Generating Circular Motion of a Human-Like Robotic Arm Using Attractor Selection Model
Atsushi Sugahara, Yutaka Nakamura, Ippei Fukuyori,
Yoshio Matsumoto, and Hiroshi Ishiguro
Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan
Since animals have survived in unstructured environments, it would be beneficial to refer to animals to develop a robot that operate practical tasks. In this research, we developed a human-like robotic arm imitating the anatomy of human upper limb. Although human can control his arm flexibly and robustly, controlling such complex system by existing control methods would be difficult because of its complexity. In this paper, we propose a simple but flexible control mechanism inspired by a biological adaptation mechanism called “yuragi.” We applied our proposed method to the control of the robot, and experimental results show that our proposed method is applicable to the control of a robot with complex structure.
Yoshio Matsumoto, and Hiroshi Ishiguro, “Generating Circular Motion of a Human-Like Robotic Arm Using Attractor Selection Model,” J. Robot. Mechatron., Vol.22, No.3, pp. 315-321, 2010.
-  R. S. Sutton and A. G. Barto, “Reinforcement Learning: An Introduction,” MIT Press 1998.
-  G. Taga, Y. Yamaguchi, and H. Shimizu. “Self-organized control of biped locomotion by neural oscillator in unpredictable environment,” Biological Cybernetics, Vol.65, pp. 147-159, 1991.
-  C. Furusawa and K. Kaneko, “Emergence of Rules in Cell Society: Differentiation, Hierarchy, and Stability,” Bullein of Mathematical Biology, pp. 659-687.
-  T. Yanagida, M. Ueda, T. Murata, S. Esaki, and Y. Ishii, “Brown motion, fluctuation and life. Biosystems,” Vol.88, No.3, pp. 228-242, 2006.
-  A. Kashiwagi, I. Urabe, K. Kaneko, and T. Yomo, “Adaptive response of a gene network to environment changes by fitnessinduced attractor selection,” PLos ONE, 1, 2006.
-  E. Rimon and D. E. Koditschek. “Exact robot navigation using artificial potential functions,” IEEE Trans. on Robotics and Automation, Vol.8, pp. 501-518, 1992.
-  L. P. Kaelbling and W. A. Moore, “Reinforcement Learning: A Survey. Journel of Artificial Intelligence Research,” Vol.4, pp. 237-285, 1996.
-  I. Fukuyori, Y. Nakamura, Y. Matsumoto, and H. Ishiguro. “Flexible control mechanism for multi-DOF robotic arm based on biological fluctuation,” Int. Conf. on the simulation of adaptive behavior, pp. 22-31, 2008.
-  D. J. C. Mackay, “Information Theory, Inference, and Learning Algorithms,” Cambridge University Press, 2002.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.
Copyright© 2010 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.