logger
self.robot = Robot(unum, robot_type)
def log(self, msg:str):
'''
Shortcut for:
self.logger.write(msg, True, self.step)
Parameters
----------
msg : str
message to be written after the simulation step
'''
self.logger.write(msg, True, self.step)
def get_ball_rel_vel(self, history_steps:int):
'''
Get ball velocity, relative to torso (m/s)
Parameters
----------
history_steps : int
number of history steps to consider [1,20]
Examples
--------
get_ball_rel_vel(1) is equivalent to (current rel pos - last rel pos) / 0.04
get_ball_rel_vel(2) is equivalent to (current rel pos - rel pos 0.08s ago) / 0.08
get_ball_rel_vel(3) is equivalent to (current rel pos - rel pos 0.12s ago) / 0.12
'''
assert 1 <= history_steps <= 20, "Argument 'history_steps' must be in range [1,20]"
if len(self.ball_rel_torso_cart_pos_history) == 0:
return np.zeros(3)
h_step = min(history_steps, len(self.ball_rel_torso_cart_pos_history))
t = h_step * World.VISUALSTEP
return (self.ball_rel_torso_cart_pos - self.ball_rel_torso_cart_pos_history[h_step-1]) / t
def get_ball_abs_vel(self, history_steps:int):
'''
Get ball absolute velocity (m/s)
Parameters
----------
history_steps : int
number of history steps to consider [1,20]
Examples
--------
get_ball_abs_vel(1) is equivalent to (current abs pos - last abs pos) / 0.04
get_ball_abs_vel(2) is equivalent to (current abs pos - abs pos 0.08s ago) / 0.08
get_ball_abs_vel(3) is equivalent to (current abs pos - abs pos 0.12s ago) / 0.12
'''
assert 1 <= history_steps <= 20, "Argument 'history_steps' must be in range [1,20]"
if len(self.ball_abs_pos_history) == 0:
return np.zeros(3)
h_step = min(history_steps, len(self.ball_abs_pos_history))
t = h_step * World.VISUALSTEP
return (self.ball_abs_pos - self.ball_abs_pos_history[h_step-1]) / t
def get_predicted_ball_pos(self, max_speed):
'''
Get predicted 2D ball position when its predicted speed is equal to or less than `max_speed`
In case that position exceeds the prediction horizon, the last available prediction is returned
Parameters
----------
max_speed : float
maximum speed at which the ball will be moving at returned future position
'''
b_sp = self.ball_2d_pred_spd
index = len(b_sp) - max( 1, np.searchsorted(b_sp[::-1], max_speed, side='right') )
return self.ball_2d_pred_pos[index]
def get_intersection_point_with_ball(self, player_speed):
'''
Get 2D intersection point with moving ball, based on `self.ball_2d_pred_pos`
Parameters
----------
player_speed : float
average speed at which the robot will chase the ball
Returns
-------
2D intersection point : ndarray
2D intersection point with moving ball, assuming the robot moves at an avg. speed of `player_speed`
intersection distance : float
distance between current robot position and intersection point
'''
params = np.array([*self.robot.loc_head_position[:2], player_speed*0.02, *self.ball_2d_pred_pos.flat], np.float32)
pred_ret = ball_predictor.get_intersection(params)
return pred_ret[:2], pred_ret[2]
def update(self):
r = self.robot
PM = self.play_mode
W = World
# reset variables
r.loc_is_up_to_date = False
r.loc_head_z_is_up_to_date = False
# update play mode groups
if PM in (W.M_PLAY_ON, W.M_GAME_OVER): # most common group
self.play_mode_group = W.MG_OTHER
elif PM in (W.M_OUR_KICKOFF, W.M_OUR_KICK_IN, W.M_OUR_CORNER_KICK, W.M_OUR_GOAL_KICK,
W.M_OUR_OFFSIDE, W.M_OUR_PASS, W.M_OUR_DIR_FREE_KICK, W.M_OUR_FREE_KICK):
self.play_mode_group = W.MG_OUR_KICK
elif PM in (W.M_THEIR_KICK_IN, W.M_THEIR_CORNER_KICK, W.M_THEIR_GOAL_KICK, W.M_THEIR_OFFSIDE,
W.M_THEIR_PASS, W.M_THEIR_DIR_FREE_KICK, W.M_THEIR_FREE_KICK, W.M_THEIR_KICKOFF):
self.play_mode_group = W.MG_THEIR_KICK
elif PM in (W.M_BEFORE_KICKOFF, W.M_THEIR_GOAL):
self.play_mode_group = W.MG_ACTIVE_BEAM
elif PM in (W.M_OUR_GOAL,):
self.play_mode_group = W.MG_PASSIVE_BEAM
elif PM is not None:
raise ValueError(f'Unexpected play mode ID: {PM}')
r.update_pose() # update forward kinematics
if self.ball_is_visible:
# Compute ball position, relative to torso
self.ball_rel_torso_cart_pos = r.head_to_body_part_transform("torso",self.ball_rel_head_cart_pos)
if self.vision_is_up_to_date: # update vision based localization
# Prepare all variables for localization
feet_contact = np.zeros(6)
lf_contact = r.frp.get('lf', None)
rf_contact = r.frp.get('rf', None)
if lf_contact is not None:
feet_contact[0:3] = Matrix_4x4( r.body_parts["lfoot"].transform ).translate( lf_contact[0:3] , True).get_translation()
if rf_contact is not None:
feet_contact[3:6] = Matrix_4x4( r.body_parts["rfoot"].transform ).translate( rf_contact[0:3] , True).get_translation()
ball_pos = np.concatenate(( self.ball_rel_head_cart_pos, self.ball_cheat_abs_pos))
corners_list = [[key in self.flags_corners, 1.0, *key, *self.flags_corners.get(key,(0,0,0))] for key in World.FLAGS_CORNERS_POS]
posts_list = [[key in self.flags_posts , 0.0, *key, *self.flags_posts.get( key,(0,0,0))] for key in World.FLAGS_POSTS_POS]
all_landmarks = np.array(corners_list + posts_list, float)
# Compute localization
loc = localization.compute(
r.feet_toes_are_touching['lf'],
r.feet_toes_are_touching['rf'],
feet_contact,
self.ball_is_visible,
ball_pos,
r.cheat_abs_pos,
all_landmarks,
self.lines[0:self.line_count])
r.update_localization(loc, self.time_local_ms)
# Update self in teammates list (only the most useful parameters, add as needed)
me = self.teammates[r.unum-1]
me.state_last_update = r.loc_last_update
me.state_abs_pos = r.loc_head_position
me.state_fallen = r.loc_head_z < 0.3 # uses same criterion as for other teammates - not as reliable as player.behavior.is_ready("Get_Up")
me.state_orientation = r.loc_torso_orientation
me.state_ground_area = (r.loc_head_position[:2],0.2) # relevant for localization demo
# Save last ball position to history at every vision cycle (even if not up to date)
self.ball_abs_pos_history.appendleft(self.ball_abs_pos) # from vision or radio
self.ball_rel_torso_cart_pos_history.appendleft(self.ball_rel_torso_cart_pos)
'''
Get ball position based on vision or play mode
Sources:
Corner kick position - rcssserver3d/plugin/soccer/soccerruleaspect/soccerruleaspect.cpp:1927 (May 2022)
Goal kick position - rcssserver3d/plugin/soccer/soccerruleaspect/soccerruleaspect.cpp:1900 (May 2022)
'''
ball = None
if self.apply_play_mode_correction:
if PM == W.M_OUR_CORNER_KICK:
ball = np.array([15, 5.483 if self.ball_abs_pos[1] > 0 else -5.483, 0.042], float)
elif PM == W.M_THEIR_CORNER_KICK:
ball = np.array([-15, 5.483 if self.ball_abs_pos[1] > 0 else -5.483, 0.042], float)
elif PM in [W.M_OUR_KICKOFF, W.M_THEIR_KICKOFF, W.M_OUR_GOAL, W.M_THEIR_GOAL]:
ball = np.array([0, 0, 0.042], float)
elif PM == W.M_OUR_GOAL_KICK:
ball = np.array([-14, 0, 0.042], float)
elif PM == W.M_THEIR_GOAL_KICK:
ball = np.array([14, 0, 0.042], float)
# Discard hard-coded ball position if robot is near that position (in favor of its own vision)
if ball is not None and np.linalg.norm(r.loc_head_position[:2] - ball[:2]) < 1:
ball = None
if ball is None and self.ball_is_visible and r.loc_is_up_to_date:
ball = r.loc_head_to_field_transform( self.ball_rel_head_cart_pos )
ball[2] = max(ball[2], 0.042) # lowest z = ball radius
if PM != W.M_BEFORE_KICKOFF: # for compatibility with tests without active soccer rules
