International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지

Self-Learning Control of Cooperative Motion for Humanoid Robots

  • Hwang, Yoon-Kwon (School of Mechatronics, Changwon National University) ;
  • Choi, Kook-Jin (Department of Mechanical Design and Manufacturing, Changwon National University) ;
  • Hong, Dae-Sun (Department of Mechanical Design and Manufacturing, Changwon National University)
  • Published : 2006.12.30
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Abstract

This paper deals with the problem of self-learning cooperative motion control for the pushing task of a humanoid robot in the sagittal plane. A model with 27 linked rigid bodies is developed to simulate the system dynamics. A simple genetic algorithm(SGA) is used to find the cooperative motion, which is to minimize the total energy consumption for the entire humanoid robot body. And the multi-layer neural network based on backpropagation(BP) is also constructed and applied to generalize parameters, which are obtained from the optimization procedure by SGA, in order to control the system.

Keywords

References

  1. D. E. Orin and S. Y. Oh, 'Control of force distribution in robotic manipulator mechanisms containing closed kinematic chains,' Journal of Dynamics Systems, Measurement, and Control, vol. 102, pp. 134-141, 1981
  2. T. Yoshikawa, 'Dynamic manipulability of robot manipulators,' Journal of Robotic Systems, vol. 2, no. 1, pp. 113-124,1985
  3. Y. F. Zheng and I. Y. S. Luh, 'Joint torque for control of two coordinating moving robots,' Proc. of IEEE Int. Conf on Robotics and Automation, pp. 1375-1380, 1986
  4. Y. F. Zheng and Q. Yin, 'Coordinating multi1imbed robots for generating large cartesian force,' Proc. of Int. Conf on Robotics and Automation, pp. 1653-1658, 1990
  5. C. Su and Y. F. Zheng, 'Task decomposition for multilimbed robots to work in the reachable-butunorientable space,' Proc. of IEEE Int. Conf on Robotics and Automation, pp. 1659-1664, 1990
  6. Y. Yokokohji, S. Nomoto, and T. Yoshikawa, 'Static evaluation of humanoid robot postures constrained to the surrounding environment through their limbs,' Proc. of IEEE Int. Conf on Robotics and Automation, pp. 1856-1863, 2002
  7. H. Yoshida, K. Inoue, T. Arai, and Y. Mae, 'Mobile manipulation of humanoid robots Optimal posture for generating large force based on statics-,' Proc. of IEEE Int. Conf on Robotics and Automation, pp. 2271-2276, 2002
  8. J. H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Michigan, 1975
  9. M. Mitchell, An Introduction to Genetic Algorithm, Massachusetts Institute of Technology, 1996
  10. M. T. Hagan, H. B. Demuth, and M. Beale, Neural Network Design, PWS Publishing Co., 1995
  11. A. Konno, N. Kato, S. Shirata, T. Furuta, and M.Uchiyama, 'Development of a light-weight biped humanoid robot,' Proc. of IEEE/RSJ Int. Conf on Intelligent Robots and Systems, pp. 1565-1570,2000
  12. D. E. Rosenthal, 'An order n formulation for robotic system,' The Journal of the Astronautical Sciences, vol. 38, no. 4, pp. 511529, 1990
  13. Y. K. Hwang, E. Inohira, A. Konno, and M.Uchiyama, 'An order n dynamic simulator for a humanoid robot with a virtual spring-damper contact model,' Proc. of IEEE Int. Conf on Robotics and Automation, pp. 31-36,2003
  14. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, AddisonWesley Publishing Co. Inc., NY, 1989