Windowed MAPF with Completeness Guarantees

TL;DR

This paper introduces WinC-MAPF, a framework for windowed multi-agent pathfinding that guarantees completeness, using heuristic updates and agent independence, and demonstrates its effectiveness with the novel SS-CBS algorithm.

Contribution

The paper presents WinC-MAPF, a novel framework that ensures completeness in windowed MAPF, along with the SS-CBS algorithm that plans one step at a time.

Findings

SS-CBS effectively solves challenging MAPF scenarios.

WinC-MAPF guarantees completeness in windowed MAPF.

The approach outperforms existing windowed methods in deadlock scenarios.

Abstract

Traditional multi-agent path finding (MAPF) methods try to compute entire start-goal paths which are collision free. However, computing an entire path can take too long for MAPF systems where agents need to replan fast. Methods that address this typically employ a "windowed" approach and only try to find collision free paths for a small windowed timestep horizon. This adaptation comes at the cost of incompleteness; all current windowed approaches can become stuck in deadlock or livelock. Our main contribution is to introduce our framework, WinC-MAPF, for Windowed MAPF that enables completeness. Our framework uses heuristic update insights from single-agent real-time heuristic search algorithms as well as agent independence ideas from MAPF algorithms. We also develop Single-Step CBS (SS-CBS), an instantiation of this framework using a novel modification to CBS. We show how SS-CBS, which only plans a single step and updates heuristics, can effectively solve tough scenarios where existing windowed approaches fail.