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)
class PursuitEnvTorque(gym.Env):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': 2
}
def __init__(self):
self.max_v = MAX_V
self.min_v = MIN_V
self.max_w = MAX_W
self.max_acc_v = 0.7
self.max_acc_w = pi / 3.0 * 5
self.init_x = 0.0
self.init_y = 6.0
self.init_yaw = pi / 8.0
self.robot_radius = 0.3
self.ob_radius = 0.3
self.target_radius = 0.3
self.dt = 0.1
self.target_init_x = 10
self.target_init_y = 4
self.target_init_yaw = pi / 2.0
self.target_u = None
self.pursuitor_model = RobotModel(self.max_v, self.min_v, self.max_w,
self.max_acc_v, self.max_acc_w,
self.init_x, self.init_y,
self.init_yaw)
self.evader_model = RobotModel(self.max_v, self.min_v, self.max_w,
self.max_acc_v, self.max_acc_w,
self.target_init_x, self.target_init_y,
self.target_init_yaw)
self.config_dwa = ConfigDWA(self.max_v,
self.min_v,
self.max_w,
self.max_acc_v,
self.max_acc_w,
robot_raduis=self.robot_radius,
obstacle_radius=self.ob_radius,
dt=self.dt)
self.confg_apf = ConfigAPF(self.max_v,
self.min_v,
self.max_w,
self.max_acc_v,
self.max_acc_w,
obstacle_radius=self.ob_radius)
self.confg_pn = ConfigPN()
"""env parameters need reset
"""
self.ob_list = None
# self.ob_list = None
self.last_obs = None
self.last_pur_state = None
self.last_tar_state = None
# trajectory
self.traj = np.array(self.get_state())
# state done
self.collision = False
self.catch = False
# actions
# self.v_reso = 0.1
# self.w_reso = pi/18.0
#
#
# self.action_table = []
# for v in np.arange(self.min_v, self.max_v+self.v_reso, self.v_reso):
# for w in np.arange(-self.max_w, self.max_w+self.w_reso, self.w_reso):
# self.action_table.append(np.array([v, w]))
# self.action_num = len(self.action_table)
self.action_num = 2
self.action_space = spaces.Box(np.array([-1]), np.array(
[+1])) # v accelerate, w accelerate
# observations = laser, goal_angle, goal_distance
self.percept_region = np.array([-4, 4, -2,
6]) # left, right, behind, ahead
self.basic_sample_reso = 0.1 # sampling resolution of percept region
self.sample_reso_scale = 1.5 # sampling density increases with distance from
def cal_samples(sample_length, basic_sample_reso, sample_reso_scale):
# get sample number
# x = sympy.Symbol('x')
# result_dict = sympy.solve(
# [basic_sample_reso * (1 - sample_reso_scale ** x) / (1 - sample_reso_scale)
# - abs(sample_length)], [x])
# print(result_dict)
a = math.log(sample_length / basic_sample_reso *
(sample_reso_scale - 1) + 1)
b = math.log(sample_reso_scale)
# print(str(a)+", "+str(b))
x = a / b
return int(x)
left_samples = right_samples = cal_samples(abs(self.percept_region[0]),
self.basic_sample_reso,
self.sample_reso_scale)
behind_samples = cal_samples(abs(self.percept_region[2]),
self.basic_sample_reso,
self.sample_reso_scale)
ahead_samples = cal_samples(abs(self.percept_region[3]),
self.basic_sample_reso,
self.sample_reso_scale)
self.sample_map_center = np.array(
[left_samples - 1, behind_samples - 1])
self.sample_map_width = left_samples + right_samples - 1
self.sample_map_height = behind_samples + ahead_samples - 1
self.percept_sample_map = []
for i in range(self.sample_map_width):
tmp_list_y = []
for j in range(self.sample_map_height):
x = self.basic_sample_reso * (1 - self.sample_reso_scale ** abs(i - self.sample_map_center[0])) \
/ (1 - self.sample_reso_scale)
x = x if i - self.sample_map_center[0] >= 0 else -x
y = self.basic_sample_reso * (1 - self.sample_reso_scale ** abs(j - self.sample_map_center[1])) \
/ (1 - self.sample_reso_scale)
y = y if j - self.sample_map_center[1] >= 0 else -y
tmp_list_y.append(np.array([x, y]))
self.percept_sample_map.append(tmp_list_y)
# self.obs_num = self.pursuitor_model.laser_num + 2
# high = np.hstack((np.zeros(self.pursuitor_model.laser_num)+self.pursuitor_model.laser_max_range, pi, 50))
# low = np.hstack((np.zeros(self.pursuitor_model.laser_num), -pi, 0))
# self.observation_space = spaces.Box(low, high, dtype=np.float)
self.obs_num = 2 + 2
