이족 휴머노이드 로봇의 유연한 보행을 위한 학습기반 뉴로-퍼지시스템의 응용

Biped locomotion is a popular research area in robotics due to the high adaptability of a walking robot in an unstructured environment. When attempting to automate the motion planning process for a biped walking robot, one of the main issues is assurance of dynamic stability of motion. This can be categorized into three general groups: body stability, body path stability, and gait stability. A zero moment point (ZMP), a point where the total forces and moments acting on the robot are zero, is usually employed as a basic component for dynamically stable motion. In this paper, learning based neuro-fuzzy systems have been developed and applied to model ZMP trajectory of a biped walking robot. As a result. we can provide more improved insight into physical walking mechanisms.