Added Gazebo Obstacle Detection

GEMstack/onboard/interface/gem_gazebo.py

@@ -1,3 +1,4 @@
+from GEMstack.state.obstacle import ObstacleMaterialEnum, ObstacleState, ObstacleStateEnum
 from .gem import *
 from ...utils import settings
 import math
@@ -53,7 +54,13 @@ class GNSSReading:
     'car': 'car',
     'vehicle': 'car',
     'truck': 'medium_truck',
-    'large_truck': 'large_truck'
+    'large_truck': 'large_truck',
+    'cone': 'traffic_cone'
+}
+
+# Map model prefixes to obstacle types
+MODEL_PREFIX_TO_OBSTACLE_TYPE = {
+    'cone': 'traffic_cone'
 }
 
 class GEMGazeboInterface(GEMInterface):
@@ -93,11 +100,15 @@ def __init__(self):
         self.model_states_sub = None
         self.tracked_model_prefixes = settings.get('simulator.agent_tracker.model_prefixes', 
                                                  ['pedestrian', 'bicycle', 'car'])
+        self.tracked_obstacle_prefixes = settings.get('simulator.obstacle_tracker.model_prefixes', 
+                                                 ['cone'])
         self.agent_detector_callback = None
+        self.obstacle_detector_callback = None
         self.last_agent_positions = {}
         self.last_agent_velocities = {}
         self.last_model_states_time = 0.0
         self.agent_detection_rate = settings.get('simulator.agent_tracker.rate', 10.0)  # Hz
+        self.obstacle_detection_rate = settings.get('simulator.obstacle_tracker.rate', 50.0)  # Hz
 
         # Frame transformation variables
         self.start_pose_abs = None  # Initial vehicle pose in GLOBAL frame
@@ -141,15 +152,15 @@ def model_states_callback(self, msg: ModelStates):
         current_time = self.time()
 
         # Check if we should process this update (rate limiting)
-        if current_time - self.last_model_states_time < 1.0/self.agent_detection_rate:
+        if ((current_time - self.last_model_states_time < 1.0/self.agent_detection_rate) and (current_time - self.last_model_states_time < 1.0/self.obstacle_detection_rate)):
             return
 
         # Calculate time delta since last update
         dt = current_time - self.last_model_states_time
         self.last_model_states_time = current_time
 
         # Skip if no callback is registered
-        if self.agent_detector_callback is None:
+        if self.agent_detector_callback is None and self.obstacle_detector_callback is None:
             return
 
         # Find vehicle in model states
@@ -203,16 +214,24 @@ def model_states_callback(self, msg: ModelStates):
             agent_type = None
             for prefix in self.tracked_model_prefixes:
                 if model_name.lower().startswith(prefix.lower()):
-                    # Find the appropriate agent type from the prefix
                     for key, value in MODEL_PREFIX_TO_AGENT_TYPE.items():
                         if prefix.lower().startswith(key.lower()):
                             agent_type = value
                             break
                     break
+
+            obstacle_type = None
+            for prefix in self.tracked_obstacle_prefixes:
+                if model_name.lower().startswith(prefix.lower()):
+                    for key, value in MODEL_PREFIX_TO_OBSTACLE_TYPE.items():
+                        if prefix.lower().startswith(key.lower()):
+                            obstacle_type = value
+                            break
+                    break
 
-            if agent_type is None:
+            if agent_type is None and obstacle_type is None:
                 continue  # Not an agent we're tracking
-                
+
             # Get position and orientation from model states
             position = msg.pose[i].position
             orientation = msg.pose[i].orientation
@@ -224,7 +243,6 @@ def model_states_callback(self, msg: ModelStates):
             # Convert orientation quaternion to euler angles
             quaternion = (orientation.x, orientation.y, orientation.z, orientation.w)
             roll, pitch, yaw = euler_from_quaternion(quaternion)
-
             # Create agent pose in ABSOLUTE_CARTESIAN frame (Gazebo's native frame)
             agent_global_pose = ObjectPose(
                 frame=ObjectFrameEnum.ABSOLUTE_CARTESIAN,
@@ -269,61 +287,72 @@ def model_states_callback(self, msg: ModelStates):
                 # If transformation not initialized yet, just use the model_states pose
                 agent_pose = agent_global_pose
 
