Gait Pattern Based on CMAC Neural Network for Robotic Applications

C. Sabourin 1 W. Yu K. Madani 1
1 SYNAPSE
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
Abstract : The main goal of this paper is to provide a general methodology and a practical approach for the design of gait pattern for biped robotic applications directly usable by researchers and engineers. This approach, which is based on CMAC neural network, is an alternative way in comparison to the traditional Central Pattern Generator. In the proposed method, the CMAC neural networks are used to learn basic motions (e.g. reference gait) and a Fuzzy Inference System allows to merge these reference motions in order to built more complex gaits. The results of our biped robotic applications show how to design a self-adaptive gait pattern according to average velocity and external perturbations.
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Neural Processing Letters, Springer, 2013, 38 (2), pp.261‑279. 〈10.1007/s11063-012-9257-6〉
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https://hal.archives-ouvertes.fr/hal-01568426
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Soumis le : mardi 25 juillet 2017 - 11:43:13
Dernière modification le : vendredi 15 février 2019 - 17:51:33

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C. Sabourin, W. Yu, K. Madani. Gait Pattern Based on CMAC Neural Network for Robotic Applications. Neural Processing Letters, Springer, 2013, 38 (2), pp.261‑279. 〈10.1007/s11063-012-9257-6〉. 〈hal-01568426〉

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