Backward-Forward Search for Manipulation Planning
This addresses manipulation planning for robots in cluttered environments, but it appears incremental as it builds on existing sampling-based methods.
The paper tackles manipulation planning in high-dimensional hybrid spaces by introducing the hybrid backward-forward (HBF) algorithm, which uses backward constraint identification to guide forward sampling, resulting in a probabilistically complete planner that effectively constructs long plans with prehensile and nonprehensile actions in cluttered environments.
In this paper we address planning problems in high-dimensional hybrid configuration spaces, with a particular focus on manipulation planning problems involving many objects. We present the hybrid backward-forward (HBF) planning algorithm that uses a backward identification of constraints to direct the sampling of the infinite action space in a forward search from the initial state towards a goal configuration. The resulting planner is probabilistically complete and can effectively construct long manipulation plans requiring both prehensile and nonprehensile actions in cluttered environments.