Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

TL;DR

This paper introduces a scalable framework for lifelong multi-agent pathfinding in large warehouses, enabling efficient collision-free navigation for thousands of agents with continually changing goals.

Contribution

It proposes the Rolling-Horizon Collision Resolution (RHCR) framework that decomposes lifelong MAPF into windowed instances, improving scalability and adaptability in large-scale warehouse scenarios.

Findings

RHCR can handle up to 1,000 agents efficiently.

It significantly outperforms existing methods in solution quality.

The approach adapts well to dynamic goal changes.

Abstract

Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. We propose a new framework Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of the agents only within a bounded time horizon and ignores collisions beyond it. RHCR is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. We empirically evaluate RHCR with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents (= 38.9\% of the empty cells on the map) for simulated warehouse instances, significantly outperforming existing work.