Neural Processing Letters

, Volume 38, Issue 2, pp 261–279

Gait Pattern Based on CMAC Neural Network for Robotic Applications

Authors

    • Images, Signals and Intelligence Systems Laboratory (LISSI/EA 3956), Senart-Fontainebleau Institute of TechnologyUPEC University
  • Weiwei Yu
    • School of Mechatronic EngineeringNorthwestern Polytechnical University
  • Kurosh Madani
    • Images, Signals and Intelligence Systems Laboratory (LISSI/EA 3956), Senart-Fontainebleau Institute of TechnologyUPEC University
Article

DOI: 10.1007/s11063-012-9257-6

Cite this article as:
Sabourin, C., Yu, W. & Madani, K. Neural Process Lett (2013) 38: 261. doi:10.1007/s11063-012-9257-6

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.

Keywords

CMAC neural networkFuzzy CMACGait pattern

Copyright information

© Springer Science+Business Media New York 2012