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Hybrid Control of Swarms for Resource Selection

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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 design and control of swarm robotics systems generally relies on either a fully self-organizing approach or a completely centralized one. Self-organization is leveraged to obtain systems that are scalable, flexible and fault-tolerant at the cost of reduced controllability and performance. Centralized systems, instead, are easier to design and generally perform better than self-organizing ones but come with the risks associated with a single point of failure. We investigate a hybrid approach to the control of robot swarms in which a part of the swarm acts as a control entity, estimating global information, to influence the remaining robots in the swarm and increase performance. We investigate this concept by implementing a consensus achievement system tasked with choosing the best of two resource locations. We show (i) how estimating and leveraging global information impacts the decision-making process and (ii) how the proposed hybrid approach improves performance over a fully self-organizing approach.

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Notes

  1. 1.

    In the paper we use the terms ‘robot opinion’ and ‘robot preference’ interchangeably.

  2. 2.

    A swarm is connected if a path of communicating robots can be found between any two robots in the swarm.

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Acknowledgements

Gabriele Valentini acknowledges support from the NSF grant No. PHY-1505048. Marco Dorigo acknowledges support from the Belgian F.R.S.-FNRS, of which he is a Research Director. The work presented in this paper was partially supported by the FLAG-ERA project RoboCom++ and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 681872).

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Authors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium

    Marco Trabattoni & Marco Dorigo

  2. School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA

    Gabriele Valentini

Authors
  1. Marco Trabattoni
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  2. Gabriele Valentini
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  3. Marco Dorigo
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Correspondence to Marco Trabattoni .

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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Trabattoni, M., Valentini, G., Dorigo, M. (2018). Hybrid Control of Swarms for Resource Selection. 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_5

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  • DOI: https://doi.org/10.1007/978-3-030-00533-7_5

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