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Grasping Control of Robot Hand Using Fuzzy Neural Network

  • Peng Chen
  • Yoshizo Hasegawa
  • Mitushi Yamashita
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

In this paper, we propose a grasping control method for robot hand using fuzzy theory and partially- linearized neural network. The robot hand has Double-Octagon Tactile Sensor (D.O.T.S), which has been proposed in our previous papers, to detect grasping force between the grasped object and the robot fingers. Because the measured forces are fluctuant due to the measuring error and vibration of the hand, the tactile information is ambiguous. In order to quickly control the grasping force to prevent the grasped object sliding out off the robot fingers, we apply the possibility theory to deal with the ambiguous problem of the tactile information, and use the partially- linearized neural network (P.L.N.N) to construct a fuzzy neural network. The method proposed in this paper is verified by applying it to practical grasping control of breakable objects, such as eggs, fruits, etc.

Keywords

Tactile Sensor Fuzzy Neural Network Possibility Theory Tactile Information Robot Hand 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Maeno, T., Kobayashi, K., Kawai, T., Hirano, Y.: Grip Force Control by Detecting the Internal Strain Distribution Inside the Elastic Finger Having Curved Surface. Transactions of the Japan Society of Mechanical Engineers (C) 65(633), 1907–1914 (1999)Google Scholar
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    Nakayama, S., Chen, P., Matumiya, T., Toyota, T.: Development of Double-Octagon Tactile Sensor for Grasping Control. In: Proc. of 1999 IEEE/ASME International Conference on Advanced Intelligent Mechanics, pp. 218–223 (1999)Google Scholar
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    Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
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    Dubois, D., Prade, H.: Possibility Theory, An approach to Computerized Processing. Plenum Press, New York (1988)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peng Chen
    • 1
  • Yoshizo Hasegawa
    • 1
  • Mitushi Yamashita
    • 1
  1. 1.Department of Environmental Science & TechnologyMie UniversityTsuJapan

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