ball[:2] = np.clip(ball[:2], [-15,-10], [15,10]) # force ball position to be inside field
# Update internal ball position (also updated by Radio)
if ball is not None:
time_diff = (self.time_local_ms - self.ball_abs_pos_last_update) / 1000
self.ball_abs_vel = (ball - self.ball_abs_pos) / time_diff
self.ball_abs_speed = np.linalg.norm(self.ball_abs_vel)
self.ball_abs_pos_last_update = self.time_local_ms
self.ball_abs_pos = ball
self.is_ball_abs_pos_from_vision = True
# Velocity decay for teammates and opponents (it is later neutralized if the velocity is updated)
for p in self.teammates:
p.state_filtered_velocity *= p.vel_decay
for p in self.opponents:
p.state_filtered_velocity *= p.vel_decay
# Update teammates and opponents
if r.loc_is_up_to_date:
for p in self.teammates:
if not p.is_self: # if teammate is not self
if p.is_visible: # if teammate is visible, execute full update
self.update_other_robot(p)
elif p.state_abs_pos is not None: # otherwise update its horizontal distance (assuming last known position)
p.state_horizontal_dist = np.linalg.norm(r.loc_head_position[:2] - p.state_abs_pos[:2])
for p in self.opponents:
if p.is_visible: # if opponent is visible, execute full update
self.update_other_robot(p)
elif p.state_abs_pos is not None: # otherwise update its horizontal distance (assuming last known position)
p.state_horizontal_dist = np.linalg.norm(r.loc_head_position[:2] - p.state_abs_pos[:2])
# Update prediction of ball position/velocity
if self.play_mode_group != W.MG_OTHER: # not 'play on' nor 'game over', so ball must be stationary
self.ball_2d_pred_pos = self.ball_abs_pos[:2].copy().reshape(1, 2)
self.ball_2d_pred_vel = np.zeros((1,2))
self.ball_2d_pred_spd = np.zeros(1)
elif self.ball_abs_pos_last_update == self.time_local_ms: # make new prediction for new ball position (from vision or radio)
params = np.array([*self.ball_abs_pos[:2], *np.copy(self.get_ball_abs_vel(6)[:2])], np.float32)
pred_ret = ball_predictor.predict_rolling_ball(params)
sample_no = len(pred_ret) // 5 * 2
self.ball_2d_pred_pos = pred_ret[:sample_no].reshape(-1, 2)
self.ball_2d_pred_vel = pred_ret[sample_no:sample_no*2].reshape(-1, 2)
self.ball_2d_pred_spd = pred_ret[sample_no*2:]
elif len(self.ball_2d_pred_pos) > 1: # otherwise, advance to next predicted step, if available
self.ball_2d_pred_pos = self.ball_2d_pred_pos[1:]
self.ball_2d_pred_vel = self.ball_2d_pred_vel[1:]
self.ball_2d_pred_spd = self.ball_2d_pred_spd[1:]
r.update_imu(self.time_local_ms) # update imu (must be executed after localization)
def update_other_robot(self,other_robot : Other_Robot):
'''
Update other robot state based on the relative position of visible body parts
(also updated by Radio, with the exception of state_orientation)
'''
o = other_robot
r = self.robot
# update body parts absolute positions
o.state_body_parts_abs_pos = o.body_parts_cart_rel_pos.copy()
for bp, pos in o.body_parts_cart_rel_pos.items():
# Using the IMU could be beneficial if we see other robots but can't self-locate
o.state_body_parts_abs_pos[bp] = r.loc_head_to_field_transform( pos, False )
# auxiliary variables
bps_apos = o.state_body_parts_abs_pos # read-only shortcut
bps_2d_apos_list = [v[:2] for v in bps_apos.values()] # list of body parts' 2D absolute positions
avg_2d_pt = np.average(bps_2d_apos_list, axis=0) # 2D avg pos of visible body parts
head_is_visible = 'head' in bps_apos
# evaluate robot's state (unchanged if head is not visible)
if head_is_visible:
o.state_fallen = bps_apos['head'][2] < 0.3
# compute velocity if head is visible
if o.state_abs_pos is not None:
time_diff = (self.time_local_ms - o.state_last_update) / 1000
if head_is_visible:
# if last position is 2D, we assume that the z coordinate did not change, so that v.z=0
old_p = o.state_abs_pos if len(o.state_abs_pos)==3 else np.append(o.state_abs_pos, bps_apos['head'][2])
velocity = (bps_apos['head'] - old_p) / time_diff
decay = o.vel_decay # neutralize decay in all axes
else: # if head is not visible, we only update the x & y components of the velocity
velocity = np.append( (avg_2d_pt - o.state_abs_pos[:2]) / time_diff, 0)
decay = (o.vel_decay,o.vel_decay,1) # neutralize decay (except in the z-axis)
# apply filter
if np.linalg.norm(velocity - o.state_filtered_velocity) < 4: # otherwise assume it was beamed
o.state_filtered_velocity /= decay # neutralize decay
o.state_filtered_velocity += o.vel_filter * (velocity-o.state_filtered_velocity)
# compute robot's position (preferably based on head)
if head_is_visible:
o.state_abs_pos = bps_apos['head'] # 3D head position, if head is visible
else:
o.state_abs_pos = avg_2d_pt # 2D avg pos of visible body parts
# compute robot's horizontal distance (head distance, or avg. distance of visible body parts)
o.state_horizontal_dist = np.linalg.norm(r.loc_head_position[:2] - o.state_abs_pos[:2])
# compute orientation based on pair of lower arms or feet, or average of both
lr_vec = None
if 'llowerarm' in bps_apos and 'rlowerarm' in bps_apos:
lr_vec = bps_apos['rlowerarm'] - bps_apos['llowerarm']
if 'lfoot' in bps_apos and 'rfoot' in bps_apos:
if lr_vec is None:
lr_vec = bps_apos['rfoot'] - bps_apos['lfoot']
else:
lr_vec = (lr_vec + (bps_apos['rfoot'] - bps_apos['lfoot'])) / 2
if lr_vec is not None:
o.state_orientation = atan2(lr_vec[1],lr_vec[0]) * 180 / pi + 90
# compute projection of player area on ground (circle)
if o.state_horizontal_dist < 4: # we don't need precision if the robot is farther than 4m
max_dist = np.max(np.linalg.norm(bps_2d_apos_list - avg_2d_pt, axis=1))
else:
max_dist = 0.2
o.state_ground_area = (avg_2d_pt,max_dist)
# update timestamp
o.state_last_update = self.time_local_ms
|
2302_81918214/robocup3D
|
FCPCodebase-main/world/World.py
|
Python
|
agpl-3.0
| 22,806
|
from math_ops.Matrix_4x4 import Matrix_4x4
class Body_Part():
def __init__(self, mass) -> None:
self.mass = float(mass)
self.joints = []
self.transform = Matrix_4x4() # body part to head transformation matrix
|
2302_81918214/robocup3D
|
FCPCodebase-main/world/commons/Body_Part.py
|
Python
|
agpl-3.0
| 237
|
import socket
from math_ops.Math_Ops import Math_Ops as M
import numpy as np
class Draw():
_socket = None
def __init__(self, is_enabled:bool, unum:int, host:str, port:int) -> None:
self.enabled = is_enabled
self._is_team_right = None
self._unum = unum
self._prefix = f'?{unum}_'.encode() # temporary prefix that should never be used in normal circumstances
#Create one socket for all instances
if Draw._socket is None:
Draw._socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM )
Draw._socket.connect((host, port))
Draw.clear_all()
def set_team_side(self, is_right):
''' Called by world parser to switch side '''
'''
Generate an appropriate player ID
RoboViz has a bug/feature: we send "swap buffers for player: 'l_1' and RoboViz
will swap every buffer that contains 'l_1' in the name, including
'l_10' and 'l_11'. To avoid that, we swap the separator to 'l-10', 'l-11'
'''
self._is_team_right = is_right
self._prefix = f"{'r' if is_right else 'l'}{'_' if self._unum < 10 else '-'}{self._unum}_".encode() #e.g. b'l_5', b'l-10'
@staticmethod
def _send(msg, id, flush):
''' Private method to send message if RoboViz is accessible '''
try:
if flush:
Draw._socket.send(msg + id + b'\x00\x00\x00' + id + b'\x00')
else:
