/
dwa_evader_control.py
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/
dwa_evader_control.py
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"""
Mobile robot motion planning sample with Dynamic Window Approach
author: Atsushi Sakai (@Atsushi_twi)
"""
import math
import numpy as np
import matplotlib.pyplot as plt
import sys
from sklearn import preprocessing
sys.path.append("../../")
show_animation = True
class ConfigDWA():
# simulation parameters
def __init__(self, max_v, min_v, max_w, max_acc_v, max_acc_w, v_reso=0.01, yawrate_reso=0.1, dt=0.1,
predict_time=1.0, to_goal_cost_gain=0.6, speed_cost_gain=0.3, ob_gain=0.3, robot_raduis=0.3, obstacle_radius=0.3):
# robot parameter
self.max_speed = max_v # [m/s]
self.min_speed = min_v # [m/s]
self.max_yawrate = max_w # [rad/s]
self.max_accel = max_acc_v # [m/ss]
self.max_dyawrate = max_acc_w # [rad/ss]
self.v_reso = v_reso # [m/s]
self.yawrate_reso = yawrate_reso # [rad/s]
self.dt = dt # [s]
self.predict_time = predict_time # [s]
self.to_goal_cost_gain = to_goal_cost_gain
self.speed_cost_gain = speed_cost_gain
self.ob_gain = ob_gain
self.robot_radius = robot_raduis # [m]
self.obstacle_radius = obstacle_radius # [m]
def motion(x, u, dt):
# motion model
x[2] += u[1] * dt
x[0] += u[0] * math.cos(x[2]) * dt
x[1] += u[0] * math.sin(x[2]) * dt
x[3] = u[0]
x[4] = u[1]
return x
def calc_dynamic_window(x, config):
# Dynamic window from robot specification
Vs = [config.min_speed, config.max_speed,
-config.max_yawrate, config.max_yawrate]
# Dynamic window from motion model
Vd = [x[3] - config.max_accel * config.dt,
x[3] + config.max_accel * config.dt,
x[4] - config.max_dyawrate * config.dt,
x[4] + config.max_dyawrate * config.dt]
# [vmin,vmax, yawrate min, yawrate max]
dw = [max(Vs[0], Vd[0]), min(Vs[1], Vd[1]),
max(Vs[2], Vd[2]), min(Vs[3], Vd[3])]
return dw
def calc_trajectory(xinit, v, y, config):
x = np.array(xinit)
traj = np.array(x)
time = 0
while time <= config.predict_time:
x = motion(x, [v, y], config.dt)
traj = np.vstack((traj, x))
time += config.dt
return traj
def calc_final_input(x, u, dw, config, goal, ob):
xinit = x[:]
min_cost = 10000.0
min_u = u
min_u[0] = 0.0
best_traj = np.array([x])
traj_list = []
cost_list = []
u_list = []
# evalucate all trajectory with sampled input in dynamic window
for v in np.arange(dw[0], dw[1], config.v_reso):
for y in np.arange(dw[2], dw[3], config.yawrate_reso):
traj = calc_trajectory(xinit, v, y, config)
# calc cost
to_goal_cost = calc_to_goal_cost(traj, goal, config)
# speed_cost = config.speed_cost_gain * \
# (config.max_speed - traj[-1, 3])
speed_cost = config.max_speed - traj[-1, 3]
ob_cost = calc_obstacle_cost(traj, ob, config)
# print(ob_cost)
final_cost = to_goal_cost + speed_cost + ob_cost
#print (final_cost)
if not np.isinf(final_cost):
traj_list.append(traj)
cost_list.append([to_goal_cost, speed_cost, ob_cost])
# print([to_goal_cost, speed_cost, ob_cost])
u_list.append([v, y])
# search minimum trajectory
# if min_cost >= final_cost:
# min_cost = final_cost
# min_u = [v, y]
# best_traj = traj
# normalize cost
if len(cost_list)>0:
cost_list = np.array(cost_list)
cost_var = np.var(cost_list, axis=0)
for j in range(len(cost_var)):
if cost_var[j]!=0.0:
cost_list[:,j] = preprocessing.minmax_scale(cost_list[:,j])
cost_sum = cost_list[:, 0] * config.to_goal_cost_gain \
+ cost_list[:, 1] * config.speed_cost_gain \
+ cost_list[:, 2] * config.ob_gain
cost_sum = cost_sum.tolist()
best_index = cost_sum.index(min(cost_sum))
min_u = u_list[best_index]
best_traj = traj_list[best_index]
return min_u, best_traj
def calc_obstacle_cost(traj, ob, config):
# calc obstacle cost inf: collistion, 0:free
skip_n = 1
minr = float("inf")
if ob is None:
return 0
for ii in range(0, len(traj[:, 1]), skip_n):
for i in range(len(ob[:, 0])):
ox = ob[i, 0]
oy = ob[i, 1]
dx = traj[ii, 0] - ox
dy = traj[ii, 1] - oy
r = math.sqrt(dx**2 + dy**2)
if r <= config.robot_radius + config.obstacle_radius:
return float("Inf") # collision
if minr >= r:
minr = r
return 1.0/abs(minr-config.robot_radius - config.obstacle_radius) # OK
def calc_to_goal_cost(traj, goal, config):