self.observation_space = spaces.Box(-np.inf,
np.inf,
shape=(self.obs_num, ),
dtype=float)
# seed
self.np_random = None
self.seed()
# time limit
self.limit_step = 300
self.count = 0
self.action_plot = 0
# record
self.pursuit_strategy = None
self.traj_pursuitor = None
self.traj_evader = None
# intelligent evasion mode
self.evasion_mode = False
self.config_evasion = ConfigDWA(self.max_v * 0.9,
self.min_v,
self.max_w,
self.max_acc_v,
self.max_acc_w,
robot_raduis=self.robot_radius,
obstacle_radius=self.ob_radius,
dt=self.dt)
self.evasion_route = None
self.evasion_route_index = None
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def step(self, action):
# assert self.action_space.contains(action), "%r (%s) invalid" % (action, type(action))
observation, reward, done, info = None, None, None, None
total_reward = 0
for _ in range(random.randint(3, 3)):
observation, reward, done, info = self.mini_step(action)
total_reward += reward
self.count += 1
if done:
self.record_count = self.count
prefix = "scenario3/"
# np.save(prefix+"p_strategy.npy", np.array(self.pursuit_strategy))
# np.save(prefix+"p_hybrid.npy", np.array(self.traj_pursuitor))
# np.save(prefix+"e_hybrid.npy", np.array(self.traj_evader))
#
# np.save(prefix+"p_dwa.npy", np.array(self.traj_pursuitor))
# np.save(prefix+"e_dwa.npy", np.array(self.traj_evader))
#
# np.save(prefix+"p_apf.npy", np.array(self.traj_pursuitor))
# np.save(prefix+"e_apf.npy", np.array(self.traj_evader))
break
return observation, total_reward, done, info
def mini_step(self, action):
self.action_plot = action
self._set_action(action)
observation = self._get_obs()
done = self._is_done(observation)
if self.count >= self.limit_step:
done = True
reward = self._compute_reward(observation)
self.last_obs = observation
return observation, reward, done, {}
def reset(self):
self.init_terrain(2)
self.init_env_from_list(random.randint(0, 0))
if self.evasion_mode:
self.init_evader_route(random.randint(0, 2))
else:
self.init_evader_circle(random.randint(2, 2))
# self.init_env_from_list(3, 1)
self.pursuitor_model.set_init_state(self.init_x, self.init_y,
self.init_yaw)
self.evader_model.set_init_state(self.target_init_x,
self.target_init_y,
self.target_init_yaw)
self.count = 0
# trajectory
self.traj = np.array(self.get_state())
obs = self._get_obs()
self.last_obs = obs
self.last_pur_state = self.get_state()
self.last_tar_state = self.get_goal()
# record
self.pursuit_strategy = []
self.traj_pursuitor = []
self.traj_pursuitor.append(self.get_state())
self.traj_evader = []
self.traj_evader.append(self.get_goal())
# intelligent evasion
self.keyboard_u = [0.0, 0.0]
return obs
def close(self):
pass
def render(self, mode='human'):
plt.cla()
x = self.get_state()
goal = self.get_goal()
ob = self.ob_list
plt.plot(x[0], x[1], "xr")
plt.plot(goal[0], goal[1], "xb")
if ob is not None:
plt.plot(ob[:, 0], ob[:, 1], "ok")
plot_arrow(x[0], x[1], x[2])
# # plot laser
# T_robot = self.get_TF()
# points = self.scan_to_points(self.last_obs[:self.pursuitor_model.laser_num], self.pursuitor_model.laser_min_angle,
# self.pursuitor_model.laser_increment_angle, T_robot)
# if len(points)>0:
# plt.plot(points[:,0], points[:,1], ".m")
plt.axis("equal")
plt.grid(True)
plt.pause(0.0001)
# print(self.last_obs)
return np.array([[[1, 1, 1]]], dtype=np.uint8)
def scan_to_points(self, laser_ranges, min_angle, increment_angle,
T_robot):
laser_points = []
for laser_index in range(len(laser_ranges)):
laser_range = laser_ranges[laser_index]
laser_angle = increment_angle * laser_index + min_angle
# only plot reflected
if laser_range < self.pursuitor_model.laser_max_range - 2:
laser_points.append([
laser_range * cos(laser_angle),
laser_range * sin(laser_angle)
])
laser_points = np.array(laser_points)
for i, laser_point in enumerate(laser_points):
laser_point_tmp = np.matmul(T_robot, np.hstack((laser_point, 1)))
laser_points[i] = laser_point_tmp[:2]
return laser_points
def _get_obs(self):
"""Returns the observation.
