RBF Neural Network Based Shape Control of Hyper-redundant Manipulator with Constrained End-Effector

  • Jinguo Liu
  • Yuechao Wang
  • Shugen Ma
  • Bin Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


Hyper-redundant manipulator has more degrees of freedom than the least necessary to perform a given task, thus it has the features of overcoming conventional industrial robot’s limitation to carry out a designated difficult task. When the manipulator carries out the missions such as brushing or writing on a surface, drilling or inspection in a hole, the end-effector of the manipulator usually has both position and orientation requirement. Effective control of the hyper-redundant manipulator with such constrained end-effector is difficult for its redundancy. In this paper, a novel approach based on RBF neural network has been proposed to kinematically control the hyper-redundant manipulator. This technique, using variable regular polygon and RBF neural networks models, is completely capable of solving the control problem of a planar hyper-redundant manipulator with any number of links following any desired direction and path. With the shape transformation of variable regular polygon, the manipulator’s configuration changes accordingly and moves actively to perform the tasks. Compared with other methods to our knowledge, this technique has such superiorities as fewer control parameters and higher precision. Simulations have demonstrated that this control technique is available and effective.


Radial Basis Function Neural Network Redundant Manipulator Radial Basis Function Neural Network Model Single Neural Network Orientation Requirement 
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  1. 1.
    Xiong, Y.: Robotics. China Mechanical Press (in Chinese)(1993)Google Scholar
  2. 2.
    Zhang, Y., Wang, J., Xu, Y.: A dual neural network for bi-criteria kinematic control redundant manipulators. IEEE Transactions on Robotics and Automation 18(6), 923–931 (2002)CrossRefGoogle Scholar
  3. 3.
    Nanayakkara, T., Watanabe, K., Kiguchi, K., et al.: Evolutionary structured RBF neural network based control of a seven-link redundant manipulator. In: IEEE Society (eds.) Proceedings of the 39th SICE Annual Conference, Saga Univ., pp. 148–153 (2000)Google Scholar
  4. 4.
    Chirikjian, G.S., Burdick, J.W.: The Kinematics of Hyper-Redundant Robot Locomotion. IEEE Transactions on Robotics and Automation 11(6), 781–793 (1995)CrossRefGoogle Scholar
  5. 5.
    Ma, S., Konno, M.: An Obstacle Avoidance Scheme for Hyper-redundant Manipulators –Global Motion Planning in Posture Space. In: Harrigan, R., Jamshidi, M. (eds.) Proceedings of IEEE ICRA, Albuquerque, pp. 161–166 (1997)Google Scholar
  6. 6.
    Kobyashi, H., Ohtake, S.: Shape Control of Hyper Redundant Manipulator. In: Fukuda, T., Arimoto, S. (eds.) Proceedings of IEEE ICRA, Nagoya, pp. 2803–2808 (1995)Google Scholar
  7. 7.
    Moody, J., Darken, C.: Learning with Localized Receptive Fields. In: Touretzky, D., Hin-ton, G., Sejnowski, T. (eds.) Proceedings of Connectionist Models Summer School, San Mateo, pp. 133–143 (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jinguo Liu
    • 1
    • 3
  • Yuechao Wang
    • 1
  • Shugen Ma
    • 1
    • 2
  • Bin Li
    • 1
  1. 1.Robotics Laboratory of Chinese Academy of SciencesShenyang Institute of AutomationShenyangChina
  2. 2.COE Research InstituteRitsumeikan UniversityShiga-kenJapan
  3. 3.Graduate School of Chinese Academy of SciencesBejingChina

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