forked from Telos4/RoboRally
541 lines
18 KiB
Python
541 lines
18 KiB
Python
# startup:
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# roscore -> start ros
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# rosparam set cv_camera/device_id 0 -> set appropriate camera device
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# rosrun cv_camera cv_camera_node -> start the camera
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# roslaunch aruco_detect aruco_detect.launch camera:=cv_camera image:=image_raw dictionary:=16 transport:= fiducial_len:=0.1 # aruco marker detection
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# python fiducial_to_2d_pos_angle.py -> compute position and angle of markers in 2d plane
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import sys
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import rospy
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import pygame
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import numpy as np
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import socket
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import scipy.integrate
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import copy
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import threading
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from copy import deepcopy
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import matplotlib.pyplot as plt
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import matplotlib.animation as anim
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import matplotlib.patches as patch
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from shapely.geometry import Polygon
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import time
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from casadi_opt import OpenLoopSolver
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from marker_pos_angle.msg import id_pos_angle
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from collections import OrderedDict
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from argparse import ArgumentParser
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MSGLEN = 32
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def myreceive(sock):
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chunks = []
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bytes_recd = 0
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while bytes_recd < MSGLEN:
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chunk = sock.recv(min(MSGLEN - bytes_recd, 2048))
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if chunk == b'':
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raise RuntimeError("socket connection broken")
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chunks.append(chunk)
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bytes_recd = bytes_recd + len(chunk)
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if chunk[-1] == '\n':
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break
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return b''.join(chunks)
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class Robot:
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def __init__(self, id, ip):
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self.pos = None
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self.orient = None
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self.grid_pos = (0,0,0)
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self.id = id
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self.pos = None
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self.euler = None
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self.ip = ip
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def f_ode(t, x, u):
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# dynamical model of the two-wheeled robot
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# TODO: find exact values for these parameters
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r = 0.03
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R = 0.05
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d = 0.02
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theta = x[2]
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omega_r = u[0]
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omega_l = u[1]
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dx = np.zeros(3)
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dx[0] = (r/2.0 * np.cos(theta) - r*d/(2.0*R) * np.sin(theta)) * omega_r \
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+ (r/2.0 * np.cos(theta) + r*d/(2.0 * R) * np.sin(theta)) * omega_l
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dx[1] = (r/2.0 * np.sin(theta) + r*d/(2.0*R) * np.cos(theta)) * omega_r \
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+ (r/2 * np.sin(theta) - r*d/(2.0*R) * np.cos(theta)) * omega_l
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dx[2] = -r/(2.0*R) * (omega_r - omega_l)
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return dx
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class RemoteController:
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def __init__(self, id, ip):
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self.anim_stopped = False
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#self.robots = [Robot(14, '192.168.1.103')]
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#self.robots = [Robot(15, '192.168.1.102')]
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self.robots = [Robot(id, ip)]
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self.robot_ids = {}
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for r in self.robots:
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self.robot_ids[r.id] = r
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self.valid_cmds = ['forward', 'backward', 'turn left', 'turn right']
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# connect to robot
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self.rc_socket = socket.socket()
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#self.rc_socket = None
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try:
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for r in self.robots:
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self.rc_socket.connect((r.ip, 1234)) # connect to robot
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except socket.error:
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print("could not connect to socket")
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sys.exit(1)
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# socket for movement commands
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self.comm_socket = socket.socket()
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self.comm_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
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#self.comm_socket.bind((socket.gethostname(), 1337))
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self.comm_socket.bind(('', 1337))
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self.comm_socket.listen(5)
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self.t = time.time()
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# variables for simulated state
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self.x0 = None
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self.ts = np.array([])
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self.xs = []
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# variables for measurements
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self.tms_0 = None
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self.xm_0 = None
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self.tms = None
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self.xms = None
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# variable for mpc open loop
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self.ol_x = None
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self.ol_y = None
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self.mutex = threading.Lock()
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# ROS subscriber for detected markers
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marker_sub = rospy.Subscriber("/marker_id_pos_angle", id_pos_angle, self.measurement_callback)
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# pid parameters
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self.controlling = False
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# currently active control
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self.u1 = 0.0
