NDLI: An empirical exploration of a neural oscillator for biped locomotion control

Content Provider	IEEE Xplore Digital Library
Author	Endo, G. Morimoto, J. Nakanishi, J. Cheng, G.
Copyright Year	2004
Description	Author affiliation: Sony Corp., Tokyo, Japan (Endo, G.)
Abstract	Humanoid research has made remarkable progress during the past 10 years. However, currently most humanoids use the target ZMP (zero moment point) control algorithm for bipedal locomotion, which requires precise modeling and actuation with high control gains. On the contrary, humans do not rely on such precise modeling and actuation. Our aim is to examine biologically related algorithms for bipedal locomotion that resemble human-like locomotion. This paper describes an empirical study of a neural oscillator for the control of biped locomotion. We propose a new neural oscillator arrangement applied to a compass-like biped robot. Dynamic simulations and experiments with a real biped robot were carried out and the controller performs steady walking for over 50 steps. Gait variations resulting in energy efficiency was made possible through the adjustment of only a single neural activity parameter. Aspects of adaptability and robustness of our approach are shown by allowing the robot to walk over terrains with varying surfaces with different frictional properties. Initial results suggesting optimal amplitude for dealing with perturbation are also presented.
Sponsorship	IEEE Robotics and Automation Soc
Starting Page	3036
Ending Page	3042
File Size	662784
Page Count	7
File Format	PDF
ISBN	0780382323
ISSN	10504729
DOI	10.1109/ROBOT.2004.1307523
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-04-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Oscillators Legged locomotion Humans Biological system modeling Robots Joints Muscles Laboratories Information processing Neuroscience
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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