"""
# laser_ranges = self.pursuitor_model.get_laser_scan(self.ob_list, self.ob_radius)
percept_map = self.sample_map()
percept_map = np.reshape(percept_map, (1, -1))[0].tolist()
goal = self.get_goal()
state = self.get_state()
goal_angle = atan2(goal[1] - state[1], goal[0] - state[0]) - state[2]
goal_angle = self.normlize_angle(goal_angle)
goal_distance = np.linalg.norm(goal[:2] - state[:2])
return np.array(state[3:5].tolist() + [goal_angle, goal_distance])
def sample_map(self):
"""
“exteroceptive information”, sample the percept region.
:return: sampling points of percept region, with value representing occupancy (0 is non-free, 255 is free)
"""
if self.ob_list is None:
return np.ones([self.sample_map_width, self.sample_map_height])
# represent obstacle in robot coordinate system
T_robot = self.get_TF()
ob_r_list = [
np.matmul(T_robot.T, np.hstack((np.array(ob), 1)))[:2]
for ob in self.ob_list
]
# filter obstacle out of percept region
def filter_ob(obstacle_radius, ob_list, percept_region):
percept_region_expanded = percept_region + np.array([
-obstacle_radius, obstacle_radius, -obstacle_radius,
obstacle_radius
])
def is_in_percept_region(ob):
check_ob_center_in = percept_region_expanded[0] <= ob[0] <= percept_region_expanded[1] and \
percept_region_expanded[2] <= ob[1] <= percept_region_expanded[3]
return check_ob_center_in
filtered_ob_list = list(filter(is_in_percept_region, ob_list))
return filtered_ob_list
filtered_ob_list = filter_ob(self.ob_radius, ob_r_list,
self.percept_region)
if len(filtered_ob_list) is 0:
return np.ones([self.sample_map_width, self.sample_map_height])
sampled_map = np.zeros([self.sample_map_width, self.sample_map_height])
for i in range(self.sample_map_width):
for j in range(self.sample_map_height):
sample_point = self.percept_sample_map[i][j]
dis2ob = np.linalg.norm(np.array(filtered_ob_list) -
sample_point,
axis=1,
keepdims=True)
if np.any(dis2ob <= self.ob_radius):
sampled_map[i, j] = 0
else:
sampled_map[i, j] = 1
return sampled_map
def _set_action(self, action):
"""Applies the given action to the simulation.
"""
u = self.get_state()[3:5]
u[0] = self.max_v
u[1] = (action[0] - (-1)) / 2 * (self.max_w + self.max_w) - self.max_w
# use for differential guidence law
self.last_pur_state = self.get_state()
self.last_tar_state = self.get_goal()
# u=[0,0]
self.pursuitor_model.motion(u, self.dt)
if self.evasion_mode:
# if reach this point
if np.linalg.norm(
self.evasion_route[self.evasion_route_index, :2] -
self.get_goal()[:2]) <= self.robot_radius * 2:
self.evasion_route_index = (self.evasion_route_index +
1) % len(self.evasion_route)
target_u, _ = dwa_control(
self.get_goal(),
self.get_goal()[3:5], self.config_evasion,
self.evasion_route[self.evasion_route_index, :2], self.ob_list)
self.evader_model.motion((target_u), self.dt)
else:
self.evader_model.motion(self.target_u, self.dt)
assert not self.robot_collision_with_obstacle(
self.evader_model.state[:2], self.target_radius, self.ob_list,
self.ob_radius)
x = self.get_state()
self.traj = np.vstack((self.traj, x)) # store state history
self.traj_pursuitor.append(self.get_state())
self.pursuit_strategy.append(action)
self.traj_evader.append(self.get_goal())
def _compute_reward(self, observation):
# reward = ((-observation[-1] + self.last_obs[-1])/(self.max_v*self.dt)) ** 3 * 10
reward = -observation[-1] / 30.0 + -abs(observation[-2]) / 15.0
if self.collision:
reward = -150
if self.catch:
reward = 200
return reward
def _is_done(self, observations):
self.catch = False
self.collision = False
