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Load Balancing in Distributed Multi-Agent Path Finder (DMAPF)

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Engineering Multi-Agent Systems (EMAS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14378))

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Abstract

The Multi-Agent Path Finding (MAPF) is a problem of finding a plan for agents to reach their desired locations without collisions. Distributed Multi-Agent Path Finder (DMAPF) solves the MAPF problem by decomposing a given MAPF problem instance into smaller subproblems and solve them in parallel. DMAPF works in rounds. Between two consecutive rounds, agents may migrate between two adjacent subproblems following their abstract plans, which are pre-computed, until all of them reach the areas that contain their desired locations. Previous works on DMAPF compute an abstract plan for each agent without the knowledge of other agents’ abstract plans, resulting in high congestion in some areas, especially those that act as corridors. The congestion negatively impacts the runtime of DMAPF and prevents it from being able to solve dense MAPF problems.

In this paper, we (i) investigate the use of Uniform-Cost Search to mitigate the congestion. Additionally, we explore the use of several other techniques including (ii) using timeout estimation to preemptively stop solving and relax a subproblem when it is likely to get stuck; (iii) allowing a solving process to manage multiple subproblems – aimed to increase concurrency; and (iv) integrating with MAPF solvers from the Conflict-Based Search family. Experimental results show that our new system is several times faster than the previous ones; can solve larger and denser problems that were unsolvable before; and has better runtime than PBS and EECBS, which are state-of-the-art centralized suboptimal MAPF solvers, in problems with a large number of agents.

Tran Cao Son was partially supported by NSF awards #1757207, #1914635, and #1812628.

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Notes

  1. 1.

    https://movingai.com/benchmarks/mapf/index.html.

  2. 2.

    https://github.com/Jiaoyang-Li/CBSH2-RTC.

  3. 3.

    https://github.com/Jiaoyang-Li/EECBS.

  4. 4.

    https://github.com/Jiaoyang-Li/PBS.

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Author information

Authors and Affiliations

  1. New Mexico State University, Las Cruces, NM, USA

    Poom Pianpak & Tran Cao Son

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    Jiaoyang Li

Authors
  1. Poom Pianpak
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  2. Jiaoyang Li
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  3. Tran Cao Son
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Correspondence to Poom Pianpak .

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Editors and Affiliations

  1. University of St.Gallen, St. Gallen, Switzerland

    Andrei Ciortea

  2. Utrecht University, Utrecht, The Netherlands

    Mehdi Dastani

  3. Zhejiang University, Hangzhou, China

    Jieting Luo

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Pianpak, P., Li, J., Son, T.C. (2023). Load Balancing in Distributed Multi-Agent Path Finder (DMAPF). In: Ciortea, A., Dastani, M., Luo, J. (eds) Engineering Multi-Agent Systems. EMAS 2023. Lecture Notes in Computer Science(), vol 14378. Springer, Cham. https://doi.org/10.1007/978-3-031-48539-8_9

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  • DOI: https://doi.org/10.1007/978-3-031-48539-8_9

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