Draw._socket.send(msg + id + b'\x00')
except ConnectionRefusedError:
pass
def circle(self, pos2d, radius, thickness, color:bytes, id:str, flush=True):
'''
Draw circle
Examples
----------
Circle in 2D (z=0): circle((-1,2), 3, 2, Draw.Color.red, "my_circle")
'''
if not self.enabled: return
assert type(color)==bytes, "The RGB color must be a bytes object, e.g. red: b'\xFF\x00\x00'"
assert not np.isnan(pos2d).any(), "Argument 'pos2d' contains 'nan' values"
if self._is_team_right:
pos2d = (-pos2d[0],-pos2d[1])
msg = b'\x01\x00' + (
f'{f"{pos2d[0] :.4f}":.6s}'
f'{f"{pos2d[1] :.4f}":.6s}'
f'{f"{radius :.4f}":.6s}'
f'{f"{thickness :.4f}":.6s}').encode() + color
Draw._send(msg, self._prefix + id.encode(), flush)
def line(self, p1, p2, thickness, color:bytes, id:str, flush=True):
'''
Draw line
Examples
----------
Line in 3D: line((0,0,0), (0,0,2), 3, Draw.Color.red, "my_line")
Line in 2D (z=0): line((0,0), (0,1), 3, Draw.Color.red, "my_line")
'''
if not self.enabled: return
assert type(color)==bytes, "The RGB color must be a bytes object, e.g. red: b'\xFF\x00\x00'"
assert not np.isnan(p1).any(), "Argument 'p1' contains 'nan' values"
assert not np.isnan(p2).any(), "Argument 'p2' contains 'nan' values"
z1 = p1[2] if len(p1)==3 else 0
z2 = p2[2] if len(p2)==3 else 0
if self._is_team_right:
p1 = (-p1[0],-p1[1],p1[2]) if len(p1)==3 else (-p1[0],-p1[1])
p2 = (-p2[0],-p2[1],p2[2]) if len(p2)==3 else (-p2[0],-p2[1])
msg = b'\x01\x01' + (
f'{f"{p1[0] :.4f}":.6s}'
f'{f"{p1[1] :.4f}":.6s}'
f'{f"{z1 :.4f}":.6s}'
f'{f"{p2[0] :.4f}":.6s}'
f'{f"{p2[1] :.4f}":.6s}'
f'{f"{z2 :.4f}":.6s}'
f'{f"{thickness :.4f}":.6s}').encode() + color
Draw._send(msg, self._prefix + id.encode(), flush)
def point(self, pos, size, color:bytes, id:str, flush=True):
'''
Draw point
Examples
----------
Point in 3D: point((1,1,1), 3, Draw.Color.red, "my_point")
Point in 2D (z=0): point((1,1), 3, Draw.Color.red, "my_point")
'''
if not self.enabled: return
assert type(color)==bytes, "The RGB color must be a bytes object, e.g. red: b'\xFF\x00\x00'"
assert not np.isnan(pos).any(), "Argument 'pos' contains 'nan' values"
z = pos[2] if len(pos)==3 else 0
if self._is_team_right:
pos = (-pos[0],-pos[1],pos[2]) if len(pos)==3 else (-pos[0],-pos[1])
msg = b'\x01\x02' + (
f'{f"{pos[0] :.4f}":.6s}'
f'{f"{pos[1] :.4f}":.6s}'
f'{f"{z :.4f}":.6s}'
f'{f"{size :.4f}":.6s}').encode() + color
Draw._send(msg, self._prefix + id.encode(), flush)
def sphere(self, pos, radius, color:bytes, id:str, flush=True):
'''
Draw sphere
Examples
----------
Sphere in 3D: sphere((1,1,1), 3, Draw.Color.red, "my_sphere")
Sphere in 2D (z=0): sphere((1,1), 3, Draw.Color.red, "my_sphere")
'''
if not self.enabled: return
assert type(color)==bytes, "The RGB color must be a bytes object, e.g. red: b'\xFF\x00\x00'"
assert not np.isnan(pos).any(), "Argument 'pos' contains 'nan' values"
z = pos[2] if len(pos)==3 else 0
if self._is_team_right:
pos = (-pos[0],-pos[1],pos[2]) if len(pos)==3 else (-pos[0],-pos[1])
msg = b'\x01\x03' + (
f'{f"{pos[0] :.4f}":.6s}'
f'{f"{pos[1] :.4f}":.6s}'
f'{f"{z :.4f}":.6s}'
f'{f"{radius :.4f}":.6s}').encode() + color
Draw._send(msg, self._prefix + id.encode(), flush)
def polygon(self, vertices, color:bytes, alpha:int, id:str, flush=True):
'''
Draw polygon
Examples
----------
Polygon in 3D: polygon(((0,0,0),(1,0,0),(0,1,0)), Draw.Color.red, 255, "my_polygon")
'''
if not self.enabled: return
assert type(color)==bytes, "The RGB color must be a bytes object, e.g. red: b'\xFF\x00\x00'"
assert 0<=alpha<=255, "The alpha channel (degree of opacity) must be in range [0,255]"
if self._is_team_right:
vertices = [(-v[0],-v[1],v[2]) for v in vertices]
msg = b'\x01\x04' + bytes([len(vertices)]) + color + alpha.to_bytes(1,'big')
for v in vertices:
msg += (
f'{f"{v[0] :.4f}":.6s}'
f'{f"{v[1] :.4f}":.6s}'
f'{f"{v[2] :.4f}":.6s}').encode()
Draw._send(msg, self._prefix + id.encode(), flush)
def annotation(self, pos, text, color:bytes, id:str, flush=True):
'''
Draw annotation
Examples
----------
Annotation in 3D: annotation((1,1,1), "SOMEtext!", Draw.Color.red, "my_annotation")
Annotation in 2D (z=0): annotation((1,1), "SOMEtext!", Draw.Color.red, "my_annotation")
'''
if not self.enabled: return
if type(text) != bytes: text = str(text).encode()
assert type(color)==bytes, "The RGB color must be a bytes object, e.g. red: b'\xFF\x00\x00'"
z = pos[2] if len(pos)==3 else 0
if self._is_team_right:
pos = (-pos[0],-pos[1],pos[2]) if len(pos)==3 else (-pos[0],-pos[1])
msg = b'\x02\x00' + (
f'{f"{pos[0] :.4f}":.6s}'
f'{f"{pos[1] :.4f}":.6s}'
f'{f"{z :.4f}":.6s}').encode() + color + text + b'\x00'
Draw._send(msg, self._prefix + id.encode(), flush)
def arrow(self, p1, p2, arrowhead_size, thickness, color:bytes, id:str, flush=True):
'''
Draw arrow
Examples
----------
Arrow in 3D: arrow((0,0,0), (0,0,2), 0.1, 3, Draw.Color.red, "my_arrow")
Arrow in 2D (z=0): arrow((0,0), (0,1), 0.1, 3, Draw.Color.red, "my_arrow")
'''
if not self.enabled: return
assert type(color)==bytes, "The RGB color must be a bytes object, e.g. red: b'\xFF\x00\x00'"
# No need to invert sides, the called shapes will handle that
if len(p1)==2: p1 = M.to_3d(p1)
else: p1 = np.asarray(p1)
if len(p2)==2: p2 = M.to_3d(p2)
else: p2 = np.asarray(p2)
vec = p2-p1
vec_size = np.linalg.norm(vec)
if vec_size == 0: return #return without warning/error
if arrowhead_size > vec_size: arrowhead_size = vec_size
ground_proj_perpendicular = np.array([ vec[1], -vec[0], 0 ])
if np.all(ground_proj_perpendicular == 0): #vertical arrow
ground_proj_perpendicular = np.array([ arrowhead_size/2, 0, 0 ])
else:
ground_proj_perpendicular *= arrowhead_size/2 / np.linalg.norm(ground_proj_perpendicular)
head_start = p2 - vec * (arrowhead_size/vec_size)
head_pt1 = head_start + ground_proj_perpendicular
head_pt2 = head_start - ground_proj_perpendicular
self.line(p1,p2,thickness,color,id,False)
self.line(p2,head_pt1,thickness,color,id,False)
self.line(p2,head_pt2,thickness,color,id,flush)
def flush(self, id):
''' Flush specific drawing by ID '''
if not self.enabled: return
Draw._send(b'\x00\x00', self._prefix + id.encode(), False)
def clear(self, id):
''' Clear specific drawing by ID '''
if not self.enabled: return
Draw._send(b'\x00\x00', self._prefix + id.encode(), True) #swap buffer twice
def clear_player(self):
''' Clear all drawings made by this player '''
if not self.enabled: return
Draw._send(b'\x00\x00', self._prefix, True) #swap buffer twice
@staticmethod
def clear_all():
''' Clear all drawings of all players '''
if Draw._socket is not None:
Draw._send(b'\x00\x00\x00\x00\x00',b'',False) #swap buffer twice using no id
class Color():
'''
Based on X11 colors
The names are restructured to make better suggestions
'''
pink_violet = b'\xC7\x15\x85'
pink_hot = b'\xFF\x14\x93'
pink_violet_pale = b'\xDB\x70\x93'
pink = b'\xFF\x69\xB4'
pink_pale = b'\xFF\xB6\xC1'
red_dark = b'\x8B\x00\x00'
red = b'\xFF\x00\x00'
red_brick = b'\xB2\x22\x22'
red_crimson = b'\xDC\x14\x3C'
red_indian = b'\xCD\x5C\x5C'
red_salmon = b'\xFA\x80\x72'
orange_red = b'\xFF\x45\x00'
orange = b'\xFF\x8C\x00'
orange_ligth = b'\xFF\xA5\x00'
yellow_gold = b'\xFF\xD7\x00'
yellow = b'\xFF\xFF\x00'
yellow_light = b'\xBD\xB7\x6B'
brown_maroon =b'\x80\x00\x00'
brown_dark = b'\x8B\x45\x13'
brown = b'\xA0\x52\x2D'