# calc to goal cost. It is 2D norm.
def normalize_angle(angle):
norm_angle = angle % (2 * math.pi)
if norm_angle > math.pi:
norm_angle -= 2 * math.pi
return norm_angle
# goal_magnitude = math.sqrt(goal[0]**2 + goal[1]**2)
# traj_magnitude = math.sqrt(traj[-1, 0]**2 + traj[-1, 1]**2)
# dot_product = (goal[0] * traj[-1, 0]) + (goal[1] * traj[-1, 1])
# error = dot_product / (goal_magnitude * traj_magnitude)
# error_angle = math.acos(error)
# cost = config.to_goal_cost_gain * error_angle
# mid_point = int(round(config.predict_time/config.dt/2))
mid_point = 4
goal_line_angle = math.atan2(goal[1] - traj[mid_point, 1], goal[0]-traj[mid_point, 0])
traj_angle = traj[mid_point, 2]
error_angle = abs(normalize_angle(goal_line_angle-traj_angle))
# cost = config.to_goal_cost_gain * error_angle
cost = error_angle
return -cost
def dwa_control(x, u, config, goal, ob):
# Dynamic Window control
dw = calc_dynamic_window(x, config)
u, traj = calc_final_input(x, u, dw, config, goal, ob)
return u, traj
def plot_arrow(x, y, yaw, length=0.5, width=0.1): # pragma: no cover
plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw),
head_length=width, head_width=width)
plt.plot(x, y)
from robot_model import RobotModel
if __name__ == '__main__':
gx = 10.0
gy = 12.0
print(__file__ + " start!!")
# initial state [x(m), y(m), yaw(rad), v(m/s), omega(rad/s)]
# x = np.array([0.0, 0.0, math.pi / 8.0, 0.0, 0.0])
motionModel = RobotModel(2, 0, math.pi / 3 * 2, 0.7, math.pi / 3 * 5,
0.0, 0.0, math.pi / 8.0, 0.0, 0.0)
x = motionModel.state
# goal position [x(m), y(m)]
target_model = RobotModel(2, 0, math.pi / 3 * 2, 0.7, math.pi / 3 * 5,
gx, gy, -math.pi / 2.0, 0.0, 0.0)
goal = target_model.state[:2]
# obstacles [x(m) y(m), ....]
ob = np.array([[-1, -1],
[0, 2],
[4.0, 2.0],
[5.0, 4.0],
[5.0, 5.0],
[5.0, 6.0],
[5.0, 9.0],
[8.0, 9.0],
[7.0, 9.0],
[12.0, 12.0],
[4.0, 12.0],
[3.5, 15.8],
[12.1, 17.0],
[7.16, 14.6],
[8.6, 13.0]
])
u = np.array([0.0, 0.0])
config = ConfigDWA(2, 0, math.pi / 3 * 2, 0.7, math.pi / 3 * 5)
traj = np.array(x)
for i in range(1000):
u, ltraj = dwa_control(x, u, config, goal, ob)
x = motionModel.motion(u, config.dt)
target_model.motion([1.8, -math.pi/5.0], config.dt)
goal = target_model.state[:2]
traj = np.vstack((traj, x)) # store state history
# print(traj)
if show_animation:
plt.cla()
plt.plot(ltraj[:, 0], ltraj[:, 1], "-g")
plt.plot(x[0], x[1], "xr")
plt.plot(goal[0], goal[1], "xb")
plt.plot(ob[:, 0], ob[:, 1], "ok")
plot_arrow(x[0], x[1], x[2])
plt.axis("equal")
plt.grid(True)
plt.pause(0.0001)
# check goal
if math.sqrt((x[0] - goal[0])**2 + (x[1] - goal[1])**2) <= config.robot_radius*2:
print("Goal!!")
break
# check collision
collision = False
for obstacle in ob:
if math.sqrt((x[0] - obstacle[0]) ** 2 + (x[1] - obstacle[1]) ** 2) <= (
motionModel.robot_radius + config.obstacle_radius):
collision = True
if collision:
print("Collision!")
break
print("Done")
plt.plot(traj[:, 0], traj[:, 1], "-r")
plt.xlabel('X(m)')
plt.ylabel('Y(m)')
plt.figure()
# plot v(t)
plt.figure(figsize=(5,2))
plt.plot(np.arange(0, len(traj))*config.dt, traj[:, 3], "-b")
plt.xlabel('T(s)')
plt.ylabel('V(m/s)')
# plot w(t)
plt.figure(figsize=(5,2))
plt.plot(np.arange(0, len(traj))*config.dt, traj[:, 4], "-g")
plt.xlabel('T(s)')
plt.ylabel('W(rad/s)')
plt.show()