International Journal of Control, Automation, and Systems

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

Fuzzy Hint Acquisition for the Collision Avoidance Solution of Redundant Manipulators Using Neural Network

  • Assal Samy F. M. (Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University) ;
  • Watanabe Keigo (Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University) ;
  • Izumi Kiyotaka (Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University)
  • Published : 2006.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

A novel inverse kinematics solution based on the back propagation neural network (NN) for redundant manipulators is developed for online obstacles avoidance. A laser transducer at the end-effctor is used for online planning the trajectory. Since the inverse kinematics in the present problem has infinite number of joint angle vectors, a fuzzy reasoning system is designed to generate an approximate value for that vector. This vector is fed into the NN as a hint input vector rather than as a training vector to guide the output of the NN. Simulations are implemented on both three- and four-link redundant planar manipulators to show the effectiveness of the proposed position control system.

Keywords

References

  1. S. I. Choi and B. K. Kim, 'Obstacle avoidance control for redundant manipulators using collidability measure,' Robotica, vol. 18, pp. 143-151, 2000 https://doi.org/10.1017/S0263574799001861
  2. E. G. Gilbert and D. W. Johnson, 'Distance functions and their application to robot path planning in the presence of obstacles,' IEEE J. of Robot. and Automat., vol. RA-1, no. 1, pp. 21-30, March 1985
  3. A. Guez and Z. Ahmad, 'Solution to the inverse kinematics problem in robotics by neural networks,' Proc. IEEE Int. Conf. on Neural Networks, vol. II, pp. 617-624, 1988
  4. J. A. Francisco, 'Multilayer back-propagation neural network for learning the forward and inverse kinematics equations,' Proc. Int. Joint Conf. on Neural Networks, pp. II-319-321, 1990
  5. E. S. H. Hou and W. Utama, 'An artificial neural network for redundant manipulator inverse kinematics computation,' Proc. of the SPIE- The International Society for Optical Engineering, vol. 1607, pp. 668-677, 1992
  6. M. Ziqiang and T. C. Hsia, 'Obstacle avoidance inverse kinematics solution of redundant robots by neural networks,' Robotica, vol. 15, pp. 3-10, Jan-Feb 1997 https://doi.org/10.1017/S0263574797000027
  7. M. T. H. Beheshti and A. K. Tehranl, 'Obstacle avoidance for kinematically redundant robots using an adaptive fuzzy logic algorithm,' Proc. the American Control Conf., San Diego, California, pp. 1371-1375, June 1999
  8. T. S. Wikman and W. S. Newman, 'A fast, online collision avoidance method for a kinematically redundant manipulator based on reflex control,' Proc. IEEE Int. Conf. Robot. and Automat., pp. 261-266, 1992
  9. K. Glass, R. Colbaugh, D. Lim, and H. Seraji, 'Real-time collision avoidance for redundant manipulators,' IEEE Trans. on Robot. and Automat., vol. 11, no. 3, pp. 448-257, June 1995 https://doi.org/10.1109/70.388789
  10. H. Seraji, M. K. Long, and T. S. Lee, 'Motion control of 7-DOF arms: The configuration control approach,' IEEE Trans. on Robot. and Automat., vol. 9, no. 2, pp. 125-139, April 1993 https://doi.org/10.1109/70.238277
  11. W. J. Cho and D. S. Kwon, 'A sensor-based obstacle avoidance for a redundant manipulator using a velocity potential function,' Proc. IEEE Int. Workshop on Robot and Human Communication, pp. 306-310, 1996
  12. L. Zlajpah and B. Nemec, 'Kinematic control algorithms for on-line obstacle avoidance for redundant manipulators,' Proc. IEEE/RSJ Int.Conf. on Intelligent Robots and Systems, Lausanne, pp. 1898-1903, 2002
  13. V. Perdereau, C. Passi, and M. Drouin, 'Realtime control of redundant robotic manipulators for mobile obstacle avoidance,' Robotics and Autonomous Systems, vol. 41, pp. 41-59, 2002 https://doi.org/10.1016/S0921-8890(02)00274-9
  14. W. S. Tang, C. M. L. Lam, and J. Wang, 'Kinematic control and obstacle avoidance for redundant manipulators using a recurrent neural network,' Proc. Int. Conf. on Artificial Neural Networks, pp. 922-929, 2001 https://doi.org/10.1007/3-540-44668-0_127
  15. J. Wang, Q. Hu, and D. Jiang, 'A Lagrangian network for kinematic control of redundant robot manipulators,' IEEE Trans. on Neural Networks, vol. 10, no. 1, pp. 1123-1132, 1999 https://doi.org/10.1109/72.788651
  16. Y. Zhang and J. Wang, 'Obstacle avoidance for kinematically redundant manipulators using a dual neural network,' IEEE Trans. on Syst., Man, and Cyber., vol. 34, no. 1, pp. 752-759, Feb. 2004 https://doi.org/10.1109/TSMCB.2003.811519
  17. R. P. Paul, Robot Manipulators: Mathematics, Programming, and Control, MIT Press, Cambridge, MA, 1981
  18. Y. S. Abu-Mostafa, 'A method for learning from hints,' Advances in Neural Information Processing Systems 5, pp. 73-80, 1993
  19. M. Riedmiller and H. Braun, 'A direct adaptive method for faster backpropagation learning: The RPROP algorithm,' Proc. IEEE Int. Conf. on Neural Networks, San Francisco, pp. 586-591, 1993
  20. J. S. R. Jang and C. T. Sun, 'Neuro-fuzzy modeling and control,' Proc. of the IEEE, vol. 83, no. 3, pp. 378-406, March 1995 https://doi.org/10.1109/5.364486
  21. S. Haykin, Neural Networks: A Comprehensive Foundation, Macmillan, New York, 1994
  22. F. L. Lewis, S. Jagannathan, and A. Yesidirek, Neural Network Control of Robot Manipulators and Nonlinear Systems, Taylor & Francis, 1999
  23. T. Yoshikawa, 'Analysis and control of robot manipulators with redundancy,' in Robotics Research: The First International Symposium M. Brady and R. Paul, Eds., MIT Press, Cambridge, MA, pp. 735-748, 1984
  24. A. A. Maciejewski and C. A. Klein, 'Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments,' Int. J. of Robotic Research, vol. 4, no. 3, pp.109-117, 1985 https://doi.org/10.1177/027836498500400308