brown_gold = b'\xB8\x86\x0B'
brown_light = b'\xCD\x85\x3F'
brown_pale = b'\xDE\xB8\x87'
green_dark = b'\x00\x64\x00'
green = b'\x00\x80\x00'
green_lime = b'\x32\xCD\x32'
green_light = b'\x00\xFF\x00'
green_lawn = b'\x7C\xFC\x00'
green_pale = b'\x90\xEE\x90'
cyan_dark = b'\x00\x80\x80'
cyan_medium = b'\x00\xCE\xD1'
cyan = b'\x00\xFF\xFF'
cyan_light = b'\xAF\xEE\xEE'
blue_dark = b'\x00\x00\x8B'
blue = b'\x00\x00\xFF'
blue_royal = b'\x41\x69\xE1'
blue_medium = b'\x1E\x90\xFF'
blue_light = b'\x00\xBF\xFF'
blue_pale = b'\x87\xCE\xEB'
purple_violet = b'\x94\x00\xD3'
purple_magenta = b'\xFF\x00\xFF'
purple_light = b'\xBA\x55\xD3'
purple_pale = b'\xDD\xA0\xDD'
white = b'\xFF\xFF\xFF'
gray_10 = b'\xE6\xE6\xE6'
gray_20 = b'\xCC\xCC\xCC'
gray_30 = b'\xB2\xB2\xB2'
gray_40 = b'\x99\x99\x99'
gray_50 = b'\x80\x80\x80'
gray_60 = b'\x66\x66\x66'
gray_70 = b'\x4C\x4C\x4C'
gray_80 = b'\x33\x33\x33'
gray_90 = b'\x1A\x1A\x1A'
black = b'\x00\x00\x00'
@staticmethod
def get(r,g,b):
''' Get RGB color (0-255) '''
return bytes([int(r),int(g),int(b)])
|
2302_81918214/robocup3D
|
FCPCodebase-main/world/commons/Draw.py
|
Python
|
agpl-3.0
| 11,899
|
import numpy as np
class Joint_Info():
def __init__(self, xml_element) -> None:
self.perceptor = xml_element.attrib['perceptor']
self.effector = xml_element.attrib['effector']
self.axes = np.array([
float(xml_element.attrib['xaxis']),
float(xml_element.attrib['yaxis']),
float(xml_element.attrib['zaxis'])])
self.min = int(xml_element.attrib['min'])
self.max = int(xml_element.attrib['max'])
self.anchor0_part = xml_element[0].attrib['part']
self.anchor0_axes = np.array([
float(xml_element[0].attrib['y']),
float(xml_element[0].attrib['x']),
float(xml_element[0].attrib['z'])]) #x and y axes are switched
self.anchor1_part = xml_element[1].attrib['part']
self.anchor1_axes_neg = np.array([
-float(xml_element[1].attrib['y']),
-float(xml_element[1].attrib['x']),
-float(xml_element[1].attrib['z'])]) #x and y axes are switched
|
2302_81918214/robocup3D
|
FCPCodebase-main/world/commons/Joint_Info.py
|
Python
|
agpl-3.0
| 1,158
|
import numpy as np
#Note: When other robot is seen, all previous body part positions are deleted
# E.g. we see 5 body parts at 0 seconds -> body_parts_cart_rel_pos contains 5 elements
# we see 1 body part at 1 seconds -> body_parts_cart_rel_pos contains 1 element
class Other_Robot():
def __init__(self, unum, is_teammate) -> None:
self.unum = unum # convenient variable to indicate uniform number (same as other robot's index + 1)
self.is_self = False # convenient flag to indicate if this robot is self
self.is_teammate = is_teammate # convenient variable to indicate if this robot is from our team
self.is_visible = False # True if this robot was seen in the last message from the server (it doesn't mean we know its absolute location)
self.body_parts_cart_rel_pos = dict() # cartesian relative position of the robot's visible body parts
self.body_parts_sph_rel_pos = dict() # spherical relative position of the robot's visible body parts
self.vel_filter = 0.3 # EMA filter coefficient applied to self.state_filtered_velocity
self.vel_decay = 0.95 # velocity decay at every vision cycle (neutralized if velocity is updated)
# State variables: these are computed when this robot is visible and when the original robot is able to self-locate
self.state_fallen = False # true if the robot is lying down (updated when head is visible)
self.state_last_update = 0 # World.time_local_ms when the state was last updated
self.state_horizontal_dist = 0 # horizontal head distance if head is visible, otherwise, average horizontal distance of visible body parts (the distance is updated by vision or radio when state_abs_pos gets a new value, but also when the other player is not visible, by assuming its last position)
self.state_abs_pos = None # 3D head position if head is visible, otherwise, 2D average position of visible body parts, or, 2D radio head position
self.state_orientation = 0 # orientation based on pair of lower arms or feet, or average of both (WARNING: may be older than state_last_update)
self.state_ground_area = None # (pt_2d,radius) projection of player area on ground (circle), not precise if farther than 3m (for performance), useful for obstacle avoidance when it falls
self.state_body_parts_abs_pos = dict() # 3D absolute position of each body part
self.state_filtered_velocity = np.zeros(3) # 3D filtered velocity (m/s) (if the head is not visible, the 2D part is updated and v.z decays)
|
2302_81918214/robocup3D
|
FCPCodebase-main/world/commons/Other_Robot.py
|
Python
|
agpl-3.0
| 2,695
|
from cpp.a_star import a_star
from math_ops.Math_Ops import Math_Ops as M
from world.World import World
import math
import numpy as np
class Path_Manager():
MODE_CAUTIOUS = 0
MODE_DRIBBLE = 1 # safety margins are increased
MODE_AGGRESSIVE = 2 # safety margins are reduced for opponents
STATUS_SUCCESS = 0 # the pathfinding algorithm was executed normally
STATUS_TIMEOUT = 1 # timeout before the target was reached (may be impossible)
STATUS_IMPOSSIBLE = 2 # impossible to reach target (all options were tested)
STATUS_DIRECT = 3 # no obstacles between start and target (path contains only 2 points: the start and target)
HOT_START_DIST_WALK = 0.05 # hot start prediction distance (when walking)
HOT_START_DIST_DRIBBLE = 0.10 # hot start prediction distance (when dribbling)
def __init__(self, world : World) -> None:
self.world = world
self._draw_obstacles = False # enabled by function 'draw_options'
self._draw_path = False # enabled by function 'draw_options'
self._use_team_channel = False # enabled by function 'draw_options'
# internal variables to bootstrap the path to start from a prediction (to reduce path instability)
self.last_direction_rad = None
self.last_update = 0
self.last_start_dist = None
def draw_options(self, enable_obstacles, enable_path, use_team_drawing_channel=False):
'''
Enable or disable drawings, and change drawing channel
If self.world.draw.enable is False, these options are ignored
Parameters
----------
enable_obstacles : bool
draw relevant obstacles for path planning
enable_path : bool
draw computed path
use_team_drawing_channel : bool
True to use team drawing channel, otherwise use individual channel
Using individual channels for each player means that drawings with the same name can coexist
With the team channel, drawings with the same name will replace previous drawings, even if drawn by a teammate
'''
self._draw_obstacles = enable_obstacles
self._draw_path = enable_path
self._use_team_channel = use_team_drawing_channel
def get_obstacles(self, include_teammates, include_opponents, include_play_mode_restrictions, max_distance = 4, max_age = 500,
ball_safety_margin = 0, goalpost_safety_margin = 0, mode = MODE_CAUTIOUS, priority_unums=[]):
'''
Parameters
----------
include_teammates : bool
include teammates in the returned list of obstacles
include_opponents : bool
include opponents in the returned list of obstacles
max_distance : float
teammates or opponents are only considered if they are closer than `max_distance` (meters)
max_age : float
teammates or opponents are only considered if they were seen in the last `max_age` (milliseconds)
ball_safety_margin : float
