move_base plugin implementing global trajectory planning with D* informed incremental search algorithm with external optimizers. It strives to simplify development of the robotic systems operating in dense industrial environments.
The package contains two plugins:
- D-star trajectory planner that maintains generation of the global trajectory on the provided costmap, and dynamic replanning by request coming from
move_base. - Virtual Walls module processing JSON objects to generate the non-passable and explicitly passable zones as OpenCV polygons added over the existing map.
The plugin accepts the following parameters
| Parameter | Unit | Default value | Description |
|---|---|---|---|
goal_distance_threshold |
m | 0.3 | Maximal distance from the chassis' reference frame origin to consider the goal reached |
neighbor_distance_threshold |
m | 0.1 | The distance to consider as close for replanning |
occupancy_threshold |
- | 64 | OccupancyGrid cell weight threshold to consider the cell non-passable |
cutoff_distance |
OccupancyGrid cells |
16 | Maximal cutoff distance for raytracing optimizer |
trajectory_optimizer |
- | - | Group of parameters to set up the potential field optimizer |
trajectory_optimizer/repulsion_gain |
- | 50.0 | Gain value for repulsive potential calculation |
trajectory_optimizer/potential_field_radius |
OccupancyGrid cells |
10 | Maximal repulsion radius to calculate the potential during optimization |
erosion |
- | - | Group of parameters to set up map preprocessor |
erosion/enable |
- | false |
Applies an erosion algorithm to clean a noisy map |
erosion/erosion_gap |
OccupancyGrid cells |
2 | Erosion gap |
The plugin was developed within the Robotics and Remotization initiative held at Elettra Sincrotrone Trieste. It is a part of an innovative flexible control system for mobile robots that was presented by the group leaders in Tokyo during 16th IFToMM World Congress 2023. The interested ones can find the published paper here: https://doi.org/10.1007/978-3-031-45770-8_29