mppi dynamic reconfigure

aichallenge/workspace/src/aichallenge_submit/booars_launch/config/planning/costmap_generator.param.yaml

@@ -13,7 +13,7 @@
         type: "cached_lanelet"
         map_topic: "/map/vector_map"
         costmap_topic: "~/debug/cached_costmap"
-        inflation_radius: 1.4 # [m]
+        inflation_radius: 1.8 # [m]
         cached_costmap:
           min_x: 89607.0  # [m]
           max_x: 89687.0  # [m]

aichallenge/workspace/src/aichallenge_submit/booars_launch/config/planning/mppi_controller.param.yaml

@@ -2,20 +2,20 @@
   ros__parameters:
       # mppi
       horizon : 25
-      num_samples : 4000
-      u_min : [-4.0, -0.35] # accel(m/s2), steer angle(rad)
-      u_max : [3.0, 0.35]
-      sigmas : [2.0, 0.35] # sample range
+      num_samples : 5000
+      u_min : [-4.0, -0.25] # accel(m/s2), steer angle(rad)
+      u_max : [2.0, 0.25]
+      sigmas : [2.0, 0.25] # sample range
       lambda : 1.0 
-      auto_lambda : false
+      auto_lambda : true
       # reference path
       DL : 0.1
       lookahead_distance : 0.1
-      reference_path_interval : 0.8
+      reference_path_interval : 0.83
       # cost weights
-      Qc : 10.0
-      Ql : 1.0
-      Qv : 4.0
+      Qc : 20.0
+      Ql : 5.0
+      Qv : 0.5
       Qo : 1000.0
       Qin : 0.01
       Qdin : 0.0

aichallenge/workspace/src/aichallenge_submit/booars_launch/launch/components/control.launch.xml

@@ -12,8 +12,8 @@
 
     <!-- <node pkg="simple_pure_pursuit" exec="simple_pure_pursuit" name="simple_pure_pursuit_node"
       output="screen" unless="$(var use_stanley)">
-      <param name="use_external_target_vel" value="true" />
-      <param name="external_target_vel" value="8.0" />
+      <param name="use_external_target_vel" value="false" />
+      <param name="external_target_vel" value="8.3" />
       <param name="lookahead_gain" value="0.24" />
       <param name="lookahead_min_distance" value="2.0" />
       <param name="speed_proportional_gain" value="2.0" />

aichallenge/workspace/src/aichallenge_submit/mppi_controller/mppi_controller/mppi_controller.py

@@ -59,6 +59,23 @@ def __init__(self, config, debug=False, device=torch.device("cuda"), dtype=torch
         self.reference_path: torch.Tensor = None
         self.cost_map: CostMapTensor = None
 
+    def update_params(self, config):
+        self.config = config
+
+        # model parameter
+        self.delta_t = torch.tensor(self.config["delta_t"], device=self._device, dtype=self._dtype)
+        self.vehicle_L = torch.tensor(self.config["vehicle_L"], device=self._device, dtype=self._dtype)
+        self.V_MAX = torch.tensor(self.config["V_MAX"], device=self._device, dtype=self._dtype)
+
+        # cost weights
+        self.Qc = self.config["Qc"]
+        self.Ql = self.config["Ql"]
+        self.Qv = self.config["Qv"]
+        self.Qo = self.config["Qo"]
+        self.Qin = self.config["Qin"]
+        self.Qdin = self.config["Qdin"]
+
+
     def update(self, state: torch.Tensor, racing_center_path: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
         """
         Update the controller with the current state and reference path.
@@ -170,6 +187,12 @@ def cost_function(self, state: torch.Tensor, action: torch.Tensor, info: dict) -
         input_cost = self.Qin * action.pow(2).sum(dim=1)
         input_cost += self.Qdin * (action - prev_action).pow(2).sum(dim=1)
 
+        # dtheta cost
+        # steer = action[:, 1]
+        # dtheta = v * torch.tan(steer) / self.vehicle_L
+        # MAX_DTHETA = 1
+        # dtheta_cost = 400*torch.relu(torch.abs(dtheta) - MAX_DTHETA)
+
         cost = path_cost + velocity_cost + obstacle_cost + input_cost
 
         return cost
@@ -218,6 +241,45 @@ def resample_path(path, DL):
                 start_idx += segment_length
 
