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Robot Swarms Break Decision Deadlocks in Collective Perception Through Cross-Inhibition

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Swarm Intelligence (ANTS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13491))

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Abstract

We study how robot swarms can achieve a consensus on the best among a set of n possible options available in the environment. While the robots rely on local communication with one another, follow simple rules, and make estimates of the option’s qualities subject to measurement errors, the swarm as a whole is able to make accurate collective decisions. We compare the performance of two prominent decision-making algorithms that are based, respectively, on the direct-switching and the cross-inhibition models, both of which are well-suited for simplistic robots. Most studies used these models to let robots achieve consensus by solely relying on social interactions and ignored the aspect of enabling robots to self-source information from the environment. However, in order to select the best option, we deem sampling environmental information crucial for the successful performance of the task. Through robot-swarm simulations, we show that swarms programmed with the direct-switching model are only able to make consensus decisions in asymmetric environments where options have different quality values. Instead, using cross-inhibition, the robot swarm can also break decision deadlocks and reach a consensus in symmetric environments with equal quality options. We investigate the mechanistic causes of such differences and we find that the time the robots spend in a state of indecision is a key parameter to break the symmetry. This research highlights the importance of considering both social and environmental information when studying collective decision-making.

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Acknowledgements

The authors thank Till Aust and Jonas Kuckling for the technical support on simulating the Kilogrid and running simulations on the HPC. This work was supported by Service Public de Wallonie Recherche under grant n\(^\circ \) 2010235 - ARIAC by DigitalWallonia4.AI. M. Dorigo and A. Reina acknowledge support from the Belgian F.R.S.-FNRS, of which they are Research Director and Chargé de Recherches, respectively.

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Authors and Affiliations

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

    Raina Zakir, Marco Dorigo & Andreagiovanni Reina

Authors
  1. Raina Zakir
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  2. Marco Dorigo
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  3. Andreagiovanni Reina
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Corresponding author

Correspondence to Raina Zakir .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  3. University of Manchester, Manchester, UK

    Manuel López-Ibáñez

  4. University of Málaga, Málaga, Spain

    José García-Nieto

  5. Stellenbosch University, Stellenbosch, South Africa

    Andries Engelbrecht

  6. Worcester Polytechnic Institute, Worcester, MA, USA

    Carlo Pinciroli

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

  8. Université Libre de Bruxelles, Brussels, Belgium

    Christian Camacho-Villalón

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Zakir, R., Dorigo, M., Reina, A. (2022). Robot Swarms Break Decision Deadlocks in Collective Perception Through Cross-Inhibition. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2022. Lecture Notes in Computer Science, vol 13491. Springer, Cham. https://doi.org/10.1007/978-3-031-20176-9_17

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  • DOI: https://doi.org/10.1007/978-3-031-20176-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20175-2

  • Online ISBN: 978-3-031-20176-9

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