-            # Calculate velocity manually if twist data is zero or missing
-            velocity = (linear_vel.x, linear_vel.y, linear_vel.z)
-            velocity_is_zero = abs(linear_vel.x) < 1e-6 and abs(linear_vel.y) < 1e-6 and abs(linear_vel.z) < 1e-6
-
-            if velocity_is_zero and model_name in self.last_agent_positions and dt > 0:
-                # Calculate velocity from position difference
-                prev_pos = self.last_agent_positions[model_name]
-                dx = position.x - prev_pos[0]
-                dy = position.y - prev_pos[1]
-                dz = position.z - prev_pos[2]
+            if agent_type is not None:
+                # Calculate velocity manually if twist data is zero or missing
+                velocity = (linear_vel.x, linear_vel.y, linear_vel.z)
+                velocity_is_zero = abs(linear_vel.x) < 1e-6 and abs(linear_vel.y) < 1e-6 and abs(linear_vel.z) < 1e-6
 
-                # Calculate velocity (position change / time)
-                calculated_vel = (dx/dt, dy/dt, dz/dt)
+                if velocity_is_zero and model_name in self.last_agent_positions and dt > 0:
+                    # Calculate velocity from position difference
+                    prev_pos = self.last_agent_positions[model_name]
+                    dx = position.x - prev_pos[0]
+                    dy = position.y - prev_pos[1]
+                    dz = position.z - prev_pos[2]
+
+                    # Calculate velocity (position change / time)
+                    calculated_vel = (dx/dt, dy/dt, dz/dt)
+
+                    # Apply some smoothing with the previous velocity if available
+                    if model_name in self.last_agent_velocities:
+                        prev_vel = self.last_agent_velocities[model_name]
+                        # Apply exponential smoothing (0.7 current + 0.3 previous)
+                        velocity = (
+                            0.7 * calculated_vel[0] + 0.3 * prev_vel[0],
+                            0.7 * calculated_vel[1] + 0.3 * prev_vel[1],
+                            0.7 * calculated_vel[2] + 0.3 * prev_vel[2]
+                        )
+                    else:
+                        velocity = calculated_vel
 
-                # Apply some smoothing with the previous velocity if available
-                if model_name in self.last_agent_velocities:
-                    prev_vel = self.last_agent_velocities[model_name]
-                    # Apply exponential smoothing (0.7 current + 0.3 previous)
-                    velocity = (
-                        0.7 * calculated_vel[0] + 0.3 * prev_vel[0],
-                        0.7 * calculated_vel[1] + 0.3 * prev_vel[1],
-                        0.7 * calculated_vel[2] + 0.3 * prev_vel[2]
-                    )
+                # Determine activity state based on velocity magnitude
+                velocity_magnitude = np.linalg.norm(velocity)
+                if velocity_magnitude < 0.1:
+                    activity = AgentActivityEnum.STOPPED
+                elif velocity_magnitude > 5.0:  # Arbitrary threshold for "fast"
+                    activity = AgentActivityEnum.FAST
                 else:
-                    velocity = calculated_vel
+                    activity = AgentActivityEnum.MOVING
+
+                # Get agent dimensions
+                dimensions = AGENT_DIMENSIONS.get(agent_type, (1.0, 1.0, 1.0))  # Default if unknown
 
-            # Determine activity state based on velocity magnitude
-            velocity_magnitude = np.linalg.norm(velocity)
-            if velocity_magnitude < 0.1:
-                activity = AgentActivityEnum.STOPPED
-            elif velocity_magnitude > 5.0:  # Arbitrary threshold for "fast"
-                activity = AgentActivityEnum.FAST
-            else:
-                activity = AgentActivityEnum.MOVING
+                # Create agent state
+                agent_state = AgentState(
+                    pose=agent_pose,  # Using START frame pose
+                    dimensions=dimensions,
+                    outline=None,
+                    type=getattr(AgentEnum, agent_type.upper()),
+                    activity=activity,
+                    velocity=velocity,
+                    yaw_rate=angular_vel.z
+                )
 
