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Particle swarm optimization algorithms for autonomous robots with deterministic leaders using space filling movements

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Abstract

In this work the swarm behavior principles of Craig W. Reynolds are combined with deterministic traits. This is done by using leaders with motions based on space filling curves like Peano and Hilbert. Our goal is to evaluate how the swarm of agents works with this approach, supposing the entire swarm will better explore the entire space. Therefore, we examine different combinations of Peano and Hilbert with the already known swarm algorithms and test them in a practical challenge for the harvesting of manganese nodules on the sea ground with the use of autonomous agents. We run experiments with various settings, then evaluate and describe the results. In the last section some further development ideas and thoughts for the expansion of this study are considered.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences, 1 Nibelungenplatz, Frankfurt am Main, 60318, Germany

    Doina Logofătu, Christina Andersson & Kristiyan Balabanov

  2. Faculty of Industrial Engineering and Management, Technion—Israel Institute of Technology, Haifa, 3200003, Israel

    Gil Sobol

  3. Department of Computing, Goldsmiths College, University of London, London, SE146NW, UK

    Daniel Stamate

  4. Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gabriela Narutowicza 11/12, Gdańsk, 80-233, Poland

    Tymoteusz Cejrowski

Authors
  1. Doina Logofătu
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  2. Gil Sobol
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  3. Christina Andersson
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  4. Daniel Stamate
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  5. Kristiyan Balabanov
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  6. Tymoteusz Cejrowski
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Correspondence to Doina Logofătu.

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Logofătu, D., Sobol, G., Andersson, C. et al. Particle swarm optimization algorithms for autonomous robots with deterministic leaders using space filling movements. Evolving Systems 11, 383–396 (2020). https://doi.org/10.1007/s12530-018-9245-9

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  • DOI: https://doi.org/10.1007/s12530-018-9245-9

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