A Lightweight Traffic Map for Efficient Anytime LaCAM*

2026-03-09

AI Summary

This work addresses the high computational cost and reliance on static guidance in existing guided multi-agent pathfinding (MAPF) approaches, which hinder efficient solution quality improvement in large-scale scenarios. The authors propose a dynamic, lightweight traffic graph mechanism that constructs traffic graphs in real time within the LaCAM* framework to guide agents in avoiding congestion, thereby eliminating the need for repeated single-agent searches inherent in traditional Frank-Wolfe optimization. By integrating configuration space search, the method enables efficient anytime MAPF solving. Experimental results demonstrate that the approach consistently outperforms state-of-the-art guided methods on two MAPF variants, achieving significant improvements in both solution quality and computational efficiency.

Technology Category

• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Multi-Agent Path Finding
• LaCAM*
• guidance path
• computational overhead
• traffic congestion

Innovation

• Lightweight Traffic Map
• Dynamic Guidance
• Anytime MAPF
• LaCAM*
• Multi-Agent Path Finding