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self.u2 = 0.0
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# animation
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self.fig = plt.figure()
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self.ax = self.fig.add_subplot(2,2,1)
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self.ax2 = self.fig.add_subplot(2, 2, 2)
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self.ax3 = self.fig.add_subplot(2, 2, 4)
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self.xdata, self.ydata = [], []
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self.line, = self.ax.plot([],[], color='grey', linestyle=':')
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self.line_sim, = self.ax.plot([], [])
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self.line_ol, = self.ax.plot([],[], color='green', linestyle='--')
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self.dirm, = self.ax.plot([], [])
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self.dirs, = self.ax.plot([], [])
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self.line_x, = self.ax2.plot([],[])
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self.line_y, = self.ax3.plot([], [])
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self.track_line_inner, = self.ax.plot([], [])
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self.track_line_outer, = self.ax.plot([], [])
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self.ax.set_xlabel('x-position')
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self.ax.set_ylabel('y-position')
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self.ax.grid()
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self.ax2.set_xlabel('Zeit t')
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self.ax2.set_ylabel('x-position')
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self.ax2.grid()
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self.ax3.set_xlabel('Zeit t')
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self.ax3.set_ylabel('y-position')
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self.ax3.grid()
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self.mstep = 2
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self.ols = OpenLoopSolver(N=20, T=1.0)
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self.ols.setup()
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self.dt = self.ols.T / self.ols.N
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self.target = (0.0, 0.0, 0.0)
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# integrator
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self.r = scipy.integrate.ode(f_ode)
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self.omega_max = 5.0
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self.control_scaling = 0.2
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#self.omega_max = 13.32
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def ani(self):
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print("starting animation")
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self.ani = anim.FuncAnimation(self.fig, init_func=self.ani_init, func=self.ani_update, interval=10, blit=True)
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plt.ion()
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plt.show(block=True)
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def ani_init(self):
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self.ax.set_xlim(-2, 2)
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self.ax.set_ylim(-2, 2)
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self.ax.set_aspect('equal', adjustable='box')
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self.ax2.set_ylim(-2, 2)
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self.ax2.set_xlim(0, 10)
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self.ax3.set_ylim(-2, 2)
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self.ax3.set_xlim(0, 10)
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self.track_line_inner.set_data(self.track.inner_poly.exterior.xy)
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self.track_line_outer.set_data(self.track.outer_poly.exterior.xy)
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return self.line, self.line_sim, self.dirm, self.dirs, self.line_ol,\
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self.track_line_inner, self.track_line_outer, self.line_x,self.line_y,
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def ani_update(self, frame):
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if self.anim_stopped:
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self.ani.event_source.stop()
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sys.exit(0)
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#print("plotting")
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self.mutex.acquire()
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try:
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# copy data for plot from global arrays
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if self.tms is not None:
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tm_local = deepcopy(self.tms)
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xm_local = deepcopy(self.xms)
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if len(tm_local) > 0:
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# plot path of the robot
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self.line.set_data(xm_local[:,0], xm_local[:,1])
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# compute and plot direction the robot is facing
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a = xm_local[-1, 0]
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b = xm_local[-1, 1]
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a2 = a + np.cos(xm_local[-1, 2]) * 0.2
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b2 = b + np.sin(xm_local[-1, 2]) * 0.2
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self.dirm.set_data(np.array([a, a2]), np.array([b, b2]))
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n_plot = 300
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if len(tm_local) > n_plot:
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# plot x and y coordinate
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self.line_x.set_data(tm_local[-n_plot:] - (tm_local[-1] - 10), xm_local[-n_plot:,0])
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self.line_y.set_data(tm_local[-n_plot:] - (tm_local[-1] - 10), xm_local[-n_plot:, 1])
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ts_local = deepcopy(self.ts)
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xs_local = deepcopy(self.xs)
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if len(ts_local) > 0:
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# plot simulated path of the robot
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self.line_sim.set_data(xs_local[:,0], xs_local[:,1])
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# compute and plot direction the robot is facing
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a = xs_local[-1, 0]
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b = xs_local[-1, 1]
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a2 = a + np.cos(xs_local[-1, 2]) * 0.2
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b2 = b + np.sin(xs_local[-1, 2]) * 0.2
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self.dirs.set_data(np.array([a, a2]), np.array([b, b2]))
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ol_x_local = deepcopy(self.ol_x)
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ol_y_local = deepcopy(self.ol_y)
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if ol_x_local is not None:
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self.line_ol.set_data(ol_x_local, ol_y_local)
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else:
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self.line_ol.set_data([],[])
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finally:
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self.mutex.release()
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return self.line, self.line_sim, self.dirm, self.dirs, self.line_ol, self.track_line_inner, self.track_line_outer,\
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self.line_x, self.line_y,
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def measurement_callback(self, data):
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#print(data)
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# detect robots
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if data.id in self.robot_ids:
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r = self.robot_ids[data.id]