-            # Get agent dimensions
-            dimensions = AGENT_DIMENSIONS.get(agent_type, (1.0, 1.0, 1.0))  # Default if unknown
-
-            # Create agent state
-            agent_state = AgentState(
-                pose=agent_pose,  # Using START frame pose
-                dimensions=dimensions,
-                outline=None,
-                type=getattr(AgentEnum, agent_type.upper()),
-                activity=activity,
-                velocity=velocity,
-                yaw_rate=angular_vel.z
-            )
-
-            # Store current position for next velocity calculation (using raw positions)
-            self.last_agent_positions[model_name] = (position.x, position.y, position.z)
-            self.last_agent_velocities[model_name] = velocity
-
-            # Call the callback with the agent state
-            self.agent_detector_callback(model_name, agent_state)
+                # Store current position for next velocity calculation (using raw positions)
+                self.last_agent_positions[model_name] = (position.x, position.y, position.z)
+                self.last_agent_velocities[model_name] = velocity
+                # Call the callback with the agent state
+                self.agent_detector_callback(model_name, agent_state)
+
+            if obstacle_type is not None:
+                # Create obstacle state 
+                obstacle_state = ObstacleState(
+                    dimensions=(0,0,0),
+                    outline=None,
+                    pose=agent_pose,
+                    type=getattr(ObstacleMaterialEnum, obstacle_type.upper()),
+                    activity=ObstacleStateEnum.STANDING,
+                )
+                self.obstacle_detector_callback(model_name, obstacle_state)
 
     def subscribe_sensor(self, name, callback, type=None):
         if name == 'gnss':
@@ -424,6 +453,11 @@ def callback_with_cv2(msg: Image):
                 raise ValueError("GEMGazeboInterface only supports AgentState for agent_detector")
             self.agent_detector_callback = callback
 
+        elif name == 'obstacle_detector':
+            if type is not None and type is not ObstacleState:
+                raise ValueError("GEMGazeboInterface only supports ObstacleState for obstacle_detector")
+            self.obstacle_detector_callback = callback
+
     def hardware_faults(self) -> List[str]:
         # In simulation, we don't have real hardware faults
         return self.faults
@@ -459,10 +493,12 @@ def send_command(self, command : GEMVehicleCommand):
 
         # Calculate acceleration from pedal positions
         acceleration = pedal_positions_to_acceleration(accelerator_pedal_position, brake_pedal_position, v, 0, 1)
-
+        print("acceleration before", acceleration)
         # Apply reasonable limits to acceleration
         max_accel = settings.get('vehicle.limits.max_acceleration', 1.0)
-        max_decel = settings.get('vehicle.limits.max_deceleration', -2.0)
+        max_decel = -1 * settings.get('vehicle.limits.max_deceleration', 2.0) # cuz ackermann expects neg but pure pursiut wants positive decel val
+        print("max_accel", max_accel)
+        print("max_decel", max_decel)
         acceleration = np.clip(acceleration, max_decel, max_accel)
 
         # Convert wheel angle to steering angle (front wheel angle)
@@ -477,14 +513,15 @@ def send_command(self, command : GEMVehicleCommand):
         # Don't use infinite speed, instead calculate a reasonable target speed
         current_speed = v
         target_speed = current_speed
-
+        print("acceleration ", acceleration)
         if acceleration > 0:
             # Accelerating - set target speed to current speed plus some increment
             # This is more realistic than infinite speed
             max_speed = settings.get('vehicle.limits.max_speed', 10.0)
             target_speed = min(current_speed + acceleration * 0.5, max_speed)
         elif acceleration < 0:
             # Braking - set target speed to zero if deceleration is significant
+            print("braking ", acceleration)
             if brake_pedal_position > 0.1:
                 target_speed = 0.0
 
@@ -499,4 +536,4 @@ def send_command(self, command : GEMVehicleCommand):
         print(f"[ACKERMANN] Speed: {msg.speed:.2f}, Accel: {msg.acceleration:.2f}, Steer: {msg.steering_angle:.2f}")
 
         self.ackermann_pub.publish(msg)
-        self.last_command = command
+        self.last_command = command

