Real time gait generation for autonomous humanoid robots: A case study for walking

As autonomous humanoid robots assume more important roles in everyday life, they are expected to perform many different tasks and quickly adapt to unknown environments. Therefore, humanoid robots must generate quickly the appropriate gait based on information received from visual system. In this wor...

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Bibliographic Details
Published inRobotics and autonomous systems Vol. 42; no. 2; pp. 107 - 116
Main Authors Capi, Genci, Nasu, Yasuo, Barolli, Leonard, Mitobe, Kazuhitsa
Format Journal Article
LanguageEnglish
Published Elsevier B.V 28.02.2003
Subjects
Genetic algorithm
Humanoid robots
RBF neural network
Real time gait generation
RBF neural network
Genetic algorithm
Humanoid robots
Real time gait generation
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Summary:As autonomous humanoid robots assume more important roles in everyday life, they are expected to perform many different tasks and quickly adapt to unknown environments. Therefore, humanoid robots must generate quickly the appropriate gait based on information received from visual system. In this work, we present a new method for real time gait generation during walking based on Neural Networks. The minimum consumed energy gaits similar with human motion, are used to teach the Neural Network. After supervised learning, the Neural Network can quickly generate the humanoid robot gait. Simulation and experimental results utilizing the “Bonten-Maru I” humanoid robot show good performance of the proposed method.
ISSN:0921-8890
1872-793X
DOI:10.1016/S0921-8890(02)00351-2