mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
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329 lines
8.1 KiB
Python
329 lines
8.1 KiB
Python
"""
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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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Kp = 2.0 # speed propotional gain
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Lf = 0.5 # look-ahead distance
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T = 100.0 # max simulation time
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goal_dis = 0.5
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stop_speed = 0.5
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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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if a > unicycle_model.accel_max:
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a = unicycle_model.accel_max
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elif a < -unicycle_model.accel_max:
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a = -unicycle_model.accel_max
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return a
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def pure_pursuit_control(state, cx, cy, pind):
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ind, dis = 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 <= 0.0: # back
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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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if delta > unicycle_model.steer_max:
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delta = unicycle_model.steer_max
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elif delta < - unicycle_model.steer_max:
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delta = -unicycle_model.steer_max
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return delta, ind, dis
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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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mindis = min(d)
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ind = d.index(mindis)
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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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# print(mindis)
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return ind, mindis
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def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal):
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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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a = [0.0]
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d = [0.0]
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target_ind, mindis = calc_target_index(state, cx, cy)
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find_goal = False
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maxdis = 0.5
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while T >= time:
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di, target_ind, dis = pure_pursuit_control(state, cx, cy, target_ind)
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target_speed = speed_profile[target_ind]
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target_speed = target_speed * \
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(maxdis - min(dis, maxdis - 0.1)) / maxdis
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ai = PIDControl(target_speed, state.v)
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state = unicycle_model.update(state, ai, di)
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if abs(state.v) <= stop_speed and target_ind <= len(cx) - 2:
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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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find_goal = True
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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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a.append(ai)
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d.append(di)
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if target_ind % 1 == 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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else:
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print("Time out!!")
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return t, x, y, yaw, v, a, d, find_goal
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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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forward = True
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d = []
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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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d.append(math.sqrt(dx ** 2.0 + dy ** 2.0))
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iyaw = cyaw[i]
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move_direction = math.atan2(dy, dx)
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is_back = abs(move_direction - iyaw) >= math.pi / 2.0
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if dx == 0.0 and dy == 0.0:
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continue
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if is_back:
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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 is_back and forward:
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speed_profile[i] = 0.0
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forward = False
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# plt.plot(cx[i], cy[i], "xb")
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# print(iyaw, move_direction, dx, dy)
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elif not is_back and not forward:
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speed_profile[i] = 0.0
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forward = True
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# plt.plot(cx[i], cy[i], "xb")
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# print(iyaw, move_direction, dx, dy)
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speed_profile[0] = 0.0
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if is_back:
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speed_profile[-1] = -stop_speed
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else:
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speed_profile[-1] = stop_speed
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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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if animation:
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plt.plot(speed_profile, "xb")
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return speed_profile
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def extend_path(cx, cy, cyaw):
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dl = 0.1
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dl_list = [dl] * (int(Lf / dl) + 1)
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move_direction = math.atan2(cy[-1] - cy[-3], cx[-1] - cx[-3])
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is_back = abs(move_direction - cyaw[-1]) >= math.pi / 2.0
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for idl in dl_list:
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if is_back:
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idl *= -1
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cx = np.append(cx, cx[-1] + idl * math.cos(cyaw[-1]))
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cy = np.append(cy, cy[-1] + idl * math.sin(cyaw[-1]))
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cyaw = np.append(cyaw, cyaw[-1])
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return cx, cy, cyaw
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def main():
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# target course
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import numpy as np
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cx = np.arange(0, 50, 0.1)
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cy = [math.sin(ix / 5.0) * ix / 2.0 for ix in cx]
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target_speed = 5.0 / 3.6
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T = 15.0 # max simulation time
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state = unicycle_model.State(x=-0.0, y=-3.0, yaw=0.0, v=0.0)
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# state = unicycle_model.State(x=-1.0, y=-5.0, yaw=0.0, v=-30.0 / 3.6)
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# state = unicycle_model.State(x=10.0, y=5.0, yaw=0.0, v=-30.0 / 3.6)
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# state = unicycle_model.State(
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# x=3.0, y=5.0, yaw=np.deg2rad(-40.0), v=-10.0 / 3.6)
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# state = unicycle_model.State(
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# x=3.0, y=5.0, yaw=np.deg2rad(40.0), v=50.0 / 3.6)
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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 and lastIndex > target_ind:
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ai = PIDControl(target_speed, state.v)
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di, target_ind, _ = pure_pursuit_control(state, cx, cy, target_ind)
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state = unicycle_model.update(state, ai, di)
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time = time + unicycle_model.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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# 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.pause(0.1)
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# input()
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plt.subplots(1)
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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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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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def main2():
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import pandas as pd
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data = pd.read_csv("rrt_course.csv")
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cx = np.array(data["x"])
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cy = np.array(data["y"])
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cyaw = np.array(data["yaw"])
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target_speed = 10.0 / 3.6
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goal = [cx[-1], cy[-1]]
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cx, cy, cyaw = extend_path(cx, cy, cyaw)
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speed_profile = calc_speed_profile(cx, cy, cyaw, target_speed)
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t, x, y, yaw, v, a, d, flag = closed_loop_prediction(
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cx, cy, cyaw, speed_profile, goal)
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plt.subplots(1)
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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(goal[0], goal[1], "xg", label="goal")
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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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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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print("Pure pursuit path tracking simulation start")
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# main()
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main2()
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