if observations[-1] <= self.robot_radius + self.target_radius:
self.catch = True
#
# laser_collision = np.array(observations[:self.pursuitor_model.laser_num]) <= self.robot_radius
# if laser_collision.any():
# self.collision = True
if self.robot_collision_with_obstacle(self.get_state()[:2],
self.robot_radius, self.ob_list,
self.ob_radius):
self.collision = True
return self.catch or self.collision
def why_done(self):
if self.catch:
return 0
elif self.collision:
return 1
elif self.record_count >= self.limit_step:
return 2
return False
def robot_collision_with_obstacle(self, x, x_radius, ob_list, ob_radius):
if ob_list is None:
return False
for ob in ob_list:
if np.linalg.norm(x - ob) <= x_radius + ob_radius:
return True
return False
def get_goal(self):
return np.array(self.evader_model.state)
def get_state(self):
return np.array(self.pursuitor_model.state)
def normlize_angle(self, angle):
norm_angle = angle % 2 * math.pi
if norm_angle > math.pi:
norm_angle -= 2 * math.pi
return norm_angle
def get_TF(self):
theta = self.get_state()[2]
transition = self.get_state()[:2]
T_robot = np.array([[cos(theta), -sin(theta), transition[0]],
[sin(theta), cos(theta), transition[1]], [0, 0,
1]])
return T_robot
def init_terrain(self, num):
if num == 0:
self.ob_list = np.array([[0, 2], [4.0, 2.0], [-2.0, 4.6],
[5.0, 6.0], [7.0, 9.0], [12.0, 12.0],
[4.0, 12.0]])
elif num == 1:
self.ob_list = np.array([[0, 2], [4.0, 2.0], [-2.0, 4.6],
[5.0, 6.0], [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], [4.42, 10.76],
[-3.76, 8.8], [2.0, -1.8], [-0.16, -1.66],
[3.1, -5.1], [0.7, 6.5], [4.85, -3.05],
[8.0, -0.33], [-0.3, -6.75]])
elif num == 2:
self.ob_list = None
def init_env_from_list(self, pursuit_init_num):
if pursuit_init_num == 0:
# pursuit env 0
self.init_x = 2.1
self.init_y = 7.5
self.init_yaw = 0.0
elif pursuit_init_num == 1:
# pursuit env 1
self.init_x = 8.0
self.init_y = 6.0
self.init_yaw = 0.0
elif pursuit_init_num == 2:
# pursuit env 2
self.init_x = 12.0
self.init_y = 8.0
self.init_yaw = 0.0
elif pursuit_init_num == 3:
# pursuit env 3
self.init_x = -3.0
self.init_y = 0.0
self.init_yaw = pi / 8.0
def init_evader_circle(self, evader_init_num):
if evader_init_num == 0:
# evader env 1
self.target_init_x = 0.0
self.target_init_y = -10.0
self.target_init_yaw = -pi / 2.0
self.target_u = np.array([0.0, -pi / 5 * 2.0])
elif evader_init_num == 1:
# evader env 2
self.target_init_x = 0.0
self.target_init_y = 9.0
self.target_init_yaw = pi - 0.3
self.target_u = np.array([0.0, 0.66])
elif evader_init_num == 2:
# evader env 4
self.target_init_x = 10.0
self.target_init_y = 12.0
self.target_init_yaw = -pi / 2.0
self.target_u = np.array([0.0, -pi / 5])
def init_evader_route(self, evader_init_num):
if evader_init_num == 0:
self.evasion_route = np.array([[4.0, 3.5], [6.0, 2.7], [7.55, 6.8],
[9.57, 11.2], [9.3, 16.5],
[5.44, 16.0], [5.44, 13.23],
[5.44, 9.24], [0.0, 4.5],
[-2.2, -1.1], [0.0, -5.0]])
elif evader_init_num == 1:
self.evasion_route = np.array([[11.64, 8.78], [7.87, 10.94],
[5.49, 12.91], [2.78, 13.79],
[0.99, 10.71], [2.46, 3.27],
[6.91, 2.53]])
elif evader_init_num == 2:
self.evasion_route = np.array([
[1.16, 8.15],
[7.4, 5.63],
[8.84, 0.78],
[14.06, 11.86],
[13.68, 17.4],
])
self.evasion_route_index = 0
self.target_init_x = self.evasion_route[0, 0]
self.target_init_y = self.evasion_route[0, 1]
self.target_init_yaw = math.atan2(
self.evasion_route[1, 1] - self.evasion_route[0, 1],
self.evasion_route[1, 0] - self.evasion_route[0, 0])
class PursuitEnv2(gym.Env):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': 50
}
