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Modeling Pathfinding for Swarm Robotics

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Swarm Intelligence (ANTS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12421))

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Abstract

This paper presents a theoretical model for path planning in multi-robot navigation in swarm robotics. The plans for the paths are optimized using two objective functions, namely to maximize the safety distance between the agents and to minimize the mean time to complete a plan. The plans are designed for various vehicle models. The presented path planning model allows us to evaluate both decentralized and centralized planners. In this paper, we focus on decentralized planners and aim to find a set of Pareto-optimal plans, which enables us to investigate the fitness landscape of the problem. For solving the multi-objective problem, we design a modified version of NSGA-II algorithm with adapted operators to find sets of Pareto-optimal paths for several agents using various vehicle models and environments. Our experiments show that small problem instances can be solved well, while solving larger problems is not always possible due to the large complexity.

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Notes

  1. 1.

    A different technique to bound the search space is to plan only for a fixed time-frame, however, this means a new, problem-specific parameter needs to be introduced.

  2. 2.

    Start- and goal configuration are fixed and never affected by the crossover operation.

  3. 3.

    We did exactly 31 runs, so the median fitness (and commonly used quantiles) correspond to a specific run.

  4. 4.

    More vehicle models and source code: http://www.ci.ovgu.de/Research/Codes.html.

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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. Université Libre de Bruxelles, Brussels, Belgium

    Thomas Stützle

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Maria J. Blesa

  4. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  5. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  6. Université Libre de Bruxelles, Brussels, Belgium

    Mary Katherine Heinrich

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

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Mai, S., Mostaghim, S. (2020). Modeling Pathfinding for Swarm Robotics. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_15

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  • DOI: https://doi.org/10.1007/978-3-030-60376-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60375-5

  • Online ISBN: 978-3-030-60376-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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