Abstract
This paper presents a sampling-based motion planner geared towards mobile robots with differential constraints. The planner conducts the search for a trajectory to the goal region by using sampling to expand a tree of collision-free and dynamically-feasible motions. To guide the tree expansion, a workspace decomposition is used to partition the motion tree into groups. Priority is given to tree expansions from groups that are close to the goal according to the shortest-path distances in the workspace decomposition. To counterbalance the greediness of the shortest-path heuristic, when the planner fails to expand the tree from one region to the next, the costs of the corresponding edges in the workspace decomposition are increased. Such cost increases enable the planner to quickly discover alternative routes to the goal when progress along the current route becomes difficult or impossible. Comparisons to related work show significant speedups.
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