Engineering LaCAM$^\ast$: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding

TL;DR

This paper enhances the LaCAM* algorithm for multi-agent pathfinding, making it more suitable for real-time, large-scale applications by improving solution quality and convergence speed through various techniques.

Contribution

It introduces several improvements to LaCAM* that significantly enhance its solution quality and convergence speed, advancing large-scale MAPF capabilities.

Findings

Improved solution quality of LaCAM* with new techniques

Faster convergence to near-optimal solutions

Enhanced scalability for large multi-agent systems

Abstract

This paper addresses the challenges of real-time, large-scale, and near-optimal multi-agent pathfinding (MAPF) through enhancements to the recently proposed LaCAM* algorithm. LaCAM* is a scalable search-based algorithm that guarantees the eventual finding of optimal solutions for cumulative transition costs. While it has demonstrated remarkable planning success rates, surpassing various state-of-the-art MAPF methods, its initial solution quality is far from optimal, and its convergence speed to the optimum is slow. To overcome these limitations, this paper introduces several improvement techniques, partly drawing inspiration from other MAPF methods. We provide empirical evidence that the fusion of these techniques significantly improves the solution quality of LaCAM*, thus further pushing the boundaries of MAPF algorithms.