minimum value for the ball's soft repulsion radius
this value is increased when the game is stopped, and when the ball is almost out of bounds
default is zero, the ball is ignored
goalpost_safety_margin : float
hard repulsion radius around the opponents' goalposts
default is zero, uses the minimum margin
mode : int
overall attitude towards safety margins (concerns teammates and opponents)
priority_unums : list
list of teammates to avoid (since their role is more important)
Returns
-------
obstacles : list
list of obstacles, where each obstacle is a tuple of 5 floats (x, y, hard radius, soft radius, repulsive force)
'''
w = self.world
ball_2d = w.ball_abs_pos[:2]
obstacles = []
# 'comparator' is a variable local to the lambda, which captures the current value of (w.time_local_ms - max_age)
check_age = lambda last_update, comparator = w.time_local_ms - max_age : last_update > 0 and last_update >= comparator
#---------------------------------------------- Get recently seen close teammates
if include_teammates:
soft_radius = 1.1 if mode == Path_Manager.MODE_DRIBBLE else 0.6 # soft radius: repulsive force is max at center and fades
def get_hard_radius(t):
if t.unum in priority_unums:
return 1.0 # extra distance for priority roles
else:
return t.state_ground_area[1]+0.2
# Get close teammates (center, hard radius, soft radius, force)
obstacles.extend( (*t.state_ground_area[0],
get_hard_radius(t),
1.5 if t.unum in priority_unums else soft_radius,
1.0) # repulsive force
for t in w.teammates if not t.is_self and check_age(t.state_last_update) and t.state_horizontal_dist < max_distance)
#---------------------------------------------- Get recently seen close opponents
if include_opponents:
# soft radius: repulsive force is max at center and fades
if mode == Path_Manager.MODE_AGGRESSIVE:
soft_radius = 0.6
hard_radius = lambda o : 0.2
elif mode == Path_Manager.MODE_DRIBBLE:
soft_radius = 2.3
hard_radius = lambda o : o.state_ground_area[1]+0.9
else:
soft_radius = 1.0
hard_radius = lambda o : o.state_ground_area[1]+0.2
# Get close opponents (center, hard radius, soft radius, force)
obstacles.extend( (*o.state_ground_area[0],
hard_radius(o),
soft_radius,
1.5 if o.unum == 1 else 1.0) # repulsive force (extra for their GK)
for o in w.opponents if o.state_last_update > 0 and w.time_local_ms - o.state_last_update <= max_age and o.state_horizontal_dist < max_distance)
#---------------------------------------------- Get play mode restrictions
if include_play_mode_restrictions:
if w.play_mode == World.M_THEIR_GOAL_KICK:
obstacles.extend((15,i,2.1,0,0) for i in range(-2,3)) # 5 circular obstacles to cover their goal area
elif w.play_mode == World.M_THEIR_PASS:
obstacles.append((*ball_2d, 1.2, 0, 0))
elif w.play_mode in [World.M_THEIR_KICK_IN,World.M_THEIR_CORNER_KICK,World.M_THEIR_FREE_KICK,World.M_THEIR_DIR_FREE_KICK, World.M_THEIR_OFFSIDE]:
obstacles.append((*ball_2d, 2.5, 0, 0))
#---------------------------------------------- Get ball
if ball_safety_margin > 0:
# increase ball safety margin in certain game scenarios
if (w.play_mode_group != w.MG_OTHER) or abs(ball_2d[1])>9.5 or abs(ball_2d[0])>14.5:
ball_safety_margin += 0.12
obstacles.append((*ball_2d, 0, ball_safety_margin, 8))
#---------------------------------------------- Get goal posts
if goalpost_safety_margin > 0:
obstacles.append((14.75, 1.10,goalpost_safety_margin,0,0))
obstacles.append((14.75,-1.10,goalpost_safety_margin,0,0))
#---------------------------------------------- Draw obstacles
if self._draw_obstacles:
d = w.team_draw if self._use_team_channel else w.draw
if d.enabled:
for o in obstacles:
if o[3] > 0: d.circle(o[:2],o[3],o[4]/2, d.Color.orange, "path_obstacles", False)
if o[2] > 0: d.circle(o[:2],o[2],1, d.Color.red, "path_obstacles", False)
d.flush("path_obstacles")
return obstacles
def _get_hot_start(self, start_distance):
'''
Get hot start position for path (considering the previous path)
(as opposed to a cold start, where the path starts at the player)
'''
if self.last_update > 0 and self.world.time_local_ms - self.last_update == 20 and self.last_start_dist == start_distance:
return self.world.robot.loc_head_position[:2] + M.vector_from_angle(self.last_direction_rad, is_rad = True) * start_distance
else:
return self.world.robot.loc_head_position[:2] # return cold start if start_distance was different or the position was not updated in the last step
def _update_hot_start(self, next_dir_rad, start_distance):
''' Update hot start position for next run '''
self.last_direction_rad = next_dir_rad
self.last_update = self.world.time_local_ms
self.last_start_dist = start_distance
def _extract_target_from_path(self, path, path_len, ret_segments):
ret_seg_ceil = math.ceil(ret_segments)
if path_len >= ret_seg_ceil:
i = ret_seg_ceil * 2 # path index of ceil point (x)
if ret_seg_ceil == ret_segments:
return path[i:i+2]
else:
floor_w = ret_seg_ceil-ret_segments
return path[i-2:i] * floor_w + path[i:i+2] * (1-floor_w)
else:
return path[-2:] # path end
def get_path_to_ball(self, x_ori = None, x_dev = -0.2, y_dev = 0, torso_ori = None, torso_ori_thrsh = 1,
priority_unums:list=[], is_aggressive=True, safety_margin = 0.25, timeout = 3000):
'''
Get next target from path to ball (next absolute position + next absolute orientation)
If the robot is an active player, and close to the ball, it makes sense to be aggressive
If the robot is far, it should follow the role_position instead to predict the intersection with ball
Parameters
----------
x_ori : float
(This variable allows the specification of a target position, relative to the ball, in a custom reference frame.)
absolute orientation of the custom reference frame's x-axis
if None, the orientation is given by the vector (robot->ball)
x_dev : float
(This variable allows the specification of a target position, relative to the ball, in a custom reference frame.)
target position deviation, in the custom reference frame's x-axis
y_dev : float
(This variable allows the specification of a target position, relative to the ball, in a custom reference frame.)
target position deviation, in the custom reference frame's y-axis
torso_ori : float
torso's target absolute orientation (see `torso_ori_thrsh`)
if None, the orientation is given by the vector (robot->target)
torso_ori_thrsh : float
`torso_ori` will only be applied when the distance between robot and final target is < `torso_ori_thrsh` meters
otherwise, the robot will orient itself towards the final target
priority_unums : list
list of teammates to avoid (since their role is more important)
is_aggressive : bool
if True, safety margins are reduced for opponents
safety_margin : float
repulsion radius around ball to avoid colliding with it
timeout : float
maximum execution time (in microseconds)
Returns
-------
next_pos : ndarray
next absolute position from path to ball
next_ori : float
next absolute orientation
distance : float
minimum between (distance to final target) and (distance to ball)
Example
-------
----------------------------------------------------------------------------------------------
x_ori | x_dev | y_dev | torso_ori | OBS
-------------+---------+---------+-------------+----------------------------------------------
None => | - | !0 | - | Not recommended. Will not converge.
