JRM Vol.22 No.3 pp. 315-321
doi: 10.20965/jrm.2010.p0315


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

October 1, 2009
February 16, 2010
June 20, 2010
biological fluctuation, attractor selection model, human-like robotic arm, bio-inspired robotics
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.
Cite this article as:
A. Sugahara, Y. Nakamura, I. Fukuyori, Y. Matsumoto, and H. 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.
Data files:
  1. [1] R. S. Sutton and A. G. Barto, “Reinforcement Learning: An Introduction,” MIT Press 1998.
  2. [2] 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.
  3. [3] C. Furusawa and K. Kaneko, “Emergence of Rules in Cell Society: Differentiation, Hierarchy, and Stability,” Bullein of Mathematical Biology, pp. 659-687.
  4. [4] 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.
  5. [5] 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.
  6. [6] 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.
  7. [7] L. P. Kaelbling and W. A. Moore, “Reinforcement Learning: A Survey. Journel of Artificial Intelligence Research,” Vol.4, pp. 237-285, 1996.
  8. [8] 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.
  9. [9] D. J. C. Mackay, “Information Theory, Inference, and Learning Algorithms,” Cambridge University Press, 2002.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024