A Lightweight Traffic Map for Efficient Anytime LaCAM*

TL;DR

This paper introduces a dynamic, lightweight traffic map for LaCAM* that improves multi-agent pathfinding efficiency and solution quality by reducing computational overhead and adapting guidance during search.

Contribution

We propose a novel dynamic traffic map construction method for LaCAM* that enhances solution quality and efficiency in large-scale MAPF problems.

Findings

Higher solution quality than existing guidance-path methods

Reduced computational overhead in large-scale MAPF

Effective in multiple MAPF variants

Abstract

Multi-Agent Path Finding (MAPF) aims to compute collision-free paths for multiple agents and has a wide range of practical applications. LaCAM*, an anytime configuration-based solver, currently represents the state of the art. Recent work has explored the use of guidance paths to steer LaCAM* toward configurations that avoid traffic congestion, thereby improving solution quality. However, existing approaches rely on Frank-Wolfe-style optimization that repeatedly invokes single-agent search before executing LaCAM*, resulting in substantial computational overhead for large-scale problems. Moreover, the guidance path is static and primarily beneficial for finding the first solution in LaCAM*. To address these limitations, we propose a new approach that leverages LaCAM*'s ability to construct a dynamic, lightweight traffic map during its search. Experimental results demonstrate that our method achieves higher solution quality than state-of-the-art guidance-path approaches across two MAPF variants.