Abstract
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 10̂ 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.
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