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Learning Control System of Biped Locomotive Robot Using Neural Networks
Yasuo Kurematsu,* Takashi Murai,** Takuji Maeda**
and Shinzo Kitamura**
* Department of Information Processing Engineering, College of Industrial Technology, 1-27-1 Nishikoya, Amagasaki, Hyogo, 661 Japan
** Department of Computer and Systems Engineering, Faculty of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, Hyogo, 657 Japan
Received:May 6, 1993Accepted:May 16, 1993Published:December 20, 1993
Keywords:Neural network, Biped locomotive robot, Learning control system
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.
Cite this article as:Y. Kurematsu, T. Murai, T. Maeda, and S. Kitamura, “Learning Control System of Biped Locomotive Robot Using Neural Networks,” J. Robot. Mechatron., Vol.5 No.6, pp. 542-547, 1993.Data files:
