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Collective Decision-Making for Conflict Resolution in Multi-Agent Pathfinding

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Swarm Intelligence (ANTS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13491))

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Abstract

Multi-Agent Path Planning is important in many applications involving multiple mobile robots. In this paper, we present a novel algorithm to solve the Multi-Agent Pathfinding problem by using Collective Decision-making and indirect communication (stigmergy). We call this planning method Collective Conflict Resolution (CCR). The algorithm has two components: A mechanism to create a prioritization graph through collective decisions and a planning mechanism that is able to plan paths consistent with the priorities given by the graph. The CCR algorithm can be used both in global planning with full knowledge of all paths, and in decentralized settings with limited knowledge and communication. In our experiments, we compare our new planner with two state-of-the-art algorithms: Conflict-based Search (CBS) and Prioritized Planning. Furthermore, we analyze how a limited planning horizon can affect the planning cost. The results show that the proposed method offers a good trade-off between planning cost and solution quality, which results in a better success rate compared to Prioritized Planning, which sometimes does not find a solution. In addition, the algorithm is able to achieve a good solution quality with lower cost compared to the more complex CBS algorithm.

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Notes

  1. 1.

    The code is available at https://www.ci.ovgu.de/Research/Codes.html. The benchmark files are available at https://mapf.info.

  2. 2.

    We do not report the number of calls to the A* algorithm for Prioritized planning because we use a caching mechanism for the true-cost heuristic. In our implementation, this value can not be compared fairly between Prioritized Planning and CBS/CCR.

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Authors and Affiliations

  1. Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany

    Sebastian Mai & Sanaz Mostaghim

Authors
  1. Sebastian Mai
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  2. Sanaz Mostaghim
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Corresponding author

Correspondence to Sebastian Mai .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  3. University of Manchester, Manchester, UK

    Manuel López-Ibáñez

  4. University of Málaga, Málaga, Spain

    José García-Nieto

  5. Stellenbosch University, Stellenbosch, South Africa

    Andries Engelbrecht

  6. Worcester Polytechnic Institute, Worcester, MA, USA

    Carlo Pinciroli

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

  8. Université Libre de Bruxelles, Brussels, Belgium

    Christian Camacho-Villalón

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Mai, S., Mostaghim, S. (2022). Collective Decision-Making for Conflict Resolution in Multi-Agent Pathfinding. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2022. Lecture Notes in Computer Science, vol 13491. Springer, Cham. https://doi.org/10.1007/978-3-031-20176-9_7

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  • DOI: https://doi.org/10.1007/978-3-031-20176-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20175-2

  • Online ISBN: 978-3-031-20176-9

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