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r.pos = (data.x, data.y) # only x and y component are important for us
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r.euler = data.angle
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# save measured position and angle for plotting
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measurement = np.array([r.pos[0], r.pos[1], r.euler])
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if self.tms_0 is None:
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self.tms_0 = time.time()
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self.xm_0 = measurement
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self.mutex.acquire()
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try:
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self.tms = np.array([0.0])
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self.xms = measurement
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finally:
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self.mutex.release()
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else:
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self.mutex.acquire()
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try:
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self.tms = np.vstack((self.tms, time.time() - self.tms_0))
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self.xms = np.vstack((self.xms, measurement))
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finally:
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self.mutex.release()
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def controller(self):
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print("starting control")
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running = True
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while running:
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(clientsocket, address) = self.comm_socket.accept()
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connected = True
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while connected:
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try:
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data = myreceive(clientsocket)
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print(data)
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try:
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robot_id, cmd = data.split(',')
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robot_id = int(robot_id)
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cmd = cmd.strip()
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if robot_id in self.robot_ids and cmd in self.valid_cmds:
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self.mpc_control(robot_id, cmd)
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elif cmd == 'quit':
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clientsocket.close()
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self.comm_socket.close()
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connected = False
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running = False
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else:
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print("invalid command or robot id!")
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except ValueError:
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print("could not process command!")
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except RuntimeError:
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print("disconnected")
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connected = False
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clientsocket.close()
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def mpc_control(self, robot_id, cmd):
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robot = self.robot_ids[robot_id] # get robot to be controlled
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grid_pos = robot.grid_pos # grid position of the robot
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# compute new grid position and orientation
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if cmd == 'forward':
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new_x = grid_pos[0] + 1 * np.cos(grid_pos[2])
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new_y = grid_pos[1] + 1 * np.sin(grid_pos[2])
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new_angle = grid_pos[2]
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elif cmd == 'backward':
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new_x = grid_pos[0] - 1 * np.cos(grid_pos[2])
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new_y = grid_pos[1] - 1 * np.sin(grid_pos[2])
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new_angle = grid_pos[2]
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elif cmd == 'turn left':
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new_x = grid_pos[0]
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new_y = grid_pos[1]
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new_angle = np.unwrap([0, grid_pos[2] + np.pi / 2])[1]
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elif cmd == 'turn right':
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new_x = grid_pos[0]
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new_y = grid_pos[1]
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new_angle = np.unwrap([0, grid_pos[2] - np.pi / 2])[1]
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else:
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print("unknown command!")
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sys.exit(1)
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grid_pos = (new_x, new_y, new_angle)
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print("new grid pos for robot {}: {}".format(robot_id, grid_pos))
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self.target = np.array((0.25 * grid_pos[0], 0.25 * grid_pos[1], grid_pos[2]))
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self.pid = False
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self.mpc = True
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near_target = 0
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while near_target < 5:
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# open loop controller
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events = pygame.event.get()
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for event in events:
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if event.type == pygame.KEYDOWN:
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if event.key == pygame.K_UP:
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self.controlling = True
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self.t = time.time()
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elif event.key == pygame.K_DOWN:
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self.controlling = False
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if self.rc_socket:
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self.rc_socket.send('(0.0,0.0)\n')
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elif event.key == pygame.K_0:
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self.target = np.array([0,0,0])
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elif event.key == pygame.K_PLUS:
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self.control_scaling += 0.1
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self.control_scaling = min(self.control_scaling, 1.0)
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print("control scaling = ", self.control_scaling)
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elif event.key == pygame.K_MINUS:
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self.control_scaling -= 0.1
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self.control_scaling = max(self.control_scaling, 0.1)
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print("control scaling = ", self.control_scaling)
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elif event.key == pygame.K_ESCAPE:
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print("quit!")
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self.controlling = False
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if self.rc_socket:
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self.rc_socket.send('(0.0,0.0)\n')
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self.anim_stopped = True
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return
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elif event.key == pygame.QUIT:
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print("quit!")