             return new_path
+
+        def compute_curvature(path, interval=5):
+            """
+            Compute curvature for each segment of the path at specified intervals and linearly interpolate the skipped points.
+            Args:
+                path (torch.Tensor): path points with shape (N, 4) [x, y, yaw, target_v]
+                interval (int): number of points to skip when computing curvature (the larger the value, the fewer points are used)
+
+            Returns:
+                torch.Tensor: curvature values for each path point, shape (N,)
+            """
+            # Initialize curvature tensor
+            curvature = torch.zeros(path.shape[0], dtype=path.dtype, device=path.device)
+
+            # Compute curvature at every 'interval' points
+            for i in range(interval, len(path) - interval, interval):
+                # Compute central difference for first derivative (dx, dy)
+                dx = (path[i + interval, 0] - path[i - interval, 0]) / (2.0 * interval)
+                dy = (path[i + interval, 1] - path[i - interval, 1]) / (2.0 * interval)
+
+                # Compute central difference for second derivative (ddx, ddy)
+                ddx = path[i + interval, 0] - 2 * path[i, 0] + path[i - interval, 0]
+                ddy = path[i + interval, 1] - 2 * path[i, 1] + path[i - interval, 1]
+
+                # Curvature formula: k = (x'y'' - y'x'') / (x'^2 + y'^2)^(3/2)
+                curvature[i] = torch.abs(dx * ddy - dy * ddx) / (dx.pow(2) + dy.pow(2)).pow(3 / 2)
+
+            # Linearly interpolate skipped points
+            for i in range(interval, len(path) - interval, interval):
+                if i + interval < len(path):
+                    for j in range(1, interval):
+                        curvature[i + j] = curvature[i] + (curvature[i + interval] - curvature[i]) * (j / interval)
+
+            # Handle the boundary cases (start and end)
+            curvature[:interval] = curvature[interval]
+            curvature[-interval:] = curvature[-interval - 1]
+
+            return curvature
+
 
         # Resample the path with the specified DL
         path = resample_path(path, DL)
@@ -229,13 +291,30 @@ def resample_path(path, DL):
         # Ensure the index is not less than the current index
         ind = max(cind, ind)
 
+        # Compute curvature along the path
+        # curvature = compute_curvature(path)
+
         # Generate the rest of the reference trajectory
         travel = lookahead_distance
 
         for i in range(horizon + 1):
             travel += reference_path_interval
             dind = int(round(travel / DL))
 
+            # if (ind + dind) < ncourse:
+            #     xref[i] = path[ind + dind]
+
+            #     # Adjust target velocity based on curvature
+            #     if (ind + dind) < len(curvature):
+            #         current_curvature = curvature[ind + dind]
+            #         # Lower the target velocity when curvature is high
+            #         print("curvature:", current_curvature)
+            #         if current_curvature > 2.0:  # Set threshold for high curvature
+            #             xref[i, 3] = 1.0
+
+            # else:
+            #     xref[i] = path[-1]
+
             if (ind + dind) < ncourse:
                 xref[i] = path[ind + dind]
             else:

aichallenge/workspace/src/aichallenge_submit/mppi_controller/mppi_controller/mppi_controller_node.py

@@ -1,5 +1,8 @@
 import rclpy
 from rclpy.node import Node
+from rcl_interfaces.msg import ParameterEvent
+from rclpy.parameter import Parameter
+from rcl_interfaces.msg import SetParametersResult
 from nav_msgs.msg import Odometry
 from geometry_msgs.msg import Pose, Point, Quaternion
 from autoware_auto_planning_msgs.msg import Trajectory, TrajectoryPoint
@@ -40,7 +43,7 @@ def __init__(self):
         self.declare_parameter('vehicle_L', 1.0)
         self.declare_parameter('V_MAX', 8.0)
         # get
-        config = {
+        self.config = {
             "horizon": self.get_parameter('horizon').get_parameter_value().integer_value,
             "num_samples": self.get_parameter('num_samples').get_parameter_value().integer_value,
             "u_min": self.get_parameter('u_min').get_parameter_value().double_array_value,
@@ -61,8 +64,10 @@ def __init__(self):
             "vehicle_L": self.get_parameter('vehicle_L').get_parameter_value().double_value,
             "V_MAX": self.get_parameter('V_MAX').get_parameter_value().double_value,
         }
+        self.get_logger().info(f'config: {self.config}')
 
-        self.get_logger().info(f'config: {config}')
+        # Add parameter change callback
+        self.add_on_set_parameters_callback(self.parameter_callback)
 
         # publisher
         # control command
@@ -100,7 +105,7 @@ def __init__(self):
         self.dtype = torch.float32
 
         # mppi controller
-        self.controller = mppi_controller(config=config, debug=True, device=self.device, dtype=self.dtype)
+        self.controller = mppi_controller(config=self.config, debug=True, device=self.device, dtype=self.dtype)
 
         self.odometry: Odometry = None
         self.trajectory: Trajectory = None
@@ -117,6 +122,25 @@ def trajectory_callback(self, msg : Trajectory):
     def costmap_callback(self, msg : OccupancyGrid):
         self.costmap = msg
 
+    def parameter_callback(self, params):
+        for param in params:
+            if param.name in self.config:
+                if param.type_ == Parameter.Type.DOUBLE:
+                    self.config[param.name] = param.value
+                elif param.type_ == Parameter.Type.INTEGER:
+                    self.config[param.name] = param.value
+                elif param.type_ == Parameter.Type.BOOL:
+                    self.config[param.name] = param.value
+                elif param.type_ == Parameter.Type.DOUBLE_ARRAY:
+                    self.config[param.name] = param.value
+                self.get_logger().info(f"Parameter {param.name} changed to {param.value}")
+
+        # update controller
+        self.controller.update_params(self.config)
+
+        # Return a success result
+        return SetParametersResult(successful=True)
+
     def zero_ackermann_control_command(self):
         cmd = AckermannControlCommand()
         now = self.get_clock().now().to_msg()