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Improved decentralized cooperative multi-agent path finding for robots with limited communication

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Abstract

Multi-agent path finding (MAPF) holds significant practical relevance in numerous real-world applications involving fleets of mobile robots. The efficiency of such systems is directly determined by the quality of the paths calculated. Accordingly, extensive effort has been directed toward creating effective algorithms to address the MAPF problem. Yet, many existing MAPF algorithms still depend on offline centralized planning, paired with often unrealistic assumptions—such as robots having complete observability of the environment and moving in a deterministic fashion. The resultant plans are typically unsuitable for direct implementation on real robots where these assumptions do not usually apply. Aiming for more effective robot coordination under realistic conditions, we introduce an enhanced decentralized method. In this method, each robot coordinates solely with neighbors within a limited communication radius. Each robot attempts to follow the shortest path from its starting point to its designated target, addressing conflicts with other robots as they occur. Our method also incorporates path replanning, local motion coordination, and mechanisms to avoid robots becoming trapped in livelocks or deadlocks. Simulation-based results from various benchmark scenarios confirm that our enhanced decentralized method is both effective and scalable, accommodating up to at least 1000 robots.

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Acknowledgements

This work was supported by the Independent Research Fund Denmark under grant 0136-00251B.

Funding

This work was supported by the Independent Research Fund Denmark under Grant 0136-00251B.

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Authors and Affiliations

  1. Interdisciplinary Research Center for Intelligent Manufacturing and Robotics (IRC-IMR), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia

    Abderraouf Maoudj

  2. SDU UAS Center, MMMI, University of Southern Denmark (SDU), Odense, Denmark

    Anders Lyhne Christensen

Authors
  1. Abderraouf Maoudj
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  2. Anders Lyhne Christensen
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Contributions

AM contributed to conceptualization, methodology, software, validation, writing, review, and editing. ALC contributed to conceptualization, methodology, software, validation, writing, review, editing, and supervision. All authors read and approved the final manuscript.

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Correspondence to Abderraouf Maoudj.

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Maoudj, A., Christensen, A.L. Improved decentralized cooperative multi-agent path finding for robots with limited communication. Swarm Intell (2023). https://doi.org/10.1007/s11721-023-00230-7

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