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On the evolution of adaptable and scalable mechanisms for collective decision-making in a swarm of robots

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Abstract

A swarm of robots can collectively select an option among the available alternatives offered by the environment through a process known as collective decision-making. This process is characterised by the fact that once the group makes a decision, it can not be attributed to any of its group members. In swarm robotics, the individual mechanisms for collective decision-making are generally hand-designed and limited to a restricted set of solutions based on the voter or the majority model. In this paper, we demonstrate that it is possible to take an alternative approach in which the individual mechanisms are implemented using artificial neural network controllers automatically synthesised using evolutionary computation techniques. We qualitatively describe the group dynamics underlying the decision process on a collective perceptual discrimination task. We carry out extensive comparative tests that quantitatively evaluate the performance of the most commonly used decision-making mechanisms (voter model and majority model) with the proposed dynamic neural network model under various operating conditions and for swarms that differ in size. The results of our study clearly indicate that the performances of a swarm employing dynamical neural networks as the decision-making mechanism are more robust, more adaptable to a dynamic environment, and more scalable to a larger swarm size than the performances of the swarms employing the voter and the majority model. These results, generated in simulation, are ecologically validated on a swarm of physical e-puck2 robots.

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Notes

  1. Videos of the physical robot experiments can be found at this link.

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Acknowledgements

Ahmed Almansoori thanks the University of Namur (BE) for funding his PhD. Muhanad Alkilabi thanks the SPW Wallonia Region (BE) for funding his research.

Funding

Ahmed Almansoori is funded by the CERUNA fellowship offered by the University of Namur (BE). Muhanad Alkilabi is funded by the SPW Beware fellowship by the Wallonian Region, Belgium.

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Authors and Affiliations

  1. Faculty of Computer Science, University of Namur, Rue de Bruxelles 61, 5000, Namur, Belgium

    Ahmed Almansoori, Muhanad Alkilabi & Elio Tuci

  2. Faculty of Engineering, University of Kerbala, Karbala, Iraq

    Ahmed Almansoori & Muhanad Alkilabi

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  1. Ahmed Almansoori
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  2. Muhanad Alkilabi
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Contributions

A.A. carried out the experiments (Simulations and Physical robots), A.A. and E.T. performed the analysis and the statistical tests. E.T. and A.A. wrote the manuscript with support from M.A. M. A. and E.T. developed the simulator. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Ahmed Almansoori.

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Almansoori, A., Alkilabi, M. & Tuci, E. On the evolution of adaptable and scalable mechanisms for collective decision-making in a swarm of robots. Swarm Intell 18, 79–99 (2024). https://doi.org/10.1007/s11721-023-00233-4

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