mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
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218 lines
5.0 KiB
Python
218 lines
5.0 KiB
Python
#! /usr/bin/python
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"""
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Path tracking simulation with pure pursuit steering control and PID speed control.
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author: Atsushi Sakai
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"""
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# import numpy as np
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import math
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import matplotlib.pyplot as plt
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import unicycle_model
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from pycubicspline import pycubicspline
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Kp = 1.0 # speed propotional gain
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Lf = 1.0 # look-ahead distance
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# animation = True
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animation = False
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def PIDControl(target, current):
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a = Kp * (target - current)
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return a
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def pure_pursuit_control(state, cx, cy, pind):
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ind = calc_target_index(state, cx, cy)
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if pind >= ind:
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ind = pind
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# print(pind, ind)
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if ind < len(cx):
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tx = cx[ind]
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ty = cy[ind]
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else:
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tx = cx[-1]
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ty = cy[-1]
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ind = len(cx) - 1
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alpha = math.atan2(ty - state.y, tx - state.x) - state.yaw
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if state.v < 0: # back
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alpha = math.pi - alpha
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# if alpha > 0:
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# alpha = math.pi - alpha
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# else:
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# alpha = math.pi + alpha
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delta = math.atan2(2.0 * unicycle_model.L * math.sin(alpha) / Lf, 1.0)
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return delta, ind
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def calc_target_index(state, cx, cy):
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dx = [state.x - icx for icx in cx]
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dy = [state.y - icy for icy in cy]
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d = [abs(math.sqrt(idx ** 2 + idy ** 2)) for (idx, idy) in zip(dx, dy)]
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ind = d.index(min(d))
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L = 0.0
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while Lf > L and (ind + 1) < len(cx):
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dx = cx[ind + 1] - cx[ind]
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dy = cx[ind + 1] - cx[ind]
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L += math.sqrt(dx ** 2 + dy ** 2)
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ind += 1
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return ind
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def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal):
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T = 500.0 # max simulation time
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goal_dis = 0.3
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stop_speed = 0.05
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state = unicycle_model.State(x=-0.0, y=-0.0, yaw=0.0, v=0.0)
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# lastIndex = len(cx) - 1
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time = 0.0
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x = [state.x]
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y = [state.y]
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yaw = [state.yaw]
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v = [state.v]
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t = [0.0]
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target_ind = calc_target_index(state, cx, cy)
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while T >= time:
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di, target_ind = pure_pursuit_control(state, cx, cy, target_ind)
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ai = PIDControl(speed_profile[target_ind], state.v)
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state = unicycle_model.update(state, ai, di)
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if abs(state.v) <= stop_speed:
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target_ind += 1
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time = time + unicycle_model.dt
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# check goal
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dx = state.x - goal[0]
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dy = state.y - goal[1]
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if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis:
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print("Goal")
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break
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x.append(state.x)
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y.append(state.y)
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yaw.append(state.yaw)
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v.append(state.v)
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t.append(time)
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if target_ind % 20 == 0 and animation:
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plt.cla()
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plt.plot(cx, cy, "-r", label="course")
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plt.plot(x, y, "ob", label="trajectory")
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plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
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plt.axis("equal")
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plt.grid(True)
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plt.title("speed:" + str(round(state.v, 2)) +
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"tind:" + str(target_ind))
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plt.pause(0.0001)
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return t, x, y, yaw, v
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def set_stop_point(target_speed, cx, cy, cyaw):
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speed_profile = [target_speed] * len(cx)
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d = []
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direction = 1.0
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# Set stop point
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for i in range(len(cx) - 1):
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dx = cx[i + 1] - cx[i]
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dy = cy[i + 1] - cy[i]
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td = math.sqrt(dx ** 2.0 + dy ** 2.0)
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d.append(td)
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dyaw = cyaw[i + 1] - cyaw[i]
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switch = math.pi / 4.0 <= dyaw < math.pi / 2.0
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if switch:
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direction *= -1
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if direction != 1.0:
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speed_profile[i] = - target_speed
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else:
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speed_profile[i] = target_speed
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if switch:
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speed_profile[i] = 0.0
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speed_profile[0] = 0.0
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speed_profile[-1] = 0.0
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d.append(d[-1])
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return speed_profile, d
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def calc_speed_profile(cx, cy, cyaw, target_speed):
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speed_profile, d = set_stop_point(target_speed, cx, cy, cyaw)
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# flg, ax = plt.subplots(1)
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# plt.plot(speed_profile, "-r")
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# plt.show()
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return speed_profile
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def main():
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print("rear wheel feedback tracking start!!")
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ax = [0.0, 6.0, 12.5, 5.0, 7.5, 3.0, -1.0]
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ay = [0.0, 0.0, 5.0, 6.5, 0.0, 5.0, -2.0]
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goal = [ax[-1], ay[-1]]
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cx, cy, cyaw, ck, s = pycubicspline.calc_spline_course(ax, ay, ds=0.1)
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target_speed = 10.0 / 3.6
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sp = calc_speed_profile(cx, cy, cyaw, target_speed)
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t, x, y, yaw, v = closed_loop_prediction(cx, cy, cyaw, sp, goal)
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flg, _ = plt.subplots(1)
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print(len(ax), len(ay))
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plt.plot(ax, ay, "xb", label="input")
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plt.plot(cx, cy, "-r", label="spline")
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plt.plot(x, y, "-g", label="tracking")
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plt.grid(True)
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plt.axis("equal")
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plt.xlabel("x[m]")
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plt.ylabel("y[m]")
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plt.legend()
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flg, ax = plt.subplots(1)
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plt.plot(s, [math.degrees(iyaw) for iyaw in cyaw], "-r", label="yaw")
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plt.grid(True)
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plt.legend()
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plt.xlabel("line length[m]")
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plt.ylabel("yaw angle[deg]")
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flg, ax = plt.subplots(1)
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plt.plot(s, ck, "-r", label="curvature")
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plt.grid(True)
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plt.legend()
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plt.xlabel("line length[m]")
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plt.ylabel("curvature [1/m]")
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plt.show()
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if __name__ == '__main__':
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main()
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