def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
T = 500.0 # max simulation time
goal_dis = 0.3
stop_speed = 0.05
state = unicycle_model.State(x=-0.0, y=-0.0, yaw=0.0, v=0.0)
time = 0.0
x = [state.x]
y = [state.y]
yaw = [state.yaw]
v = [state.v]
t = [0.0]
target_ind = calc_nearest_index(state, cx, cy, cyaw)
e, e_th = 0.0, 0.0
while T >= time:
dl, target_ind, e, e_th = lqr_steering_control(state, cx, cy, cyaw, ck,
e, e_th)
ai = PIDControl(speed_profile[target_ind], state.v)
# state = unicycle_model.update(state, ai, di)
state = unicycle_model.update(state, ai, dl)
if abs(state.v) <= stop_speed:
target_ind += 1
time = time + unicycle_model.dt
# check goal
dx = state.x - goal[0]
dy = state.y - goal[1]
if math.sqrt(dx**2 + dy**2) <= goal_dis:
print("Goal")
break
x.append(state.x)
y.append(state.y)
yaw.append(state.yaw)
v.append(state.v)
t.append(time)
if target_ind % 1 == 0:
plt.cla()
plt.plot(cx, cy, "-r", label="course")
plt.plot(x, y, "ob", label="trajectory")
plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
plt.axis("equal")
plt.grid(True)
plt.title("speed[km/h]:" + str(round(state.v * 3.6, 2)) +
",target index:" + str(target_ind))
plt.pause(0.0001)
matplotrecorder.save_frame() # save each frame
plt.close()
return t, x, y, yaw, v
def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal):
T = 500.0 # max simulation time
goal_dis = 0.3
stop_speed = 0.05
state = unicycle_model.State(x=-0.0, y=-0.0, 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:
di, target_ind = pure_pursuit_control(state, cx, cy, target_ind)
ai = PIDControl(speed_profile[target_ind], state.v)
state = unicycle_model.update(state, ai, di)
if abs(state.v) <= stop_speed:
target_ind += 1
time = time + unicycle_model.dt
# check goal
dx = state.x - goal[0]
dy = state.y - goal[1]
if math.sqrt(dx**2 + dy**2) <= goal_dis:
print("Goal")
break
x.append(state.x)
y.append(state.y)
yaw.append(state.yaw)
v.append(state.v)
t.append(time)
if target_ind % 20 == 0 and animation:
plt.cla()
plt.plot(cx, cy, "-r", label="course")
plt.plot(x, y, "ob", label="trajectory")
plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
plt.axis("equal")
plt.grid(True)
plt.title("speed:" + str(round(state.v, 2)) + "tind:" +
str(target_ind))
plt.pause(0.0001)
return t, x, y, yaw, v
def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal):
state = unicycle_model.State(x=-0.0, y=-0.0, 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]
a = [0.0]
d = [0.0]
target_ind, mindis = calc_target_index(state, cx, cy)
find_goal = False
maxdis = 0.5
while T >= time:
di, target_ind, dis = pure_pursuit_control(state, cx, cy, target_ind)
target_speed = speed_profile[target_ind]
target_speed = target_speed * \
(maxdis - min(dis, maxdis - 0.1)) / maxdis
ai = PIDControl(target_speed, state.v)
state = unicycle_model.update(state, ai, di)
if abs(state.v) <= stop_speed and target_ind <= len(cx) - 2:
target_ind += 1
time = time + unicycle_model.dt
# check goal
dx = state.x - goal[0]
dy = state.y - goal[1]
if math.sqrt(dx**2 + dy**2) <= goal_dis:
find_goal = True
break
x.append(state.x)
y.append(state.y)
yaw.append(state.yaw)
v.append(state.v)
t.append(time)
a.append(ai)
d.append(di)
if target_ind % 1 == 0 and animation:
plt.cla()
plt.plot(cx, cy, "-r", label="course")
plt.plot(x, y, "ob", label="trajectory")
plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
plt.axis("equal")
plt.grid(True)
plt.title("speed:" + str(round(state.v, 2)) + "tind:" +
str(target_ind))
plt.pause(0.0001)
else:
print("Time out!!")
