By Topic

Zero-moment point trajectory modelling of a biped walking robot using an adaptive neuro-fuzzy system

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $33
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
D. Kim ; Dept. of Electr. Eng., Korea Univ., Seoul, South Korea ; S. -J. Seo ; G. -T. Park

A bipedal architecture is highly suitable for a robot built to work in human environments since such a robot will find avoiding obstacles a relatively easy task. However, the complex dynamics involved in the walking mechanism make the control of such a robot a challenging task. The zero-moment point (ZMP) trajectory in the robot's foot is a significant criterion for the robot's stability during walking. If the ZMP could be measured on-line then it becomes possible to create stable walking conditions for the robot and here also stably control the robot by using the measured ZMP, values. ZMP data is measured in real-time situations using a biped walking robot and this ZMP data is then modelled using an adaptive neuro-fuzzy system (ANFS). Natural walking motions on flat level surfaces and up and down a 10° slope are measured. The modelling performance of the ANFS is optimized by changing the membership functions and the consequent part of the fuzzy rules. The excellent performance demonstrated by the ANFS means that it can not only be used to model robot movements but also to control actual robots.

Published in:

IEE Proceedings - Control Theory and Applications  (Volume:152 ,  Issue: 4 )