Gabriele Valentini,1
Davide Brambilla,2
Heiko Hamann,3 and
Marco Dorigo1 (2015)
1IRIDIA, Université Libre de Bruxelles, Belgium
2Department of Computer Science, Politecnico di Milano, Milan, Italy
3Department of Computer Science, University of Paderborn, Paderborn, Germany
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Table of Contents |
In order to be effective, collective decision-making strategies need to be not only fast and accurate, but sufficiently general to be ported and reused across different problem domains. In this paper, we propose a novel problem scenario, collective perception, and use it to compare two previously proposed collective decision-making strategies, the DMMD and DMVD strategies. The robots are required to explore their environment, estimate the abundance of certain features, and collectively perceive which feature is the most frequent. We implemented the collective perception scenario in a swarm robotics system composed of 20 e-pucks and performed robot experiments with all considered strategies. Additionally, we also deepened our study by means of physics-based simulations. The results of our performance comparison in the collective perception scenario are in agreement with previous results for a different problem domain and support the generality of the considered strategies.
The following video is a high definition version of one experimental run performed with 20 e-pucks. In this experiment, an individual robot applies the majority rule.
Video recordings of all the robot experiments performed for the main paper. Video are subdivided by scenario (simple and difficult), for each of which are shown the results obtained using the three strategies considered (DMMD, DMVD, and DC).