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Publication - Professor Jonathan Lawry

    Robust distributed decision-making in robot swarms

    Exploiting a third truth state

    Citation

    Crosscombe, M, Lawry, J, Hauert, S & Homer, M, 2018, ‘Robust distributed decision-making in robot swarms: Exploiting a third truth state’. in: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017): Proceedings of a meeting held 24-28 September 2017, Vancouver, British Columbia, Canada. Institute of Electrical and Electronics Engineers (IEEE), pp. 4326-4332

    Abstract

    In this paper, we investigate the best-of-n distributed decision problem in robot swarms. In this context, we compare the weighted voter model with a three-valued model that incorporates an intermediate belief state meaning either 'uncertain' or 'indifferent'. We focus particularly on the trade-off between speed of convergence to a shared belief, and robustness to the presence of unreliable individuals in the population. By means of both simulation and embodied experiments in real robot swarms of 400 Kilobots, we show that the three-valued model is much more robust than the weighted voter model, but with decreased speed of convergence.

    Full details in the University publications repository