class MarkAgent(Agent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.route_file_path = Path(self.agent_settings.waypoint_file_path)
self.pid_controller = PIDController(agent=self,
steering_boundary=(-1, 1), throttle_boundary=(0, 1))
self.mission_planner = WaypointFollowingMissionPlanner(agent=self)
# initiated right after mission plan
self.behavior_planner = BehaviorPlanner(agent=self)
self.local_planner = DynamicWindowsApproach(
agent=self,
controller=self.pid_controller)
occu_map_file_path = Path("./ROAR_Sim/data/easy_map_cleaned_global_occu_map.npy")
self.occupancy_map = OccupancyGridMap(absolute_maximum_map_size=550,
world_coord_resolution=1,
occu_prob=0.99,
max_points_to_convert=5000,
threaded=True)
self.occupancy_map.load_from_file(file_path=occu_map_file_path)
# self.obstacle_from_depth_detector = ObstacleFromDepth(agent=self,
# threaded=True,
# max_detectable_distance=0.3,
# max_points_to_convert=10000,
# min_obstacle_height=2)
# self.add_threaded_module(self.obstacle_from_depth_detector)
# self.add_threaded_module(self.occupancy_map)
def run_step(self, sensors_data: SensorsData, vehicle: Vehicle) -> VehicleControl:
super(MarkAgent, self).run_step(vehicle=vehicle,
sensors_data=sensors_data)
control = self.local_planner.run_in_series()
option = "obstacle_coords" # ground_coords, point_cloud_obstacle_from_depth
if self.kwargs.get(option, None) is not None:
points = self.kwargs[option]
self.occupancy_map.update_async(points)
self.occupancy_map.visualize()
self.occupancy_map.get_map()
return control
class RLLocalPlannerAgent(Agent):
def __init__(self, target_speed=40, **kwargs):
super().__init__(**kwargs)
self.target_speed = target_speed
self.logger = logging.getLogger("PID Agent")
self.route_file_path = Path(self.agent_settings.waypoint_file_path)
self.pid_controller = PIDController(agent=self,
steering_boundary=(-1, 1),
throttle_boundary=(0, 1))
self.mission_planner = WaypointFollowingMissionPlanner(agent=self)
# initiated right after mission plan
self.behavior_planner = BehaviorPlanner(agent=self)
self.local_planner = RLLocalPlanner(agent=self,
controller=self.pid_controller)
self.traditional_local_planner = SimpleWaypointFollowingLocalPlanner(
agent=self,
controller=self.pid_controller,
mission_planner=self.mission_planner,
behavior_planner=self.behavior_planner,
closeness_threshold=1.5)
self.absolute_maximum_map_size, self.map_padding = 1000, 40
self.occupancy_map = OccupancyGridMap(agent=self, threaded=True)
self.obstacle_from_depth_detector = ObstacleFromDepth(agent=self,
threaded=True)
self.add_threaded_module(self.obstacle_from_depth_detector)
# self.add_threaded_module(self.occupancy_map)
self.logger.debug(f"Waypoint Following Agent Initiated. Reading f"
f"rom {self.route_file_path.as_posix()}")
def run_step(self, vehicle: Vehicle,
sensors_data: SensorsData) -> VehicleControl:
super(RLLocalPlannerAgent, self).run_step(vehicle=vehicle,
sensors_data=sensors_data)
self.traditional_local_planner.run_in_series()
self.transform_history.append(self.vehicle.transform)
option = "obstacle_coords" # ground_coords, point_cloud_obstacle_from_depth
if self.kwargs.get(option, None) is not None:
points = self.kwargs[option]
self.occupancy_map.update(points)
control = self.local_planner.run_in_series()
return control
def get_obs(self):
ch1 = self.occupancy_map.get_map(transform=self.vehicle.transform,
view_size=(100, 100))
ch1 = np.expand_dims((ch1 * 255).astype(np.uint8), -1)
ch2 = np.zeros(shape=(100, 100, 1))
ch3 = np.zeros(shape=ch2.shape)
