Advertisement

Journal of Intelligent and Robotic Systems

, Volume 45, Issue 3, pp 203–216 | Cite as

An Intelligent Controller Improving the Drive System of a Head Platform

  • G. N. MarichalEmail author
  • J. Toledo
  • L. Acosta
  • R. L. Marichal
  • M. Sigut
  • E. J. González
Article
  • 56 Downloads

Abstract

In this paper a new approach for steering a binocular head is presented. This approach is based on extracting the expert’s knowledge in order to improve the behaviour of the classical control strategies. This is carried out without inserting new elements in the system. Neuro–Fuzzy techniques have been chosen in order to reach this target. As a result a more friendly robotic system is achieved.

Key words

control fuzzy logic neural networks robotics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Breazeal, C., Edsinger, A., Fitzpatrick, P., Scassellati, B., Varchavskaia, P.: Social constraints on animate vision. IEEE Intelligent Systems, 32–37 (July–August 2000)Google Scholar
  2. 2.
    Gosselin, C., St-Pierre, E., Gagne, M.: On the development of the agile eye. In: IEEE Robotics and Automation Magazine, pp. 29–37. (1996)Google Scholar
  3. 3.
    Chen, C.H.: Fuzzy Logic and Neural Networks Handbook. McGraw-Hill (1996)Google Scholar
  4. 4.
    Ballard, D.H.: Animate vision. Artif. Intell. 48, 57–86 (1991)CrossRefGoogle Scholar
  5. 5.
    Daxwanger, W.A., Schmidt, G.: Neural and fuzzy approaches to vision-based parking control, control engineering. Practice 4(11), 1607–1614 (1996)Google Scholar
  6. 6.
    Eklundh, J.O., Pahlavan, K., Uhlin, T.: The kth head–eye system. In: Vision as a Process, Chap. 15, pp. 237–259. (1995)Google Scholar
  7. 7.
    Nyongesa, H.O., Rosin, P.L.: Editorial: Neural–Fuzzy applications in computer vision. J. Intell. Robot. Sys. 29(4), 309–315 (2000)CrossRefGoogle Scholar
  8. 8.
    Hush Don, R., Horne Bill, G.: Progress in supervised neural networks. IEEE Signal Process. Mag., 8–34 (January 1993)Google Scholar
  9. 9.
    Kosko, B.:Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice-Hall (1992)Google Scholar
  10. 10.
    Acosta, L., Marichal, G.N., Moreno, L., Rodrigo, J.J., Hamilton, A., Méndez, J.A.: A robotic system based on neural network controllers. Artif. Intell. Eng. 13, 393–398 (1999)CrossRefGoogle Scholar
  11. 11.
    Li, H., Gupta, M. (eds.): Fuzzy Logic and Intelligent Systems. Kluwer Academic Publishers (1995)Google Scholar
  12. 12.
    Swain, M., Stricker, M.A. (eds): Promising directions in active vision. Int. J. Comput. Vis., Special Issue on Active Vision I 11(2), 109–126 (1993)Google Scholar
  13. 13.
    Miller III, W., Sutton, R., Werbos, P. (eds.): Neural Networks for Control. MIT Press, Cambridge (1990)Google Scholar
  14. 14.
    Murray, D., McLauchlan, P., Reid, I., Sharkey, P.: Reactions to peripheral image motion using a head/eye plataform. Proc. of IEEE International Conference on Computer Vision, pp. 403–411 (1993)Google Scholar
  15. 15.
    Tao, W.J., Burkhardt, H.: Vision-guided flame control using fuzzy-logic and neural networks. Particle Particle Systems Characterization 12(2), 87–94 (1995)CrossRefGoogle Scholar
  16. 16.
    Zadeh, L.: Fuzzy sets. Information Control 8, 338–353 (1965)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • G. N. Marichal
    • 1
    Email author
  • J. Toledo
    • 1
  • L. Acosta
    • 1
  • R. L. Marichal
    • 1
  • M. Sigut
    • 1
  • E. J. González
    • 1
  1. 1.Department of Applied Physics, Electronics & SystemsUniversity of La LagunaTenerifeSpain

Personalised recommendations