def __init__(self):
self.max_v = 2
self.min_v = -0.3
self.max_w = pi / 3.0 * 2
self.max_acc_v = 0.7
self.max_acc_w = pi / 3.0 * 5
self.init_x = 0.0
self.init_y = 6.0
self.init_yaw = pi / 8.0
self.robot_radius = 0.3
self.ob_radius = 0.3
self.target_radius = 0.3
self.dt = 0.1
self.target_init_x = 10
self.target_init_y = 4
self.target_init_yaw = pi / 2.0
self.target_u = None
self.pursuitor_model = RobotModel(self.max_v, self.min_v, self.max_w,
self.max_acc_v, self.max_acc_w,
self.init_x, self.init_y,
self.init_yaw)
self.evader_model = RobotModel(self.max_v, self.min_v, self.max_w,
self.max_acc_v, self.max_acc_w,
self.target_init_x, self.target_init_y,
self.target_init_yaw)
self.config_dwa = ConfigDWA(self.max_v,
self.min_v,
self.max_w,
self.max_acc_v,
self.max_acc_w,
robot_raduis=self.robot_radius,
obstacle_radius=self.ob_radius,
dt=self.dt)
self.confg_apf = ConfigAPF(self.max_v,
self.min_v,
self.max_w,
self.max_acc_v,
self.max_acc_w,
obstacle_radius=self.ob_radius)
self.confg_pn = ConfigPN()
"""env parameters need reset
"""
self.ob_list = np.array([[0, 2], [4.0, 2.0], [5.0, 4.0], [5.0, 6.0],
[7.0, 9.0], [12.0, 12.0]])
self.last_obs = None
self.last_pur_state = None
self.last_tar_state = None
# trajectory
self.traj = np.array(self.get_state())
# state done
self.collision = False
self.catch = False
# actions
# self.v_reso = 0.1
# self.w_reso = pi/18.0
#
#
# self.action_table = []
# for v in np.arange(self.min_v, self.max_v+self.v_reso, self.v_reso):
# for w in np.arange(-self.max_w, self.max_w+self.w_reso, self.w_reso):
# self.action_table.append(np.array([v, w]))
# self.action_num = len(self.action_table)
self.action_num = 2
self.action_space = spaces.Discrete(self.action_num)
# observations = image
self.screen_height = 600 # pixel
self.screen_width = 600 # pixel
self.state_height = 150 # pixel
self.state_width = 150 # pixel
self.world_width = 20.0 # m
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.state_height,
self.state_width, 3),
dtype=np.uint8)
# seed
self.np_random = None
self.seed()
# time limit
self.limit_step = 300
self.count = 0
self.action_plot = 0
self.viewer = None
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def step(self, action):
assert self.action_space.contains(
action), "%r (%s) invalid" % (action, type(action))
self.action_plot = action
self._set_action(action)
observation = self._get_obs()
done = self._is_done(observation)
if self.count >= self.limit_step:
done = True
reward = self._compute_reward(observation)
self.last_obs = observation
self.count += 1
return observation, reward, done, {}
def reset(self):
self.init_env_from_list(random.randint(0, 3), random.randint(1, 2))
# self.init_env_from_list(3, 1)
self.pursuitor_model.set_init_state(self.init_x, self.init_y,
self.init_yaw)
self.evader_model.set_init_state(self.target_init_x,
self.target_init_y,
self.target_init_yaw)
self.count = 0
# trajectory
self.traj = np.array(self.get_state())
obs = self._get_obs()
self.last_obs = obs
self.last_pur_state = self.get_state()
self.last_tar_state = self.get_goal()
return obs
def close(self):
if self.viewer:
self.viewer.close()
self.viewer = None
def render(self, mode='human'):
# render image
scale = self.screen_width / self.world_width
origin = [self.screen_width / 10, self.screen_height / 10]
if self.viewer is None:
from gym.envs.classic_control import rendering
self.viewer = rendering.Viewer(self.screen_width,
self.screen_height)
# plot robot with orientation
pursuit_radius = int(self.robot_radius * scale)
pursuit = rendering.make_polygon(