obs = np.concatenate([ch1, ch2, ch3], axis=2)
print(np.shape(obs))
return obs
class OccupancyMapAgent(Agent):
def __init__(self, vehicle: Vehicle, agent_settings: AgentConfig, **kwargs):
super().__init__(vehicle, agent_settings, **kwargs)
self.route_file_path = Path(self.agent_settings.waypoint_file_path)
self.pid_controller = PIDController(agent=self, steering_boundary=(-1, 1), throttle_boundary=(0, 1))
self.mission_planner = WaypointFollowingMissionPlanner(agent=self)
# initiated right after mission plan
self.behavior_planner = BehaviorPlanner(agent=self)
self.local_planner = SimpleWaypointFollowingLocalPlanner(
agent=self,
controller=self.pid_controller,
mission_planner=self.mission_planner,
behavior_planner=self.behavior_planner,
closeness_threshold=1.5
)
self.occupancy_map = OccupancyGridMap(absolute_maximum_map_size=800,
world_coord_resolution=1,
occu_prob=0.99,
max_points_to_convert=10000,
threaded=True)
self.obstacle_from_depth_detector = ObstacleFromDepth(agent=self,
threaded=True,
max_detectable_distance=0.5,
max_points_to_convert=20000,
min_obstacle_height=2)
self.add_threaded_module(self.obstacle_from_depth_detector)
self.add_threaded_module(self.occupancy_map)
# self.vis = o3d.visualization.Visualizer()
# self.vis.create_window(width=500, height=500)
# self.pcd = o3d.geometry.PointCloud()
# self.points_added = False
def run_step(self, sensors_data: SensorsData, vehicle: Vehicle) -> VehicleControl:
super().run_step(sensors_data=sensors_data, vehicle=vehicle)
control = self.local_planner.run_in_series()
option = "obstacle_coords" # ground_coords, point_cloud_obstacle_from_depth
if self.kwargs.get(option, None) is not None:
points = self.kwargs[option]
self.occupancy_map.update_async(points)
arb_points = [self.local_planner.way_points_queue[0].location]
m = self.occupancy_map.get_map(transform=self.vehicle.transform,
view_size=(200, 200),
vehicle_value=-1,
arbitrary_locations=arb_points,
arbitrary_point_value=-5)
# print(np.where(m == -5))
# cv2.imshow("m", m)
# cv2.waitKey(1)
# occu_map_vehicle_center = np.array(list(zip(*np.where(m == np.min(m))))[0])
# correct_next_waypoint_world = self.local_planner.way_points_queue[0]
# diff = np.array([correct_next_waypoint_world.location.x,
# correct_next_waypoint_world.location.z]) - \
# np.array([self.vehicle.transform.location.x,
# self.vehicle.transform.location.z])
# correct_next_waypoint_occu = occu_map_vehicle_center + diff
# correct_next_waypoint_occu = np.array([49.97, 44.72596359])
# estimated_world_coord = self.occupancy_map.cropped_occu_to_world(
# cropped_occu_coord=correct_next_waypoint_occu, vehicle_transform=self.vehicle.transform,
# occu_vehicle_center=occu_map_vehicle_center)
# print(f"correct o-> {correct_next_waypoint_occu}"
# f"correct w-> {correct_next_waypoint_world.location} | "
# f"estimated = {estimated_world_coord.location.x}")
# cv2.imshow("m", m)
# cv2.waitKey(1)
# print()
# if self.points_added is False:
# self.pcd = o3d.geometry.PointCloud()
# point_means = np.mean(points, axis=0)
# self.pcd.points = o3d.utility.Vector3dVector(points - point_means)
# self.vis.add_geometry(self.pcd)
# self.vis.poll_events()
# self.vis.update_renderer()
# self.points_added = True
# else:
# point_means = np.mean(points, axis=0)
# self.pcd.points = o3d.utility.Vector3dVector(points - point_means)
# self.vis.update_geometry(self.pcd)
# self.vis.poll_events()
# self.vis.update_renderer()
if self.local_planner.is_done():
self.mission_planner.restart()
self.local_planner.restart()
return control