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=550,
world_coord_resolution=1,
occu_prob=0.99,
max_points_to_convert=5000)
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.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:
# print("curr_transform", self.vehicle.transform)
points = self.kwargs[option]
self.occupancy_map.update(points)
self.occupancy_map.visualize()
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()
return control
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)
self.occupancy_map = OccupancyGridMap(absolute_maximum_map_size=2000,
world_coord_resolution=10,
occu_prob=0.9) # 1 m = 100 cm
self.add_threaded_module(
DepthToPointCloudDetector(agent=self,
should_compute_global_pointcloud=True,
threaded=True,
scale_factor=1000))
# self.gpd = GroundPlaneDetector(self, threaded=True)
# self.add_threaded_module(self.gpd)
self.obstacle_detector = ObstacleDetector(self, threaded=True)
self.add_threaded_module(self.obstacle_detector)
# 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()
if self.kwargs.get("obstacle_coords", None) is not None:
points = self.kwargs["obstacle_coords"]
self.occupancy_map.update(points)
self.occupancy_map.visualize(self.vehicle.transform.location)
# print(self.vehicle.transform)
# cv2.imshow("mask", self.obstacle_detector.curr_mask)
# cv2.waitKey(1)
# # self.occupancy_map.visualize()
# if self.points_added is False:
# self.pcd = o3d.geometry.PointCloud()
# self.pcd.points = o3d.utility.Vector3dVector(grounds)
# self.vis.add_geometry(self.pcd)
# self.vis.poll_events()
# self.vis.update_renderer()
# self.points_added = True
# else:
# self.pcd.points = o3d.utility.Vector3dVector(grounds)
# self.vis.update_geometry(self.pcd)
# self.vis.poll_events()
# self.vis.update_renderer()
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(
absolute_maximum_map_size=self.absolute_maximum_map_size,
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.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)
if self.is_done: # will never enter here
control = VehicleControl()
self.logger.debug("Path Following Agent is Done. Idling.")
else:
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
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 FreeSpaceAutoAgent(Agent):
def __init__(self, vehicle: Vehicle, agent_settings: AgentConfig,
**kwargs):
super().__init__(vehicle, agent_settings, **kwargs)
# initialize occupancy grid map content
self.occu_map = OccupancyGridMap(agent=self)
self.depth_to_pcd = DepthToPointCloudDetector(agent=self)
self.ground_plane_detector = GroundPlaneDetector(agent=self)
self.lane_detector = LaneDetector(agent=self)
# initialize open3d related content
self.vis = o3d.visualization.Visualizer()
self.vis.create_window(width=500, height=500)
self.pcd = o3d.geometry.PointCloud()
self.coordinate_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(
)
self.points_added = False
def run_step(self, sensors_data: SensorsData,
vehicle: Vehicle) -> VehicleControl:
super(FreeSpaceAutoAgent, self).run_step(sensors_data, vehicle)
if self.front_depth_camera.data is not None and self.front_rgb_camera.data is not None:
pcd = self.depth_to_pcd.run_in_series()
points: np.ndarray = np.asarray(pcd.points)
colors: np.ndarray = np.asarray(pcd.colors)
ground_locs = np.where(points[:, 1] < np.mean(points[:, 1]))
points = points[ground_locs]
colors = colors[ground_locs]
pcd.points = o3d.utility.Vector3dVector(points)
pcd.colors = o3d.utility.Vector3dVector(colors)
# self.logger.info(f"{self.vehicle.transform}")
self.occu_map.update(points)
self.occu_map.visualize()
self.non_blocking_pcd_visualization(pcd=pcd,
should_center=True,
should_show_axis=True,
axis_size=1)
# self.pcd = pcd
return VehicleControl()
def non_blocking_pcd_visualization(self,
pcd: o3d.geometry.PointCloud,
should_center=False,
should_show_axis=False,
axis_size: float = 0.1):
points = np.asarray(pcd.points)
colors = np.asarray(pcd.colors)
if should_center:
points = points - np.mean(points, axis=0)
if self.points_added is False:
self.pcd = o3d.geometry.PointCloud()
self.pcd.points = o3d.utility.Vector3dVector(points)
self.pcd.colors = o3d.utility.Vector3dVector(colors)
if should_show_axis:
self.coordinate_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(
size=axis_size, origin=np.mean(points, axis=0))
self.vis.add_geometry(self.coordinate_frame)
self.vis.add_geometry(self.pcd)
self.points_added = True
else:
# print(np.shape(np.vstack((np.asarray(self.pcd.points), points))))
self.pcd.points = o3d.utility.Vector3dVector(points)
self.pcd.colors = o3d.utility.Vector3dVector(colors)
if should_show_axis:
self.coordinate_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(
size=axis_size, origin=np.mean(points, axis=0))
self.vis.update_geometry(self.coordinate_frame)
self.vis.update_geometry(self.pcd)
self.vis.poll_events()
self.vis.update_renderer()
