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https://github.com/AtsushiSakai/PythonRobotics.git
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166 lines
3.8 KiB
Python
166 lines
3.8 KiB
Python
"""
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Path tracking simulation with Stanley steering control and PID speed control.
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author: Atsushi Sakai (@Atsushi_twi)
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"""
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import math
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import matplotlib.pyplot as plt
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from pycubicspline import pycubicspline
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k = 0.5 # control gain
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Kp = 1.0 # speed propotional gain
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dt = 0.1 # [s] time difference
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L = 2.9 # [m] Wheel base of vehicle
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max_steer = math.radians(30.0) # [rad] max steering angle
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show_animation = True
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class State:
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def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
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self.x = x
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self.y = y
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self.yaw = yaw
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self.v = v
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def update(state, a, delta):
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if delta >= max_steer:
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delta = max_steer
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elif delta <= -max_steer:
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delta = -max_steer
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state.x = state.x + state.v * math.cos(state.yaw) * dt
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state.y = state.y + state.v * math.sin(state.yaw) * dt
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state.yaw = state.yaw + state.v / L * math.tan(delta) * dt
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state.yaw = pi_2_pi(state.yaw)
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state.v = state.v + a * dt
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return state
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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 stanley_control(state, cx, cy, cyaw, pind):
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ind, efa = calc_target_index(state, cx, cy)
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if pind >= ind:
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ind = pind
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theta_e = pi_2_pi(cyaw[ind] - state.yaw)
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theta_d = math.atan2(k * efa, state.v)
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delta = theta_e + theta_d
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return delta, ind
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def pi_2_pi(angle):
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while (angle > math.pi):
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angle = angle - 2.0 * math.pi
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while (angle < -math.pi):
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angle = angle + 2.0 * math.pi
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return angle
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def calc_target_index(state, cx, cy):
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# calc frant axle position
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fx = state.x + L * math.cos(state.yaw)
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fy = state.y + L * math.sin(state.yaw)
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# search nearest point index
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dx = [fx - icx for icx in cx]
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dy = [fy - icy for icy in cy]
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d = [math.sqrt(idx ** 2 + idy ** 2) for (idx, idy) in zip(dx, dy)]
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mind = min(d)
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ind = d.index(mind)
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tyaw = pi_2_pi(math.atan2(fy - cy[ind], fx - cx[ind]) - state.yaw)
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if tyaw > 0.0:
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mind = - mind
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return ind, mind
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def main():
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# target course
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ax = [0.0, 100.0, 100.0, 50.0, 60.0]
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ay = [0.0, 0.0, -30.0, -20.0, 0.0]
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cx, cy, cyaw, ck, s = pycubicspline.calc_spline_course(ax, ay, ds=0.1)
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target_speed = 30.0 / 3.6 # [m/s]
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T = 100.0 # max simulation time
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# initial state
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state = State(x=-0.0, y=5.0, yaw=math.radians(20.0), 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, mind = calc_target_index(state, cx, cy)
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while T >= time and lastIndex > target_ind:
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ai = PIDControl(target_speed, state.v)
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di, target_ind = stanley_control(state, cx, cy, cyaw, target_ind)
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state = update(state, ai, di)
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time = time + dt
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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 show_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, "-b", 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[km/h]:" + str(state.v * 3.6)[:4])
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plt.pause(0.001)
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# Test
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assert lastIndex >= target_ind, "Cannot goal"
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if show_animation:
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plt.plot(cx, cy, ".r", label="course")
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plt.plot(x, y, "-b", label="trajectory")
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plt.legend()
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plt.xlabel("x[m]")
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plt.ylabel("y[m]")
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plt.axis("equal")
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plt.grid(True)
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flg, ax = plt.subplots(1)
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plt.plot(t, [iv * 3.6 for iv in v], "-r")
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plt.xlabel("Time[s]")
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plt.ylabel("Speed[km/h]")
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plt.grid(True)
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plt.show()
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if __name__ == '__main__':
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main()
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