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Simulating Kilobots Within ARGoS: Models and Experimental Validation

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Swarm Intelligence (ANTS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11172))

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Abstract

The Kilobot is a popular platform for swarm robotics research due to its low cost and ease of manufacturing. Despite this, the effort to bootstrap the design of new behaviours and the time necessary to develop and debug new behaviours is considerable. To make this process less burdensome, high-performing and flexible simulation tools are important. In this paper, we present a plugin for the ARGoS simulator designed to simplify and accelerate experimentation with Kilobots. First, the plugin supports cross-compiling against the real robot platform, removing the need to translate algorithms across different languages. Second, it is highly configurable to match the real robot behaviour. Third, it is fast and allows running simulations with several hundreds of Kilobots in a fraction of real time. We present the design choices that drove our work and report on experiments with physical robots performed to validate simulated behaviours.

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Notes

  1. 1.

    Kilobots are open-hardware and in Europe are produced and sold by K-Team Corporation (see https://www.k-team.com).

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Acknowledgments

This work was partially supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under Grant 647704 to James Marshall. Vito Trianni acknowledges support from the project DICE (FP7 Marie Curie Career Integration Grant, ID: 631297). The authors thank Alex Cope for assistance in the preparation of Fig. 3.

Author information

Authors and Affiliations

  1. Robotics Engineering, Worcester Polytechnic Institute, Worcester, MA, USA

    Carlo Pinciroli

  2. Department of Computer Science, University of Sheffield, Sheffield, UK

    Mohamed S. Talamali, Andreagiovanni Reina & James A. R. Marshall

  3. ISTC, National Research Council, Rome, Italy

    Vito Trianni

Authors
  1. Carlo Pinciroli
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  2. Mohamed S. Talamali
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  3. Andreagiovanni Reina
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  4. James A. R. Marshall
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  5. Vito Trianni
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Corresponding author

Correspondence to Carlo Pinciroli .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Mauro Birattari

  3. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  4. University of Southern Denmark, Odense, Denmark

    Anders L. Christensen

  5. University of Sheffield, Sheffield, UK

    Andreagiovanni Reina

  6. National Research Council, Rome, Italy

    Vito Trianni

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Pinciroli, C., Talamali, M.S., Reina, A., Marshall, J.A.R., Trianni, V. (2018). Simulating Kilobots Within ARGoS: Models and Experimental Validation. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A., Reina, A., Trianni, V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science(), vol 11172. Springer, Cham. https://doi.org/10.1007/978-3-030-00533-7_14

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  • DOI: https://doi.org/10.1007/978-3-030-00533-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00532-0

  • Online ISBN: 978-3-030-00533-7

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