Runzhe Liang, Rishi Veerapaneni, Daniel Harabor, Jiaoyang Li, Maxim Likhachev

Real-Time LaCAM for Real-Time MAPF

Introduction

Real-time lacam for real-time mapf. Discover Real-Time LaCAM, the first provably complete MAPF method for real-time multi-agent systems. Plans paths in milliseconds, ensuring high success in congested environments.

Abstract

The vast majority of Multi-Agent Path Finding (MAPF) methods with completeness guarantees require planning full-horizon paths. However, planning full-horizon paths can take too long and be impractical in real-world applications. Instead, real-time planning and execution, which only allows the planner a finite amount of time before executing and replanning, is more practical for real-world multi-agent systems. Several methods utilize real-time planning schemes but none are provably complete, which leads to livelock or deadlock. Our main contribution is Real-Time LaCAM, the first Real-Time MAPF method with provable completeness guarantees. We do this by leveraging LaCAM in an incremental fashion. Our results show how we can iteratively plan for congested environments with a cutoff time of milliseconds while still maintaining the same success rate as full-horizon LaCAM. We also show how it can be used with a single-step learned MAPF policy.

Review

This paper addresses a significant challenge in Multi-Agent Path Finding (MAPF): the practical limitations of full-horizon planning in real-world, time-sensitive applications. The authors propose Real-Time LaCAM (RT-LaCAM), an innovative approach designed to bridge the gap between theoretical completeness guarantees and the necessity for rapid, iterative planning and execution. The central claim is the development of the first Real-Time MAPF method that offers provable completeness guarantees, a critical advancement given the known issues of livelock and deadlock in existing real-time techniques. This work presents a compelling solution to a long-standing practical hurdle in the deployment of autonomous multi-agent systems. The primary strength of this work lies in achieving provable completeness within a real-time MAPF framework, directly tackling a major issue that has plagued previous real-time approaches. By incrementally leveraging LaCAM, a known complete algorithm, the authors appear to have successfully adapted its robustness to an iterative, time-constrained paradigm. The abstract highlights impressive empirical results, including planning for congested environments with millisecond-level cutoff times while maintaining the success rate of the full-horizon LaCAM. Furthermore, the demonstrated compatibility with single-step learned MAPF policies suggests a valuable degree of flexibility and potential for integration into hybrid systems, expanding the method's applicability. While the abstract presents a highly promising contribution, a full review would benefit from a more detailed explanation of the precise nature of the "provable completeness guarantees" in a real-time setting; specifically, whether this guarantee applies to each planning cycle, or to eventual resolution over multiple replanning steps under certain conditions. Further clarity on the trade-offs, if any, for achieving the "same success rate" as full-horizon LaCAM (e.g., regarding overall path quality or cumulative planning time over a longer mission) would also be valuable. Nonetheless, Real-Time LaCAM represents a significant conceptual and practical leap forward for MAPF, offering a robust solution to enable the deployment of multi-agent systems in time-critical scenarios where both efficiency and reliability are paramount. This work has the potential to substantially influence the design of future real-time autonomous systems.

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