[(-pursuit_radius, pursuit_radius), (0, pursuit_radius),
(pursuit_radius, 0), (0, -pursuit_radius),
(-pursuit_radius, -pursuit_radius)],
filled=True)
pursuit.set_color(.8, .6, .4)
self.pursuit_trans = rendering.Transform()
pursuit.add_attr(self.pursuit_trans)
self.viewer.add_geom(pursuit)
# plot target with orientation
evader_radius = int(self.robot_radius * scale)
evader = rendering.make_polygon([(-evader_radius, evader_radius),
(0, evader_radius),
(evader_radius, 0),
(0, -evader_radius),
(-evader_radius, -evader_radius)])
evader.set_color(.5, .5, .8)
self.evader_trans = rendering.Transform()
evader.add_attr(self.evader_trans)
self.viewer.add_geom(evader)
# plot obstacles
for ob in self.ob_list:
ob_radius = self.ob_radius * scale
obstacle = rendering.make_circle(ob_radius)
obstacle.set_color(0, 0, 0)
obstacle.add_attr(
rendering.Transform(translation=(ob[0] * scale + origin[0],
ob[1] * scale +
origin[1])))
self.viewer.add_geom(obstacle)
self.pursuit_trans.set_translation(
self.get_state()[0] * scale + origin[0],
self.get_state()[1] * scale + origin[1])
self.pursuit_trans.set_rotation(self.get_state()[2])
self.evader_trans.set_translation(
self.get_goal()[0] * scale + origin[0],
self.get_goal()[1] * scale + origin[1])
self.evader_trans.set_rotation(self.get_goal()[2])
return self.viewer.render(return_rgb_array=mode == 'rgb_array')
def scan_to_points(self, laser_ranges, min_angle, increment_angle,
T_robot):
laser_points = []
for laser_index in range(len(laser_ranges)):
laser_range = laser_ranges[laser_index]
laser_angle = increment_angle * laser_index + min_angle
# only plot reflected
if laser_range < self.pursuitor_model.laser_max_range - 2:
laser_points.append([
laser_range * cos(laser_angle),
laser_range * sin(laser_angle)
])
laser_points = np.array(laser_points)
for i, laser_point in enumerate(laser_points):
laser_point_tmp = np.matmul(T_robot, np.hstack((laser_point, 1)))
laser_points[i] = laser_point_tmp[:2]
return laser_points
def _get_obs(self):
"""Returns the observation.
"""
arr = self.render(mode='rgb_array')
arr = imresize(arr, (self.state_height, self.state_width, 3))
return arr
def sample_map(self):
"""
“exteroceptive information”, sample the percept region.
:return: sampling points of percept region, with value representing occupancy (0 is non-free, 255 is free)
"""
# represent obstacle in robot coordinate system
T_robot = self.get_TF()
ob_r_list = [
np.matmul(T_robot.T, np.hstack((np.array(ob), 1)))[:2]
for ob in self.ob_list
]
# filter obstacle out of percept region
def filter_ob(obstacle_radius, ob_list, percept_region):
percept_region_expanded = percept_region + np.array([
-obstacle_radius, obstacle_radius, -obstacle_radius,
obstacle_radius
])
def is_in_percept_region(ob):
check_ob_center_in = percept_region_expanded[0] <= ob[0] <= percept_region_expanded[1] and \
percept_region_expanded[2] <= ob[1] <= percept_region_expanded[3]
return check_ob_center_in
filtered_ob_list = list(filter(is_in_percept_region, ob_list))
return filtered_ob_list
filtered_ob_list = filter_ob(self.ob_radius, ob_r_list,
self.percept_region)
sampled_map = np.zeros([self.sample_map_width, self.sample_map_height])
for i in range(self.sample_map_width):
for j in range(self.sample_map_height):
sample_point = self.percept_sample_map[i][j]
dis2ob = np.linalg.norm(np.array(filtered_ob_list) -
sample_point,
axis=1,
keepdims=True)
if np.any(dis2ob <= self.ob_radius):
sampled_map[i, j] = 0
else:
sampled_map[i, j] = 255
return sampled_map
def _set_action(self, action):
"""Applies the given action to the simulation.