GEMstack/onboard/perception/obstacle_detection.py

@@ -0,0 +1,56 @@
+
+import threading
+import copy
+from typing import Dict
+from ..component import Component
+
+from ..interface.gem import GEMInterface
+from ...state.obstacle import ObstacleState
+
+class OmniscientObstacleDetector(Component):
+    """Obtains agent detections from a simulator"""
+    def __init__(self,vehicle_interface : GEMInterface):
+        self.vehicle_interface = vehicle_interface
+        self.obstacles = {}
+        self.lock = threading.Lock()
+
+    def rate(self):
+        return 15.0
+
+    def state_inputs(self):
+        return []
+
+    def state_outputs(self):
+        return ['obstacles']
+
+    def initialize(self):
+        self.vehicle_interface.subscribe_sensor('obstacle_detector',self.obstacle_callback, ObstacleState)
+
+    def obstacle_callback(self, name : str, obstacle : ObstacleState):
+        with self.lock:
+            self.obstacles[name] = obstacle
+
+    def update(self) -> Dict[str,ObstacleState]:
+        with self.lock:
+            return copy.deepcopy(self.obstacles)
+
+
+class GazeboObstacleDetector(OmniscientObstacleDetector):
+    """Obtains agent detections from the Gazebo simulator using model_states topic"""
+    def __init__(self, vehicle_interface : GEMInterface, tracked_obstacle_prefixes=None):
+        super().__init__(vehicle_interface)
+
+        # If specific model prefixes are provided, configure the interface to track them
+        if tracked_obstacle_prefixes is not None:
+            # Check if our interface has the tracked_model_prefixes attribute (is a GazeboInterface)
+            if hasattr(vehicle_interface, 'tracked_obstacle_prefixes'):
+                vehicle_interface.tracked_obstacle_prefixes = tracked_obstacle_prefixes
+                print(f"Configured GazeboConeDetector to track obstacles with prefixes: {tracked_obstacle_prefixes}")
+            else:
+                print("Warning: vehicle_interface doesn't support tracked_obstacle_prefixes configuration")
+
+    def initialize(self):
+        # Use the same agent_detector sensor as OmniscientAgentDetector
+        # The GazeboInterface implements this with model_states subscription
+        super().initialize()
+        print("GazeboConeDetector initialized and subscribed to model_states")

GEMstack/onboard/visualization/mpl_visualization.py

@@ -97,6 +97,7 @@ def update(self, state):
         # Print debugging info about the vehicle's position and frame
         if self.num_updates % 10 == 0:
             print(f"Vehicle position: ({state.vehicle.pose.x:.2f}, {state.vehicle.pose.y:.2f}), frame: {state.vehicle.pose.frame}")
+            print("Obstacle state:", state.obstacles)
 
         # Pedestrian metrics and position debugging
         ped_positions = []
@@ -246,6 +247,18 @@ def update(self, state):
         except Exception as e:
             print(f"Error in pedestrian plot: {str(e)}")
 
+        try:
+            # ---- plot static obstacles in orange ----
+            for obs in state.obstacles.values():
+                x_obs, y_obs = obs.pose.x, obs.pose.y
+                self.axs[0].plot(x_obs, y_obs, 'o',
+                                markersize=8,
+                                markerfacecolor='orange',
+                                markeredgecolor='darkorange',
+                                label='_nolegend_')
+        except Exception as e:
+            print(f"Error plotting obstacles: {str(e)}")
+
         # Update canvas
         try:
             self.fig.canvas.draw_idle()

GEMstack/state/__init__.py

@@ -8,7 +8,7 @@
            'VehicleState',
            'Roadgraph',
            'Roadmap',
-           'Obstacle',
+           'Obstacle', 'ObstacleMaterialEnum','ObstacleStateEnum','ObstacleState',
            'Sign',
            'AgentState','AgentEnum','AgentActivityEnum',
            'SceneState',
@@ -23,7 +23,7 @@
 from .trajectory import Path,Trajectory
 from .vehicle import VehicleState,VehicleGearEnum
 from .roadgraph import Roadgraph, RoadgraphLane, RoadgraphCurve, RoadgraphRegion, RoadgraphCurveEnum, RoadgraphLaneEnum, RoadgraphRegionEnum, RoadgraphSurfaceEnum, RoadgraphConnectionEnum
-from .obstacle import Obstacle, ObstacleMaterialEnum
+from .obstacle import Obstacle, ObstacleMaterialEnum, ObstacleStateEnum, ObstacleState
 from .sign import Sign, SignEnum, SignalLightEnum, SignState
 from .roadmap import Roadmap
 from .agent import AgentState, AgentEnum, AgentActivityEnum