Windowed MAPF with Completeness Guarantees

2024-10-02

arXiv.org

AI Summary

Traditional windowed multi-agent path finding (MAPF) approaches suffer from myopic planning, leading to deadlocks or livelocks and lacking theoretical completeness guarantees. This paper proposes WinC-MAPF, the first windowed online MAPF framework with provable completeness. Its core is the Single-Step Conflict-Based Search (SS-CBS) algorithm, which integrates dynamic heuristic updating, single-step planning, and agent-wise independence modeling. SS-CBS expands only the minimal necessary conflict constraints per step, thereby balancing real-time performance and completeness. Experimental results demonstrate that WinC-MAPF significantly improves solution success rates and response speed in complex, dynamic environments. It fundamentally overcomes the completeness bottleneck inherent in existing windowed MAPF methods, establishing a verifiable and deployable planning foundation for real-time multi-agent systems.

Technology Category

• Multiagent Systems: Multiagent Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Ensures completeness in windowed MAPF systems
• Prevents deadlock and livelock in path planning
• Improves real-time heuristic search efficiency

Innovation

• Windowed MAPF completeness
• Heuristic update insights
• Single-Step CBS modification