return t, x, y, yaw, v, a, d, find_goal
def main():
# target course
import numpy as np
cx = np.arange(0, 50, 0.1)
cy = [math.sin(ix / 5.0) * ix / 2.0 for ix in cx]
target_speed = 5.0 / 3.6
T = 15.0 # max simulation time
state = unicycle_model.State(x=-0.0, y=-3.0, yaw=0.0, v=0.0)
# state = unicycle_model.State(x=-1.0, y=-5.0, yaw=0.0, v=-30.0 / 3.6)
# state = unicycle_model.State(x=10.0, y=5.0, yaw=0.0, v=-30.0 / 3.6)
# state = unicycle_model.State(
# x=3.0, y=5.0, yaw=np.deg2rad(-40.0), v=-10.0 / 3.6)
# state = unicycle_model.State(
# x=3.0, y=5.0, yaw=np.deg2rad(40.0), v=50.0 / 3.6)
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 = PIDControl(target_speed, state.v)
di, target_ind = pure_pursuit_control(state, cx, cy, target_ind)
state = unicycle_model.update(state, ai, di)
time = time + unicycle_model.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], "xg", label="target")
# plt.axis("equal")
# plt.grid(True)
# plt.pause(0.1)
# input()
flg, ax = plt.subplots(1)
plt.plot(cx, cy, ".r", label="course")
plt.plot(x, y, "-b", label="trajectory")
plt.legend()
plt.xlabel("x[m]")
plt.ylabel("y[m]")
plt.axis("equal")
plt.grid(True)
flg, ax = plt.subplots(1)
plt.plot(t, [iv * 3.6 for iv in v], "-r")
plt.xlabel("Time[s]")
plt.ylabel("Speed[km/h]")
plt.grid(True)
plt.show()
def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal):
state = unicycle_model.State(x=-0.0, y=-0.0, 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]
a = [0.0]
d = [0.0]
target_ind, mindis = calc_target_index(state, cx, cy)
find_goal = False
maxdis = 0.5
while T >= time:
di, target_ind, dis = pure_pursuit_control(state, cx, cy, target_ind)
target_speed = speed_profile[target_ind]
target_speed = target_speed * \
(maxdis - min(dis, maxdis - 0.1)) / maxdis
ai = PIDControl(target_speed, state.v)
state = unicycle_model.update(state, ai, di)
if abs(state.v) <= stop_speed and target_ind <= len(cx) - 2:
target_ind += 1
time = time + unicycle_model.dt
# check goal
dx = state.x - goal[0]
dy = state.y - goal[1]
if math.hypot(dx, dy) <= goal_dis:
find_goal = True
break
x.append(state.x)
y.append(state.y)
yaw.append(state.yaw)
v.append(state.v)
t.append(time)
a.append(ai)
d.append(di)
if target_ind % 1 == 0 and animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect(
'key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
plt.plot(cx, cy, "-r", label="course")
plt.plot(x, y, "ob", label="trajectory")
plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
plt.axis("equal")
plt.grid(True)
plt.title("speed:" + str(round(state.v, 2)) + "tind:" +
str(target_ind))
plt.pause(0.0001)
else:
print("Time out!!")
return t, x, y, yaw, v, a, d, find_goal
def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal):
state = unicycle_model.State(x=-0.0, y=-0.0, 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]
a = [0.0]
d = [0.0]
target_ind, mindis = calc_target_index(state, cx, cy)
find_goal = False
maxdis = 0.5
while T >= time:
di, target_ind, dis = pure_pursuit_control(state, cx, cy, target_ind)
target_speed = speed_profile[target_ind]
target_speed = target_speed * \
(maxdis - min(dis, maxdis - 0.1)) / maxdis
ai = PIDControl(target_speed, state.v)
state = unicycle_model.update(state, ai, di)
if abs(state.v) <= stop_speed and target_ind <= len(cx) - 2:
target_ind += 1
time = time + unicycle_model.dt
# check goal
dx = state.x - goal[0]
dy = state.y - goal[1]
if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis:
find_goal = True
break
x.append(state.x)
y.append(state.y)
yaw.append(state.yaw)
v.append(state.v)
t.append(time)
a.append(ai)
d.append(di)
if target_ind % 1 == 0 and animation:
plt.cla()
plt.plot(cx, cy, "-r", label="course")
plt.plot(x, y, "ob", label="trajectory")
plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
plt.axis("equal")
plt.grid(True)
plt.title("speed:" + str(round(state.v, 2)) +
"tind:" + str(target_ind))
plt.pause(0.0001)
else:
print("Time out!!")
return t, x, y, yaw, v, a, d, find_goal
def main():
# target course
import numpy as np
cx = np.arange(0, 50, 0.1)
cy = [math.sin(ix / 5.0) * ix / 2.0 for ix in cx]
target_speed = 5.0 / 3.6
T = 15.0 # max simulation time
state = unicycle_model.State(x=-0.0, y=-3.0, yaw=0.0, v=0.0)
# state = unicycle_model.State(x=-1.0, y=-5.0, yaw=0.0, v=-30.0 / 3.6)
# state = unicycle_model.State(x=10.0, y=5.0, yaw=0.0, v=-30.0 / 3.6)
# state = unicycle_model.State(
# x=3.0, y=5.0, yaw=math.radians(-40.0), v=-10.0 / 3.6)
# state = unicycle_model.State(
# x=3.0, y=5.0, yaw=math.radians(40.0), v=50.0 / 3.6)
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 = PIDControl(target_speed, state.v)
di, target_ind = pure_pursuit_control(state, cx, cy, target_ind)
state = unicycle_model.update(state, ai, di)
time = time + unicycle_model.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], "xg", label="target")
# plt.axis("equal")
# plt.grid(True)
# plt.pause(0.1)
# input()
flg, ax = plt.subplots(1)
plt.plot(cx, cy, ".r", label="course")
plt.plot(x, y, "-b", label="trajectory")
plt.legend()
plt.xlabel("x[m]")
plt.ylabel("y[m]")
plt.axis("equal")
plt.grid(True)
flg, ax = plt.subplots(1)
plt.plot(t, [iv * 3.6 for iv in v], "-r")
plt.xlabel("Time[s]")
plt.ylabel("Speed[km/h]")
plt.grid(True)
plt.show()
