Local Guidance for Configuration-Based Multi-Agent Pathfinding

TL;DR

This paper introduces local guidance in multi-agent pathfinding, providing agents with spatiotemporal cues that improve solution quality efficiently, especially when integrated with LaCAM, a configuration-based solver.

Contribution

It presents a novel local guidance approach for MAPF that enhances solution quality without high computational costs, advancing the state-of-the-art in configuration-based methods.

Findings

Local guidance improves MAPF solution quality.

Spatiotemporal cues significantly reduce agents' waiting times.

Guidance enhances LaCAM's performance frontier.

Abstract

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.