Targeted Parallelization of Conflict-Based Search for Multi-Robot Path Planning

Multi-Robot Path Planning (MRPP) on graphs, also known as Multi-Agent PathFinding (MAPF), is a well-established NP-hard problem with critically important applications. In (near)-optimally solving MRPP, as serial computation approaches its efficiency limits, parallelization offers a promising route to extend that limit further. As a single solution is unlikely to be successful in addressing all settings, e.g., in handling small/hard or large/sparse MRPP instances, in this study, we explore a targeted parallelization effort to boost the performance of conflict-based search for MRPP. Specifically, when instances are relatively small but robots are densely packed with strong interactions, we devise a decen-tralized parallel algorithm that concurrently explores multiple branches that leads to markedly enhanced solution discovery. On the other hand, for large problems with sparse robot-robot interactions, we find that prioritizing node expansion and conflict resolution more promising. Our innovative multi-threaded approach to parallelizing bounded-suboptimal conflict search-based algorithms demonstrates significant improvements over baseline serial methods in success rate or runtime. Our work furthers the understanding of MRPP and charts a promising path for elevating solution quality and computational efficiency through parallel algorithmic strategies.