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Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm

Jean-Marc Montanier 1, 2 Nicolas Bredeche 1, 2
1 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 deals with online onboard behavior optimization for a autonomous mobile robot in the scope of the European FP7 Symbrion Project. The work presented here extends the (1+1)-online algorithm introduced in [4]. The (1+1)-online algorithm has a limitation regarding the ability to perform global search whenever a local optimum is reached. Our new implementation of the algorithm, termed (1+1)-restart-online algorithm, addresses this issue and has been successfully experimented using a Cortex M3 microcontroller connected to a realistic robot simulator as well as within an autonomous robot based on an Atmel ATmega128 microcontroller. Results from the experiments show that the new algorithm is able to escape local optima and to perform behavior optimization in a complete autonomous fashion. As a consequence, it is able to converge faster and provides a richer set of relevant controllers compared to the previous implementation.
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Jean-Marc Montanier, Nicolas Bredeche. Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm. Springer Series: Studies in Computational Intelligence. New Horizons in Evolutionary Robotics, Springer, pp.155-169, 2011. ⟨inria-00566898⟩

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