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self.controlling = False
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if self.rc_socket:
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self.rc_socket.send('(0.0,0.0)\n')
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self.anim_stopped = True
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return
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if self.mpc:
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x_pred = self.get_measurement_prediction()
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tmpc_start = time.time()
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error_pos = np.linalg.norm(x_pred[0:2] - self.target[0:2])
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angles_unwrapped = np.unwrap([x_pred[2], self.target[2]]) # unwrap angle to avoid jump in data
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error_ang = np.abs(angles_unwrapped[0] - angles_unwrapped[1])
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#print("error pos = ", error_pos)
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print(" error_ang = {}, target = {}, angle = {}".format(error_ang, self.target[2], x_pred[2]))
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if error_pos > 0.1 or error_ang > 0.35:
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# solve mpc open loop problem
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res = self.ols.solve(x_pred, self.target)
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#us1 = res[0]
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#us2 = res[1]
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us1 = res[0] * self.control_scaling
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us2 = res[1] * self.control_scaling
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#print("u = {}", (us1, us2))
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# save open loop trajectories for plotting
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self.mutex.acquire()
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try:
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self.ol_x = res[2]
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self.ol_y = res[3]
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finally:
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self.mutex.release()
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tmpc_end = time.time()
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#print("---------------- mpc solution took {} seconds".format(tmpc_end - tmpc_start))
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dt_mpc = time.time() - self.t
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if dt_mpc < self.dt: # wait until next control can be applied
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#print("sleeping for {} seconds...".format(self.dt - dt_mpc))
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time.sleep(self.dt - dt_mpc)
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else:
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us1 = [0] * self.mstep
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us2 = [0] * self.mstep
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near_target += 1
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robot.grid_pos = grid_pos
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# send controls to the robot
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for i in range(0, self.mstep): # option to use multistep mpc if len(range) > 1
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u1 = us1[i]
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u2 = us2[i]
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if self.rc_socket:
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self.rc_socket.send('({},{})\n'.format(u1, u2))
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if i < self.mstep:
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time.sleep(self.dt)
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self.t = time.time() # save time the most recent control was applied
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def get_measurement_prediction(self):
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# get measurement
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self.mutex.acquire()
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try:
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window = 3
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last_measurement = copy.deepcopy(self.xms[-window:])
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#print("last_measurements = {}".format(last_measurement))
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#print("mean = {}".format(np.mean(last_measurement, axis=0)))
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last_measurement = np.mean(last_measurement, axis=0)
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last_time = copy.deepcopy(self.tms[-1])
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finally:
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self.mutex.release()
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# prediction of state at time the mpc will terminate
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self.r.set_f_params(np.array([self.u1 * self.omega_max, self.u2 * self.omega_max]))
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self.r.set_initial_value(last_measurement, last_time)
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x_pred = self.r.integrate(self.r.t + self.dt)
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return x_pred
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def get_measurement(self):
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self.mutex.acquire()
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try:
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last_measurement = copy.deepcopy(self.xms[-1:])
|
|
finally:
|
|
self.mutex.release()
|
|
return last_measurement[0]
|
|
|
|
def pos_getter(self):
|
|
while True:
|
|
x_pred = self.get_measurement_prediction()
|
|
|
|
print("pos = ", x_pred)
|
|
|
|
def main(args):
|
|
parser = ArgumentParser()
|
|
parser.add_argument('id', metavar='id', type=str, help='marker id of the controlled robot')
|
|
parser.add_argument('ip', metavar='ip', type=str, help='ip address of the controlled robot')
|
|
args = parser.parse_args()
|
|
|
|
marker_id = int(args.id)
|
|
ip = args.ip
|
|
|
|
|
|
rospy.init_node('controller_node', anonymous=True)
|
|
|
|
rc = RemoteController(marker_id, ip)
|
|
|
|
pygame.init()
|
|
|
|
screenheight = 480
|
|
screenwidth = 640
|
|
pygame.display.set_mode([screenwidth, screenheight])
|
|
|
|
# print("waiting until track is completely detected")
|
|
# while not rc.track.track_complete:
|
|
# pass
|
|
|
|
#threading.Thread(target=rc.input_handling).start()
|
|
controller_thread = threading.Thread(target=rc.controller)
|
|
controller_thread.start()
|
|
|
|
#time.sleep(10)
|
|
#rc.ani()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main(sys.argv)
|