(orient. of: | 0 | 0 | None | Frontal ball chase, expected* slow approach
robot->ball) | 0 | 0 | value | Oriented ball chase, expected* slow approach
| >0 | 0 | - | Not recommended. Will not converge.
| <0 | 0 | None | Frontal ball chase until distance == x_dev
| <0 | 0 | value | Oriented ball chase until distance == x_dev
-------------+---------+---------+-------------+----------------------------------------------
value | - | - | None | Frontal point chase
| - | - | value | Oriented point chase
----------------------------------------------------------------------------------------------
* it depends on the caller function (expected slow walking near target)
`torso_ori` will only be applied when the distance between robot and final target is < `torso_ori_thrsh` meters
'''
w = self.world
r = w.robot
dev = np.array([x_dev,y_dev])
dev_len = np.linalg.norm(dev)
dev_mult = 1
# use ball prediction if we are further than 0.5 m and in PlayOn
if np.linalg.norm(w.ball_abs_pos[:2] - r.loc_head_position[:2]) > 0.5 and w.play_mode_group == w.MG_OTHER:
ball_2d = w.get_intersection_point_with_ball(0.4)[0] # intersection point, while moving at 0.4 m/s
else:
ball_2d = w.ball_abs_pos[:2]
# custom reference frame orientation
vec_me_ball = ball_2d - r.loc_head_position[:2]
if x_ori is None:
x_ori = M.vector_angle(vec_me_ball)
distance_boost = 0 # boost for returned distance to target
if torso_ori is not None and dev_len > 0:
approach_ori_diff = abs(M.normalize_deg( r.imu_torso_orientation - torso_ori ))
if approach_ori_diff > 15: # increase walking speed near target if robot is far from approach orientation
distance_boost = 0.15
if approach_ori_diff > 30: # increase target distance to ball if robot is far from approach orientation
dev_mult = 1.3
if approach_ori_diff > 45: # increase safety margin around ball if robot is far from approach orientation
safety_margin = max(0.32,safety_margin)
#------------------------------------------- get target
front_unit_vec = M.vector_from_angle(x_ori)
left_unit_vec = np.array([-front_unit_vec[1], front_unit_vec[0]])
rel_target = front_unit_vec * dev[0] + left_unit_vec * dev[1]
target = ball_2d + rel_target * dev_mult
target_vec = target - r.loc_head_position[:2]
target_dist = np.linalg.norm(target_vec)
if self._draw_path:
d = self.world.team_draw if self._use_team_channel else self.world.draw
d.point(target, 4, d.Color.red, "path_target") # will not draw if drawing object is internally disabled
#------------------------------------------- get obstacles
# Ignore ball if we are on the same side of the target (with small margin)
if dev_len>0 and np.dot(vec_me_ball, rel_target) < -0.10:
safety_margin = 0
obstacles = self.get_obstacles(include_teammates = True, include_opponents = True, include_play_mode_restrictions = True,
ball_safety_margin = safety_margin,
mode = Path_Manager.MODE_AGGRESSIVE if is_aggressive else Path_Manager.MODE_CAUTIOUS,
priority_unums = priority_unums)
# Add obstacle on the side opposite to the target
if dev_len>0 and safety_margin > 0:
center = ball_2d - M.normalize_vec( rel_target ) * safety_margin
obstacles.append((*center, 0, safety_margin*0.9, 5))
if self._draw_obstacles:
d = w.team_draw if self._use_team_channel else w.draw
if d.enabled:
d.circle(center,safety_margin*0.8,2.5, d.Color.orange, "path_obstacles_1")
#------------------------------------------- get path
# see explanation for the context at the hot start update section below
start_pos = self._get_hot_start(Path_Manager.HOT_START_DIST_WALK) if target_dist > 0.4 else self.world.robot.loc_head_position[:2]
path, path_len, path_status, path_cost = self.get_path(start_pos, True, obstacles, target, timeout)
path_end = path[-2:] # last position allowed by A*
#------------------------------------------- get relevant distances
if w.ball_last_seen > w.time_local_ms - w.VISUALSTEP_MS: # ball is in FOV
raw_ball_dist = np.linalg.norm(w.ball_rel_torso_cart_pos[:2]) # - distance between torso center and ball center
else: # otherwise use absolute coordinates to compute distance
raw_ball_dist = np.linalg.norm(vec_me_ball) # - distance between head center and ball center
avoid_touching_ball = (w.play_mode_group != w.MG_OTHER)
distance_to_final_target = np.linalg.norm(path_end - r.loc_head_position[:2])
distance_to_ball = max(0.07 if avoid_touching_ball else 0.14, raw_ball_dist - 0.13)
caution_dist = min(distance_to_ball,distance_to_final_target)
#------------------------------------------- get next target position
next_pos = self._extract_target_from_path( path, path_len, ret_segments=1 if caution_dist < 1 else 2 )
#------------------------------------------ get next target orientation
# use given orientation if it exists, else target's orientation if far enough, else current orientation
if torso_ori is not None:
if caution_dist > torso_ori_thrsh:
next_ori = M.vector_angle(target_vec)
else:
mid_ori = M.normalize_deg( M.vector_angle(vec_me_ball) - M.vector_angle(-dev) - x_ori + torso_ori )
mid_ori_diff = abs(M.normalize_deg(mid_ori - r.imu_torso_orientation))
final_ori_diff = abs(M.normalize_deg(torso_ori - r.imu_torso_orientation))
next_ori = mid_ori if mid_ori_diff + 10 < final_ori_diff else torso_ori
elif target_dist > 0.1:
next_ori = M.vector_angle(target_vec)
else:
next_ori = r.imu_torso_orientation
#------------------------------------------ update hot start for next run
''' Defining the hot start distance:
- if path_len is zero, there is no hot start, because we are already there (dist=0)
- if the target is close, the hot start is not applied (see above)
- if the next pos is very close (due to hard obstacle), the hot start is the next pos (dist<Path_Manager.HOT_START_DIST_WALK)
- otherwise, the hot start distance is constant (dist=Path_Manager.HOT_START_DIST_WALK)
'''
if path_len != 0:
next_pos_vec = next_pos - self.world.robot.loc_head_position[:2]
next_pos_dist = np.linalg.norm(next_pos_vec)
self._update_hot_start(M.vector_angle(next_pos_vec, is_rad=True), min(Path_Manager.HOT_START_DIST_WALK,next_pos_dist))
return next_pos, next_ori, min(distance_to_ball, distance_to_final_target + distance_boost)
def get_path_to_target(self, target, ret_segments = 1.0, torso_ori = None, priority_unums:list=[], is_aggressive=True, timeout = 3000):
'''
Get next position from path to target (next absolute position + next absolute orientation)
Parameters
----------
ret_segments : float
returned target's maximum distance (measured in path segments from hot start position)
actual distance: min(ret_segments,path_length)
each path segment has 0.10 m or 0.1*sqrt(2) m (if diagonal)
if `ret_segments` is 0, the current position is returned
torso_ori : float
torso's target absolute orientation
if None, the orientation is given by the vector (robot->target)
priority_unums : list
list of teammates to avoid (since their role is more important)
is_aggressive : bool
if True, safety margins are reduced for opponents
timeout : float
maximum execution time (in microseconds)
'''
w = self.world
#------------------------------------------- get target
target_vec = target - w.robot.loc_head_position[:2]
target_dist = np.linalg.norm(target_vec)
#------------------------------------------- get obstacles
obstacles = self.get_obstacles(include_teammates = True, include_opponents = True, include_play_mode_restrictions = True,
mode = Path_Manager.MODE_AGGRESSIVE if is_aggressive else Path_Manager.MODE_CAUTIOUS, priority_unums = priority_unums)
#------------------------------------------- get path
