On-board Evolutionary Algorithm and Off-line Rule Discovery for Column Formation in Swarm Robotics

Asuki Kuno 1 Jean-Marc Montanier 2, 3 Shigeru Takano 4 Nicolas Bredeche 2, 3 Marc Schoenauer 2, 5 Michèle Sebag 2, 3 Enoshin Suzuki 1
2 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : This paper aims at building autonomous controllers for swarm robots, specifically aimed at enforcing a given shape formation, here a column formation. The proposed approach features two main characteristics. Firstly, a state-of-the-art evolutionary setting is used to achieve the on-board optimization of the controller, avoiding any simulator-based approach. Secondly, as the cost of physical experiments might be prohibitively high for plain evolutionary approaches, a data mining approach is achieved on the top of evolution; rule discovery is used to discover the most promising regions in the controller search space. The merits of the approach are experimentally validated using a 5 robot formation, showing that the hybrid evolutionary learning process outperforms evolution alone in terms of swarm speed and shape quality.
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Communication dans un congrès
IEEE/ACM/WIC International Conference on Intelligent Agent Technology, Aug 2011, Lyon, France. 2011
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Asuki Kuno, Jean-Marc Montanier, Shigeru Takano, Nicolas Bredeche, Marc Schoenauer, et al.. On-board Evolutionary Algorithm and Off-line Rule Discovery for Column Formation in Swarm Robotics. IEEE/ACM/WIC International Conference on Intelligent Agent Technology, Aug 2011, Lyon, France. 2011. 〈inria-00601785〉

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