mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
synced 2026-02-11 05:55:43 -05:00
295 lines
7.4 KiB
Python
295 lines
7.4 KiB
Python
#! /usr/bin/python
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# -*- coding: utf-8 -*-
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u"""
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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 = 1.0 # speed propotional gain
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Lf = 1.0 # look-ahead distance
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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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tx = cx[ind]
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ty = cy[ind]
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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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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):
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T = 100.0 # max simulation time
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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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# print(target_ind)
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while T >= time and lastIndex > target_ind:
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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) <= 0.05:
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target_ind += 1
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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, "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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# input()
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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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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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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, a):
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speed_profile, d = set_stop_point(target_speed, cx, cy, cyaw)
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nsp = len(speed_profile)
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# plt.plot(speed_profile, "xb")
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# forward integration
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for i in range(nsp - 1):
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if speed_profile[i + 1] >= 0: # forward
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tspeed = speed_profile[i] + a * d[i]
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if tspeed <= speed_profile[i + 1]:
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speed_profile[i + 1] = tspeed
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else:
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tspeed = speed_profile[i] - a * d[i]
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if tspeed >= speed_profile[i + 1]:
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speed_profile[i + 1] = tspeed
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# plt.plot(speed_profile, "ok")
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# back integration
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for i in range(nsp - 1):
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if speed_profile[- i - 1] >= 0: # forward
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tspeed = speed_profile[-i] + a * d[-i]
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if tspeed <= speed_profile[-i - 1]:
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speed_profile[-i - 1] = tspeed
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else:
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tspeed = speed_profile[-i] - a * d[-i]
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if tspeed >= speed_profile[-i - 1]:
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speed_profile[-i - 1] = tspeed
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# flg, ax = plt.subplots(1)
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plt.plot(speed_profile, "-r")
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# plt.plot(cx, cy, "-r")
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plt.show()
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return speed_profile
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def main():
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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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a = 0.1
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speed_profile = calc_speed_profile(cx, cy, cyaw, target_speed, a)
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t, x, y, yaw, v = closed_loop_prediction(cx, cy, cyaw, speed_profile)
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flg, ax = 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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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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def main2():
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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 = 10.0 / 3.6
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T = 15.0 # max simulation time
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# state = unicycle_model.State(x=-0.0, y=-0.0, 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=math.radians(-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=math.radians(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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flg, ax = 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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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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print("Pure pursuit path tracking simulation start")
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main()
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