# see explanation for the context at the hot start update section below
start_pos = self._get_hot_start(Path_Manager.HOT_START_DIST_WALK) if target_dist > 0.4 else self.world.robot.loc_head_position[:2]
path, path_len, path_status, path_cost = self.get_path(start_pos, True, obstacles, target, timeout)
path_end = path[-2:] # last position allowed by A*
#------------------------------------------- get next target position
next_pos = self._extract_target_from_path(path, path_len, ret_segments)
#------------------------------------------ get next target orientation
# use given orientation if it exists, else target's orientation if far enough, else current orientation
if torso_ori is not None:
next_ori = torso_ori
elif target_dist > 0.1:
next_ori = M.vector_angle(target_vec)
else:
next_ori = w.robot.imu_torso_orientation
#------------------------------------------ update hot start for next run
''' Defining the hot start distance:
- if path_len is zero, there is no hot start, because we are already there (dist=0)
- if the target is close, the hot start is not applied (see above)
- if the next pos is very close (due to hard obstacle), the hot start is the next pos (dist<Path_Manager.HOT_START_DIST_WALK)
- otherwise, the hot start distance is constant (dist=Path_Manager.HOT_START_DIST_WALK)
'''
if path_len != 0:
next_pos_vec = next_pos - self.world.robot.loc_head_position[:2]
next_pos_dist = np.linalg.norm(next_pos_vec)
self._update_hot_start(M.vector_angle(next_pos_vec, is_rad=True), min(Path_Manager.HOT_START_DIST_WALK,next_pos_dist))
distance_to_final_target = np.linalg.norm(path_end - w.robot.loc_head_position[:2])
return next_pos, next_ori, distance_to_final_target
def get_dribble_path(self, ret_segments = None, optional_2d_target = None, goalpost_safety_margin=0.4, timeout = 3000):
'''
Get next position from path to target (next relative orientation)
Path is optimized for dribble
Parameters
----------
ret_segments : float
returned target's maximum distance (measured in path segments from hot start position)
actual distance: min(ret_segments,path_length)
each path segment has 0.10 m or 0.1*sqrt(2) m (if diagonal)
if `ret_segments` is 0, the current position is returned
if `ret_segments` is None, it is dynamically set according to the robot's speed
optional_2d_target : float
2D target
if None, the target is the opponent's goal (the specific goal point is decided by the A* algorithm)
goalpost_safety_margin : float
hard repulsion radius around the opponents' goalposts
if zero, the minimum margin is used
timeout : float
maximum execution time (in microseconds)
'''
r = self.world.robot
ball_2d = self.world.ball_abs_pos[:2]
#------------------------------------------- get obstacles
obstacles = self.get_obstacles(include_teammates = True, include_opponents = True, include_play_mode_restrictions = False,
max_distance=5, max_age=1000, goalpost_safety_margin=goalpost_safety_margin, mode = Path_Manager.MODE_DRIBBLE)
#------------------------------------------- get path
start_pos = self._get_hot_start(Path_Manager.HOT_START_DIST_DRIBBLE)
path, path_len, path_status, path_cost = self.get_path(start_pos, False, obstacles, optional_2d_target, timeout)
#------------------------------------------- get next target position & orientation
if ret_segments is None:
ret_segments = 2.0
next_pos = self._extract_target_from_path(path, path_len, ret_segments)
next_rel_ori = M.normalize_deg(M.vector_angle(next_pos - ball_2d) - r.imu_torso_orientation)
#------------------------------------------ update hot start for next run
if path_len != 0:
self._update_hot_start(M.deg_to_rad(r.imu_torso_orientation), Path_Manager.HOT_START_DIST_DRIBBLE)
#------------------------------------------ draw
if self._draw_path and path_status != Path_Manager.STATUS_DIRECT:
d = self.world.team_draw if self._use_team_channel else self.world.draw
d.point(next_pos, 2, d.Color.pink, "path_next_pos",False) # will not draw if drawing object is internally disabled
d.line(ball_2d, next_pos, 2, d.Color.pink, "path_next_pos") # will not draw if drawing object is internally disabled
return next_pos, next_rel_ori
def get_push_path(self, ret_segments = 1.5, optional_2d_target = None, avoid_opponents=False, timeout = 3000):
'''
Get next position from path ball to target (next absolute position)
Path is optimized for critical push (no obstacles, also for preparation stability)
Parameters
----------
ret_segments : float
returned target's maximum distance (measured in path segments from hot start position)
actual distance: min(ret_segments,path_length)
each path segment has 0.10 m or 0.1*sqrt(2) m (if diagonal)
if `ret_segments` is 0, the current position is returned
optional_2d_target : float
2D target
if None, the target is the opponent's goal (the specific goal point is decided by the A* algorithm)
timeout : float
maximum execution time (in microseconds)
'''
ball_2d = self.world.ball_abs_pos[:2]
#------------------------------------------- get obstacles
obstacles = self.get_obstacles(include_teammates = False, include_opponents = avoid_opponents, include_play_mode_restrictions = False)
#------------------------------------------- get path
path, path_len, path_status, path_cost = self.get_path(ball_2d, False, obstacles, optional_2d_target, timeout)
#------------------------------------------- get next target position & orientation
next_pos = self._extract_target_from_path(path, path_len, ret_segments)
return next_pos
def get_path(self, start, allow_out_of_bounds, obstacles=[], optional_2d_target = None, timeout = 3000):
'''
Parameters
----------
allow_out_of_bounds : bool
allow path to go out of bounds, should be False when dribbling
obstacles : list
list of obstacles, where each obstacle is a tuple of 5 floats (x, y, hard radius, soft radius, repulsive force)
optional_2d_target : float
2D target
if None, the target is the opponent's goal (the specific goal point is decided by the A* algorithm)
timeout : float
maximum execution time (in microseconds)
'''
go_to_goal = int(optional_2d_target is None)
if optional_2d_target is None:
optional_2d_target = (0,0)
# flatten obstacles
obstacles = sum(obstacles, tuple())
assert len(obstacles) % 5 == 0, "Each obstacle should be characterized by exactly 5 float values"
# Path parameters: start, allow_out_of_bounds, go_to_goal, optional_target, timeout (us), obstacles
params = np.array([*start, int(allow_out_of_bounds), go_to_goal, *optional_2d_target, timeout, *obstacles], np.float32)
path_ret = a_star.compute(params)
path = path_ret[:-2]
path_status = path_ret[-2]
#---------------------------------------------- Draw path segments
if self._draw_path:
d = self.world.team_draw if self._use_team_channel else self.world.draw
if d.enabled:
c = {0: d.Color.green_lawn, 1: d.Color.yellow, 2: d.Color.red, 3: d.Color.cyan}[path_status]
for j in range(0, len(path)-2, 2):
d.line((path[j],path[j+1]),(path[j+2],path[j+3]), 1, c, "path_segments", False)
d.flush("path_segments")
return path, len(path)//2-1, path_status, path_ret[-1] # path, path_len (number of segments), path_status, path_cost (A* cost)
|
2302_81918214/robocup3D
|
FCPCodebase-main/world/commons/Path_Manager.py
|
Python
|
agpl-3.0
| 29,694
|
package com.example.roller
import androidx.test.platform.app.InstrumentationRegistry
import androidx.test.ext.junit.runners.AndroidJUnit4
import org.junit.Test
import org.junit.runner.RunWith
import org.junit.Assert.*
/**
* Instrumented test, which will execute on an Android device.
*
* See [testing documentation](http://d.android.com/tools/testing).