"""
u = [0, 0]
if action == 0:
u, ltraj = dwa_control(self.get_state(),
np.array(self.pursuitor_model.state[3:5]),
self.config_dwa,
self.get_goal()[:2], self.ob_list)
elif action == 1:
# u = apf_control(self.confg_apf, self.pursuitor_model.state, self.evader_model.state, self.ob_list)
#
# # get angle velocity from angle input
# u[1] = self.pursuitor_model.rot_to_angle(u[1])
w = pn_control(self.confg_pn, self.get_state(), self.get_goal(),
self.last_pur_state, self.last_tar_state)
u = [self.max_v, w]
self.last_pur_state = self.get_state()
self.last_tar_state = self.get_goal()
self.pursuitor_model.motion(u, self.dt)
self.evader_model.motion(self.target_u, self.dt)
assert not self.robot_collision_with_obstacle(
self.evader_model.state[:2], self.target_radius, self.ob_list,
self.ob_radius)
x = self.get_state()
self.traj = np.vstack((self.traj, x)) # store state history
def _compute_reward(self, observation):
# reward = ((-observation[-1] + self.last_obs[-1])/(self.max_v*self.dt)) ** 3 * 10
reward = -observation[-1] / 10.0
if self.collision:
reward = -150
if self.catch:
reward = 200
return reward
def _is_done(self, observations):
self.catch = False
self.collision = False
if np.linalg.norm(self.get_goal()[:2] - self.get_state()[:2]
) <= self.robot_radius + self.target_radius:
self.catch = True
#
# laser_collision = np.array(observations[:self.pursuitor_model.laser_num]) <= self.robot_radius
# if laser_collision.any():
# self.collision = True
if self.robot_collision_with_obstacle(self.get_state()[:2],
self.robot_radius, self.ob_list,
self.ob_radius):
self.collision = True
return self.catch or self.collision
def robot_collision_with_obstacle(self, x, x_radius, ob_list, ob_radius):
for ob in ob_list:
if np.linalg.norm(x - ob) <= x_radius + ob_radius:
return True
return False
def get_goal(self):
return np.array(self.evader_model.state)
def get_state(self):
return np.array(self.pursuitor_model.state)
def normlize_angle(self, angle):
norm_angle = angle % 2 * math.pi
if norm_angle > math.pi:
norm_angle -= 2 * math.pi
return norm_angle
def get_TF(self):
theta = self.get_state()[2]
transition = self.get_state()[:2]
T_robot = np.array([[cos(theta), -sin(theta), transition[0]],
[sin(theta), cos(theta), transition[1]], [0, 0,
1]])
return T_robot
def init_env_from_list(self, pursuit_init_num, evader_init_num):
if pursuit_init_num == 0:
# pursuit env 0
self.init_x = 0.0
self.init_y = 6.0
self.init_yaw = 0.0
elif pursuit_init_num == 1:
# pursuit env 1
self.init_x = 8.0
self.init_y = 6.0
self.init_yaw = 0.0
elif pursuit_init_num == 2:
# pursuit env 2
self.init_x = 12.0
self.init_y = 8.0
self.init_yaw = 0.0
elif pursuit_init_num == 3:
# pursuit env 3
self.init_x = 0.0
self.init_y = 0.0
self.init_yaw = pi / 8.0
# if evader_init_num==0:
# # evader env 0
# self.target_init_x = 2.0
# self.target_init_y = 11.0
# self.target_init_yaw = -pi/2.0
# self.target_u = np.array([1.8, 0.0])
if evader_init_num == 1:
# evader env 1
self.target_init_x = 0.0
self.target_init_y = 10.0
self.target_init_yaw = -pi / 2.0
self.target_u = np.array([1.8, -pi / 5 * 2.0])
elif evader_init_num == 2:
# evader env 2
self.target_init_x = 0.0
self.target_init_y = 9.0
self.target_init_yaw = pi - 0.3
self.target_u = np.array([1.8, 0.66])
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]])
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([1.0, 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)
def main(gx=8, gy=2):
show_animation = True
print(__file__ + " start!!")
