P-ARC: Exploiting Subproblem Independence for Parallel Multi-Robot Motion Planning

原文アブストラクト

This paper presents Parallel ARC (P-ARC), a parallel formulation of the Adaptive Robot Coordination (ARC) approach to multi-robot motion planning (MRMP) which exploits subproblem independence. ARC's adaptive (de)composition of the multi-robot planning space exposes parallelism: single-robot paths are solved independently and iterative conflict detection and resolution create locally coupled subproblems. While distributing single-robot queries is trivial, not all conflicts are independent, so P-ARC proposes robot-disjoint conflict batches which enable efficient distributed detection and concurrent repair. Additionally, OR-multi-start strategies are employed at the global and subproblem resolution levels, creating a hybrid parallel strategy OR-P-ARC. We evaluate the methods against sequential ARC, multi-start OR-ARC, and coupled and prioritized parallel baselines on controlled 2D mobile robot and planar-manipulator problems with up to 256 robots and 3D Panda manipulator problems with up to 16 robots. On 16-robot Panda tasks, with 16 workers, P-ARC and OR-P-ARC achieve 3.48X and 6.67X speedups, respectively.