Local Guidance for Configuration-Based Multi-Agent Pathfinding
This work addresses congestion and coordination inefficiencies in MAPF for applications like robotics and logistics, representing an incremental improvement over existing global guidance methods.
The study tackled the problem of improving multi-agent pathfinding (MAPF) by introducing local guidance near each agent, which significantly enhanced solution quality without exceeding a moderate time budget, establishing a new performance frontier when applied to the LaCAM solver.
Guidance is an emerging concept that improves the empirical performance of real-time, sub-optimal multi-agent pathfinding (MAPF) methods. It offers additional information to MAPF algorithms to mitigate congestion on a global scale by considering the collective behavior of all agents across the entire workspace. This global perspective helps reduce agents' waiting times, thereby improving overall coordination efficiency. In contrast, this study explores an alternative approach: providing local guidance in the vicinity of each agent. While such localized methods involve recomputation as agents move and may appear computationally demanding, we empirically demonstrate that supplying informative spatiotemporal cues to the planner can significantly improve solution quality without exceeding a moderate time budget. When applied to LaCAM, a leading configuration-based solver, this form of guidance establishes a new performance frontier for MAPF.