Real-Time LaCAM for Real-Time MAPF
This addresses the need for practical, real-time planning in multi-agent systems, offering a provably complete solution to avoid livelock or deadlock, though it builds incrementally on existing LaCAM methods.
The paper tackles the problem of real-time multi-agent path finding (MAPF) by introducing Real-Time LaCAM, the first method with provable completeness guarantees, achieving success rates comparable to full-horizon planning with cutoff times in milliseconds.
The vast majority of Multi-Agent Path Finding (MAPF) methods with completeness guarantees require planning full-horizon paths. However, planning full-horizon paths can take too long and be impractical in real-world applications. Instead, real-time planning and execution, which only allows the planner a finite amount of time before executing and replanning, is more practical for real-world multi-agent systems. Several methods utilize real-time planning schemes but none are provably complete, which leads to livelock or deadlock. Our main contribution is Real-Time LaCAM, the first Real-Time MAPF method with provable completeness guarantees. We do this by leveraging LaCAM (Okumura 2023) in an incremental fashion. Our results show how we can iteratively plan for congested environments with a cutoff time of milliseconds while still maintaining the same success rate as full-horizon LaCAM. We also show how it can be used with a single-step learned MAPF policy.