feat(path_planner): params to adjust hard constraints and path reuse

Signed-off-by: Maxime CLEMENT <[email protected]>

autoware_launch/config/planning/scenario_planning/lane_driving/motion_planning/path_sampler/path_sampler.param.yaml

@@ -8,25 +8,30 @@
       id: 0  # id of the candidate paths for which to print/show details (e.g., footprint in rviz)
 
     preprocessing:
-      force_zero_initial_deviation: True  # if true, initial planning starts from the reference path
-      force_zero_initial_heading: True # if true, initial planning starts with a heading aligned with the reference path
+      force_zero_initial_deviation: False # if true, initial planning starts from the reference path
+      force_zero_initial_heading: False # if true, initial planning starts with a heading aligned with the reference path
       smooth_reference_trajectory: False # if true, the reference trajectory is smoothed before being used for planning
     constraints:
       hard:
         max_curvature: 0.1  # [m⁻¹] maximum curvature of a sampled path
         min_curvature: -0.1  # [m⁻¹] minimum curvature of a sampled path
+        min_distance_from_obstacles: 0.5  # [m] min allowed distance from obstacles
+        limit_footprint_inside_drivable_area: false # if true, paths where the ego footprint exits the drivable area are rejected
       soft:
         lateral_deviation_weight: 1.0  # cost weight for lateral deviation between the end of a sampled path and the reference path
         length_weight: 1.0  # cost weight for the length of a sampled path
         curvature_weight: 1.0  # cost weight for the curvature of a sampled path
+    path_reuse:
+      maximum_lateral_deviation: 1.0  # [m] reset the previous path if ego deviates from it by more than this value
+      direct_reuse_distance: 2.0 # [m] a new path is generated only after ego travels this distance along the previously calculated path, or if it has become invalid
     sampling:
       enable_frenet: True
       enable_bezier: True
       resolution: 0.5  # [m] target distance between sampled path points
       previous_path_reuse_points_nb: 2  # number of points reused from the previously generated path (0:no reuse, 1:replan from end of prev path, 2: end and mid of prev path, etc)
       target_lengths: [10.0, 20.0]  # [m] target lengths of the sampled paths
-      nb_target_lateral_positions: 2 # number of lateral positions to use for sampling (in addition to 0.0 and the current lateral deviation)
-      target_lateral_positions: [-0.5, 0.0, 0.5]  # manual values that are only used if nb_target_lateral_positions = 0
+      nb_target_lateral_positions: 0 # number of lateral positions to use for sampling (in addition to 0.0 and the current lateral deviation)
+      target_lateral_positions: [-2.0, -1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5, 2.0]  # manual values that are only used if nb_target_lateral_positions = 0
       frenet:  # target values for the sampled "lateral deviation over longitudinal position" polynomial
         target_lateral_velocities: [-0.1, 0.0, 0.1]
         target_lateral_accelerations: [0.0]