https://blog.csdn.net/adamshan/article/details/80555174
# coding=utf-8
import numpy as np
import math
import matplotlib.pyplot as plt
k = 0.1 # 前視距離系數
Lfc = 2.0 # 前視距離
Kp = 1.0 # 速度P控制器系數
dt = 0.1 # 時間間隔，機關：s
L = 2.9 # 車輛軸距，機關：m
class VehicleState:
def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
self.x = x
self.y = y
self.yaw = yaw
self.v = v
def update(state, a, delta):
state.x = state.x + state.v * math.cos(state.yaw) * dt
state.y = state.y + state.v * math.sin(state.yaw) * dt
state.yaw = state.yaw + state.v / L * math.tan(delta) * dt
state.v = state.v + a * dt
return state
def PControl(target, current):
a = Kp * (target - current)
return a
def pure_pursuit_control(state, cx, cy, pind):
ind = calc_target_index(state, cx, cy)
if pind >= ind:
ind = pind
if ind < len(cx):
tx = cx[ind]
ty = cy[ind]
else:
tx = cx[-1]
ty = cy[-1]
ind = len(cx) - 1
alpha = math.atan2(ty - state.y, tx - state.x) - state.yaw
if state.v < 0: # back
alpha = math.pi - alpha
Lf = k * state.v + Lfc
delta = math.atan2(2.0 * L * math.sin(alpha) / Lf, 1.0)
return delta, ind
def calc_target_index(state, cx, cy):
# 搜尋最臨近的路點
dx = [state.x - icx for icx in cx]
dy = [state.y - icy for icy in cy]
d = [abs(math.sqrt(idx ** 2 + idy ** 2)) for (idx, idy) in zip(dx, dy)]
ind = d.index(min(d))
L = 0.0
Lf = k * state.v + Lfc
while Lf > L and (ind + 1) < len(cx):
dx = cx[ind + 1] - cx[ind]
dy = cx[ind + 1] - cx[ind]
L += math.sqrt(dx ** 2 + dy ** 2)
ind += 1
return ind
def main():
# 設定目标路點
cx = np.arange(0, 50, 1)
cy = [math.sin(ix / 5.0) * ix / 2.0 for ix in cx]
target_speed = 10.0 / 3.6 # [m/s]
T = 100.0 # 最大模拟時間
# 設定車輛的出事狀态
state = VehicleState(x=-0.0, y=-3.0, yaw=0.0, v=0.0)
lastIndex = len(cx) - 1
time = 0.0
x = [state.x]
y = [state.y]
yaw = [state.yaw]
v = [state.v]
t = [0.0]
target_ind = calc_target_index(state, cx, cy)
while T >= time and lastIndex > target_ind:
ai = PControl(target_speed, state.v)
di, target_ind = pure_pursuit_control(state, cx, cy, target_ind)
state = update(state, ai, di)
time = time + dt
x.append(state.x)
y.append(state.y)
yaw.append(state.yaw)
v.append(state.v)
t.append(time)
plt.cla()
plt.plot(cx, cy, ".r", label="course")
plt.plot(x, y, "-b", label="trajectory")
plt.plot(cx[target_ind], cy[target_ind], "go", label="target")
plt.axis("equal")
plt.grid(True)
plt.title("Speed[km/h]:" + str(state.v * 3.6)[:4])
plt.pause(0.001)
if __name__ == '__main__':
main()