Lifelong LaCAM with Local Guidance for Lifelong MAPF

TL;DR

This paper introduces LLLG, a lifelong MAPF planner that enhances LaCAM with local guidance, employing a receding horizon approach to improve scalability and performance in dense, dynamic environments.

Contribution

It extends LaCAM with local guidance to the lifelong setting, demonstrating scalable, high-throughput planning for continuous, real-time multi-agent pathfinding tasks.

Findings

LLLG outperforms existing planners in dense environments.

It maintains high throughput over long horizons.

The approach scales effectively with agent density.

Abstract

Local guidance has recently proven to be a powerful driver of empirical performance in real-time, suboptimal multi-agent pathfinding (MAPF), improving the scalable configuration-based solver LaCAM. By injecting informative spatiotemporal cues around each agent, local guidance mitigates congestion, reduces waiting, and remains scalable enough even with tight time budgets, yielding state-of-the-art performance for one-shot MAPF. This study asks whether the same benefits can be lifted to the lifelong setting (LMAPF), where tasks arrive continuously and improvements in per-step plans can increase task completion throughput over long horizons. We propose LLLG, a Lifelong version of LaCAM enhanced with Local Guidance, which employs a receding-horizon windowed planning framework and warm-starts guidance from the previous solution at each timestep. Our method scales effectively, maintains high throughput even in compact, dense environments, and surpasses existing planners, thereby pushing the frontier of real-time, lifelong MAPF.