LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding
This addresses the problem of efficient multi-robot coordination for researchers and practitioners, offering an incremental improvement over existing methods.
The paper tackles the multi-agent pathfinding (MAPF) problem by proposing LaCAM, a complete algorithm that uses a two-level search to find collision-free paths for hundreds of agents, achieving comparable or better performance than state-of-the-art sub-optimal algorithms in success rate, planning time, and solution quality.
We propose a novel complete algorithm for multi-agent pathfinding (MAPF) called lazy constraints addition search for MAPF (LaCAM). MAPF is a problem of finding collision-free paths for multiple agents on graphs and is the foundation of multi-robot coordination. LaCAM uses a two-level search to find solutions quickly, even with hundreds of agents or more. At the low-level, it searches constraints about agents' locations. At the high-level, it searches a sequence of all agents' locations, following the constraints specified by the low-level. Our exhaustive experiments reveal that LaCAM is comparable to or outperforms state-of-the-art sub-optimal MAPF algorithms in a variety of scenarios, regarding success rate, planning time, and solution quality of sum-of-costs.