Parking

Overview

How TO Use

1. Prepare a lanelet2 map and draw the polygon for the parking_lot area in the map. For reference on how to draw a lanelet2 map, see lanelet2 map.
2. When the vehicle is within the parking_lot area, the planner will switch to Parking scenario, and the costmap_generator and freespace_planner will start working.
3. The costmap_generator generates an occupancy_grid as the cost map for the freespace_planner.
4. The freespace_planner uses the cost map for path searching and planning.
5. The final trajectory is passed to the trajectory following module to generate control commands.

Costmap Generator

As shown in the figure below, when the vehicle is within the parking_lot area (the light-yellow polygon inside the blue box), the goal_pose(the green box with the coordinate axis) is set, and there are objects (the red box).

After setting the target within the parking_lot area, the occupancy_grip cost map can be visualized. The drivable area has a lower cost, shown in gray, while the area occupied by obstacles has a higher cost, shown in black. It can be seen that the lanelet that overlaps with the parking_lot is also considered in the calculation of the occupancy_grip, as the vehicle may have to drive within the lane_driving lane to reach the parking_lot while performing autonomous driving tasks. To avoid a gap in the map, the lanelet and parking_lot are connected by overlapping.

Key Parameters

Parameter	Description	Default Value
grid_min_value	minimum cost for gridmap	0.0
grid_max_value	maximum cost for gridmap	1.0
grid_resolution	resolution for gridmap	0.2(m)
grid_length_x	size of gridmap for x direction	70.0(m)
grid_length_y	size of gridmap for y direction	70.0(m)
grid_position_x	offset from coordinate in x direction	0.0(m)
grid_position_y	offset from coordinate in y direction	0.0(m)
maximum_lidar_height_thres	maximum height threshold for pointcloud data	0.3(m)
minimum_lidar_height_thres	minimum height threshold for pointcloud data	-2.2(m)
expand_rectangle_size	expand object's rectangle with this value	1.0(m)

Freespace Planner

Given the vehicle's position initial_pose and goal_pose, the freespace_planner uses A* Search to plan a feasible trajectory for the vehicle to reach the goal_pose.

Because the freespace planner may generate overlapping trajectories that can not be handled by the controller, so freespace planner split trajectory into serveral partial trajectories in order to make sure they can be used by controller.

Key Parameters

Node Parameters

Parameter	Description	Default Value
vehicle_shape_margin_m	collision margin in planning algorithm	1.0(m)
waypoints_velocity	velocity in output trajectory	5.0(km/h)
th_arrived_distance_m	threshold distance to check if vehicle has arrived at the trajectory's endpoint	1.0(m)
th_stopped_time_sec	threshold time to check if vehicle is stopped	1.0(s)
th_stopped_velocity_mps	threshold velocity to check if vehicle is stopped	0.01(m/s)
th_course_out_distance_m	threshold distance to check if vehicle is out of course	1.0(m)
replan_when_obstacle_found	whether replanning when obstacle has found on the trajectory	true
replan_when_course_out	whether replanning when vehicle is out of course	true

Planner Parameters

Parameter	Description	Default Value
time_limit	time limit of planning	30000.0(s)
minimum_turning_radius	minimum turning radius of robot	9.0(m)
maximum_turning_radius	maximum turning radius of robot	9.0(m)
theta_size	the number of angle's discretization	144
lateral_goal_range	goal range of lateral position	0.5
longitudinal_goal_range	goal range of longitudinal position	2.0
angle_goal_range	goal range of angle	6.0(deg)
curve_weight	additional cost factor for curve actions	1.2
reverse_weight	additional cost factor for reverse actions	2.0
obstacle_threshold	threshold for regarding a certain grid as obstacle	100

Reference

• planning design
• costmap generator
• freespace planner
• freespace planner algorithm