Development and Implementation of an Artificial Neural Network based Controller for Gait Balance of a Biped Robot

Abstract

This paper proposes a gait balancing controller for a biped robot. The controller was designed based on a back- propagation artificial neural network (BPANN). Because of the on-line learning ability of BPANN, it allows the controller to generate advisable corrections of each joint for robotic balance according to the signals of gyroscopes. It results in a balanced locomotion whenever the biped robot is walking or standing upon the uneven terrain. There are four experiments of robotic locomotion in different postures and grounds applied to verify whether the controls of robotic gait balance are satisfactory.