HUMAN-LIKE GAIT GENERATION FOR BIPED ANDROID ROBOT USING MOTION CAPTURE AND ZMP MEASUREMENT SYSTEM

Abstract

This article proposes a novel strategy to generate both a human-like walking pattern and a human-like zero moment point (ZMP) trajectory for a biped android robot. In general, the motion-capture technique has been widely utilized to obtain a walking pattern that is kinematically similar to the walking of a human. However, in addition to kinematic considerations, a suitable ZMP shaping technique is necessary to apply the human gait derived by motion capturing to biped robots more effectively. In previous research by the authors, a walking pattern generation strategy was developed considering the kinematics using motion capturing and Fourier fitting. However, it was found that there were differences between the calculated ZMP trajectory of the earlier research and the measured ZMP trajectory directly derived from the sensor in this research. Therefore, the differences and their factors are analyzed and a new strategy is proposed that effectively reduces the differences between them. Finally, the proposed strategy is shown to be effective for generating human-like walking pattern and ZMP trajectory for biped android robots through stick figure simulations.