*/
@RunWith(AndroidJUnit4::class)
class ExampleInstrumentedTest {
@Test
fun useAppContext() {
// Context of the app under test.
val appContext = InstrumentationRegistry.getInstrumentation().targetContext
assertEquals("com.example.roller", appContext.packageName)
}
}
|
2301_82325581/exp4
|
roller2/app/src/androidTest/java/com/example/roller/ExampleInstrumentedTest.kt
|
Kotlin
|
unknown
| 663
|
package com.example.roller
import android.os.Bundle
import androidx.activity.ComponentActivity
import androidx.activity.compose.setContent
import androidx.compose.foundation.Image
import androidx.compose.foundation.background
import androidx.compose.foundation.layout.Arrangement
import androidx.compose.foundation.layout.Box
import androidx.compose.foundation.layout.Column
import androidx.compose.foundation.layout.Row
import androidx.compose.foundation.layout.Spacer
import androidx.compose.foundation.layout.fillMaxSize
import androidx.compose.foundation.layout.fillMaxWidth
import androidx.compose.foundation.layout.height
import androidx.compose.foundation.layout.padding
import androidx.compose.foundation.layout.size
import androidx.compose.foundation.layout.width
import androidx.compose.foundation.lazy.LazyRow
import androidx.compose.foundation.lazy.items
import androidx.compose.foundation.shape.RoundedCornerShape
import androidx.compose.material3.Button
import androidx.compose.material3.Card
import androidx.compose.material3.CardDefaults
import androidx.compose.material3.MaterialTheme
import androidx.compose.material3.Surface
import androidx.compose.material3.Text
import androidx.compose.runtime.Composable
import androidx.compose.runtime.getValue
import androidx.compose.runtime.mutableStateOf
import androidx.compose.runtime.remember
import androidx.compose.runtime.setValue
import androidx.compose.ui.Alignment
import androidx.compose.ui.Modifier
import androidx.compose.ui.draw.clip
import androidx.compose.ui.graphics.Color
import androidx.compose.ui.layout.ContentScale
import androidx.compose.ui.res.painterResource
import androidx.compose.ui.text.font.FontWeight
import androidx.compose.ui.tooling.preview.Preview
import androidx.compose.ui.unit.dp
import androidx.compose.ui.unit.sp
import com.example.roller.ui.theme.RollerTheme
// 图片数据类
data class ImageData(
val resId: Int,
val title: String,
val author: String,
val description: String
)
class MainActivity : ComponentActivity() {
override fun onCreate(savedInstanceState: Bundle?) {
super.onCreate(savedInstanceState)
setContent {
RollerTheme {
// A surface container using the 'background' color from the theme
Surface(
modifier = Modifier.fillMaxSize(),
color = MaterialTheme.colorScheme.background
) {
ImageGalleryScreen()
}
}
}
}
}
@Composable
fun ImageGalleryScreen() {
// 图片数据 - 使用系统图标作为备用，确保应用可以运行
val images = listOf(
ImageData(
R.drawable.image1, // 使用您项目中的图片资源
"山脉景色",
"摄影师大卫",
"壮丽的雪山景色，蓝天白云映衬"
),
ImageData(
R.drawable.image2, // 系统图标作为备用
"摄影作品",
"摄影师安娜",
"专业的摄影作品展示"
),
ImageData(
R.drawable.image3, // 系统图标作为备用
"艺术画廊",
"艺术家李明",
"现代艺术画廊作品展示"
),
ImageData(
R.drawable.image4, // 系统图标作为备用
"设计作品",
"设计师王芳",
"创意设计作品集合"
)
)
var currentImageIndex by remember { mutableStateOf(0) }
val currentImage = images[currentImageIndex]
Column(
modifier = Modifier
.fillMaxSize()
.padding(16.dp),
horizontalAlignment = Alignment.CenterHorizontally
) {
// 应用标题
Text(
text = "Roller 图片画廊",
fontSize = 24.sp,
fontWeight = FontWeight.Bold,
color = MaterialTheme.colorScheme.primary,
modifier = Modifier.padding(bottom = 24.dp)
)
// 主图片显示卡片
MainImageCard(
imageData = currentImage,
modifier = Modifier
.fillMaxWidth()
.height(300.dp)
)
Spacer(modifier = Modifier.height(24.dp))
// 导航控制按钮
NavigationButtons(
onPreviousClick = {
currentImageIndex = if (currentImageIndex - 1 < 0) {
images.size - 1
} else {
currentImageIndex - 1
}
},
onNextClick = {
currentImageIndex = (currentImageIndex + 1) % images.size
},
modifier = Modifier.fillMaxWidth()
)
Spacer(modifier = Modifier.height(20.dp))
// 缩略图列表
ThumbnailList(
images = images,
selectedIndex = currentImageIndex,
onThumbnailClick = { index ->
currentImageIndex = index
},
modifier = Modifier
.fillMaxWidth()
.height(80.dp)
)
// 图片位置指示器
ImageIndicator(
currentPosition = currentImageIndex + 1,
totalCount = images.size,
modifier = Modifier.padding(top = 16.dp)
)
}
}
@Composable
fun MainImageCard(
imageData: ImageData,
modifier: Modifier = Modifier
) {
Card(
modifier = modifier,
elevation = CardDefaults.cardElevation(defaultElevation = 8.dp),
shape = RoundedCornerShape(16.dp)
) {
Box(modifier = Modifier.fillMaxSize()) {
Image(
painter = painterResource(id = imageData.resId),
contentDescription = imageData.title,
modifier = Modifier.fillMaxSize(),
contentScale = ContentScale.Crop
)
// 图片信息覆盖层
Column(
modifier = Modifier
.align(Alignment.BottomStart)
.fillMaxWidth()
.background(Color.Black.copy(alpha = 0.7f))
.padding(16.dp)
) {
Text(
text = imageData.title,
fontSize = 18.sp,
fontWeight = FontWeight.Bold,
color = Color.White
)
Text(
text = "作者: ${imageData.author}",
fontSize = 14.sp,
color = Color.White.copy(alpha = 0.9f),
modifier = Modifier.padding(top = 4.dp)
)
}
}
}
}
@Composable
fun NavigationButtons(
onPreviousClick: () -> Unit,
onNextClick: () -> Unit,
modifier: Modifier = Modifier
) {
Row(
modifier = modifier,
horizontalArrangement = Arrangement.SpaceEvenly
) {
Button(
onClick = onPreviousClick,
modifier = Modifier.weight(1f)
) {
Text("上一张")
}
Spacer(modifier = Modifier.width(16.dp))
Button(
onClick = onNextClick,
modifier = Modifier.weight(1f)
) {
Text("下一张")
}
}
}
@Composable
fun ThumbnailList(
images: List<ImageData>,
selectedIndex: Int,
onThumbnailClick: (Int) -> Unit,
modifier: Modifier = Modifier
) {
Column(modifier = modifier) {
Text(
text = "图片预览",
fontSize = 16.sp,
fontWeight = FontWeight.Medium,
modifier = Modifier.padding(bottom = 8.dp)
)
LazyRow(
horizontalArrangement = Arrangement.spacedBy(8.dp)
) {
items(images) { image ->
val index = images.indexOf(image)
ThumbnailItem(
imageData = image,
isSelected = index == selectedIndex,
onClick = { onThumbnailClick(index) }
)
}
}
}
}
@Composable
fun ThumbnailItem(
imageData: ImageData,
isSelected: Boolean,
onClick: () -> Unit,
modifier: Modifier = Modifier
) {
Card(
onClick = onClick,
modifier = modifier
.size(60.dp)
.clip(RoundedCornerShape(8.dp)),
elevation = CardDefaults.cardElevation(
defaultElevation = if (isSelected) 6.dp else 2.dp
),
shape = RoundedCornerShape(8.dp),
border = if (isSelected) {
CardDefaults.outlinedCardBorder()
} else {
null
}
) {
Image(
painter = painterResource(id = imageData.resId),
contentDescription = "缩略图: ${imageData.title}",
modifier = Modifier
.fillMaxSize()
.clip(RoundedCornerShape(8.dp)),
contentScale = ContentScale.Crop
)
}
}
@Composable
fun ImageIndicator(
currentPosition: Int,
totalCount: Int,
modifier: Modifier = Modifier
) {
Text(
text = "$currentPosition / $totalCount",
fontSize = 14.sp,
color = MaterialTheme.colorScheme.onSurface.copy(alpha = 0.6f),
modifier = modifier
)
}
@Preview(showBackground = true)
@Composable
fun DefaultPreview() {
RollerTheme {
ImageGalleryScreen()
}
}
|
2301_82325581/exp4
|
roller2/app/src/main/java/com/example/roller/MainActivity.kt
|
Kotlin
|
unknown
| 9,404
|