Learning Control System of Biped Locomotive Robot Using Neural Networks

Abstract

The authors are studying the autonomous walking trajectory generation of a biped locomotive robot using the system consisting of an inverted pendulum equation and neural networks. This paper uses the trajectory generation system to simulate and to verify how the robot reacts to a change in its initial posture or the initial weight coefficient of a multi-layered neural network or an addition of disturbances during walking. The simulation test showed that the initial posture of the robot mainly determined a success in walking as well as a gait and that some disturbances did not prevent the robot from walking.