max_v = 2
min_v = 0
max_w = pi / 3 * 2
max_acc_v = 0.7
max_acc_w = pi / 3.0 * 5
init_x = 0.0
init_y = 6
init_yaw = pi / 8.0
robot_radius = 0.3
ob_radius = 0.3
goal_radius = 0.3
pursuitor_model = RobotModel(max_v, min_v, max_w, max_acc_v, max_acc_w,
init_x, init_y, init_yaw)
target_model = RobotModel(max_v, min_v, max_w, max_acc_v, max_acc_w, gx,
gy, -pi / 2.0)
# obstacles [x(m) y(m), ....]
ob = np.array([[0, 2], [4.0, 2.0], [5.0, 4.0], [5.0, 6.0], [7.0, 9.0]])
config = ConfigAPF(2, 0, math.pi / 3, 0.7, math.pi / 3)
traj = np.array(pursuitor_model.state)
traj_u = np.array([0, 0])
traj_target = np.array(target_model.state)
for i in range(2000):
logging.info("")
logging.info(i)
u = apf_control(config, pursuitor_model.state, target_model.state, ob)
traj_u = np.vstack((traj_u, u))
print(u)
u[1] = pursuitor_model.rot_to_angle(u[1])
# u[0]=0
x = pursuitor_model.motion(u, config.dt)
traj = np.vstack((traj, x)) # store state history
goal = target_model.motion([1.8, pi / 5], config.dt)
traj_target = np.vstack((traj_target, goal))
# print(traj)
if show_animation:
plt.cla()
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) <= (pursuitor_model.robot_radius +
target_model.robot_radius):
print("Goal!!")
break
# check collision
collision = False
for obstacle in ob:
if math.sqrt((x[0] - obstacle[0])**2 +
(x[1] - obstacle[1])**2) <= (
pursuitor_model.robot_radius +
config.obstacle_radius):
collision = True
if collision:
print("Collision!")
break
prefix = "apf_flaw/4_"
np.save(prefix + "traj.npy", traj)
np.save(prefix + "traj_target.npy", traj_target)
np.save(prefix + "traj_u.npy", traj_u)
print("Done")
if show_animation:
plt.plot(traj[:, 0], traj[:, 1], "-r")
plt.plot(traj_target[:, 0], traj_target[:, 1], "-g")
fig = plt.gcf()
# fig.set_size_inches(6,6)
plt.xlabel('X(m)')
plt.ylabel('Y(m)')
# plt.axis("auto")
plt.tight_layout()
print(plt.xlim((-1.488585497312541, 14.14373130420207)))
print(plt.ylim((0.4404926013282211, 12.089089959876205)))
plt.figure()
plt.plot(np.arange(0, len(traj)) * config.dt,
traj_u[:, 1],
label=r'Input $\theta$')
plt.plot(np.arange(0, len(traj)) * config.dt,
traj[:, 2],
label=r'Actual $\theta$')
plt.xlabel('T(s)')
plt.ylabel(r'$\theta$(rad)')
plt.legend()
plt.show()
# ax.add_patch(Rectangle(xy=(-10-1, -10-1), width=30.0+2, height=30.0+2, color="black"))
# ax.add_patch(Rectangle(xy=(-10, -10), width=30.0, height=30.0, color="white"))
for o in ob:
ax.add_patch(Circle(xy=(o[0], o[1]), radius=ob_radius, color="black"))
# ax.add_patch(Circle(xy=(-5.0, -5.0), radius=3.0, color="black"))
# ax.add_patch(Circle(xy=(15.0, -4.0), radius=2.0, color="black"))
for i in range(3):
target_init_x, target_init_y, target_init_yaw, target_u = evader_traj(i)
traj_evader = []
evader_model.set_init_state(target_init_x, target_init_y, target_init_yaw)
traj_evader.append(np.array(evader_model.state))
for steps in range(120):
evader_model.motion(target_u, dt)
traj_evader.append(np.array(evader_model.state))
traj_evader = np.array(traj_evader)
plt.plot(traj_evader[:, 0], traj_evader[:, 1], "-g")
np.save('npy_for_fig/course/simple_target_'+str(i), traj_evader)
# np.save('npy_for_fig/course/hard_target_' + str(i), traj_evader)
robot = []
for i in range(4):
init_x, init_y, init_yaw = pursuit_position(i)
robot.append([init_x, init_y, init_yaw])
plt.plot(init_x, init_y, "xr")
plt.xlabel('X(m)')
plt.ylabel('Y(m)')
plt.tight_layout()
np.save('npy_for_fig/course/simple_ob', ob)
