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Neural Networks for Visual Servoing in Robotics

  • J. P. Urban
  • J. L. Buessler
  • J. Gresser
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 21)

Abstract

This chapter introduces an application of artificial neural network techniques to robotic control. Arm movements are controlled using visual features. The neurocontroller adapts on-line without any prior knowledge of the system geometry.

Keywords

Soft Computing Active Vision Robotic Platform Neural Controller Normalize Little Mean Square 
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]
    Merlat, L., Silvestre, N. and Merckle, J. (1997), A Tutorial Introduction to Cellular Neural Networks, Technical Report EEA-TROP-TR-97–06, University of Mulhouse, France.Google Scholar
  2. [2]
    Srinivasa, N. and Sharma, R. (1997), Execution of Saccades for Active Vision Using a Neurocontroller, IEEE Control Systems Magazine, Vol. 17, No. 2, pp. 18–75.CrossRefGoogle Scholar
  3. [3]
    Ans, B., Coiton, Y., Gilhodes, J. C. and Velay, J. L. (1994), A Neural Network Model for Temporal Sequence Learning and Motor Programming, Neural Networks, Vol. 7, No. 9, pp. 1461–1476.CrossRefGoogle Scholar
  4. [4]
    Nguyen, D. and Widrow, B. (1991), The Truck Backer-Upper: An example of Self-Learning in Neural Networks, in: Neural Networks for Control, A Bradford Book, MIT Press, pp. 287–300.Google Scholar
  5. [5]
    Kröse, B. and van Dam, J. (1997), Neural vehicles, in: Neural Systems for Robotics, Academic Press, Amsterdam, pp. 271–296.Google Scholar
  6. [6]
    Miller, W. T. (1994), Real-Time Neural Network Control of a Biped Walking Robot, Control Systems Magazine, Vol. 14, No. 1, pp. 41–48.CrossRefGoogle Scholar
  7. [7]
    Miyamoto, H. and et al. (1996), A Kendama Learning Robot Based on Bidirectional Theory, Neural Networks, Vol. 9, No. 8, pp. 1281–1302.MathSciNetCrossRefGoogle Scholar
  8. [8]
    Barto, A. G. (1995), Reinforcement Learning in Motor Control, in: Handbook of Brain Theory and Neural Networks, MIT Press, pp. 809–813.Google Scholar
  9. [9]
    Kawato, M. (1991), Computational Schemes and Neural Network Models for Formation and Control, in: Neural Networks for Control, A Bradford Book, MIT Press, pp. 5–58.Google Scholar
  10. [10]
    Agarwal, M. (1997), A Systematic Classification of Neural-Network-Based Control, IEEE Control Systems Magazine, Vol. 17, No. 2, pp. 75–93.CrossRefGoogle Scholar
  11. [11]
    Jain, A. K., Mao, J. and Mohiuddin, K. M. (1996), Artificial Neural Networks: A Tutorial, IEEE Computer Magazine, Vol. 29, No. 3.Google Scholar
  12. [12]
    Hutchinson, S., Hager, G. and Corke, P. (1996), A Tutorial on Visual Servo Control, IEEE Trans. on Robotics and Automation, Vol. 12, No. 5, pp. 651–670.CrossRefGoogle Scholar
  13. [13]
    Widrow, B. and Lehr, M. A. (1990), 30 Years of Adaptative Neural Networks: Perceptron, Madaline and Backpropagation, Proc. of the IEEE,Vol. 78, No. 9.Google Scholar
  14. [14]
    Jägersand, M. (1996), Visual Servoing using Trust Region Methods and Estimation of the Full Coupled Visual-Motor Jacobian, in: LASTED Applications of Robotics and Control ‘86, Applications of Robotics and Control.Google Scholar
  15. [15]
    Hosoda, K. and Asada, M. (1994), Versatile Visual Servoing Without Knowledge of True Jacobian, in: IROS 94, Munich, pp. 186–193.Google Scholar
  16. [16]
    Weiss, L. E. (1984), Dynamic Visual Servo Control of Robots: An Adaptive Image-Based Approach, Technical Report CMU-RI-TR-84–16, CMU.Google Scholar
  17. [17]
    Espiau, B., Chaumette, F. and Rives, P. (1992), A New Approach to Visual Servoing in Robotics, IEEE Trans. on Robotics and Automation, Vol. 8, No. 3, pp. 313–326.CrossRefGoogle Scholar
  18. [18]
    Ritter, H., Martinetz, T. and Schulten, K. (1992), Neural Computation and Self-Organizing Maps, An Introduction, Addison-Wesley, New-York.Google Scholar
  19. [19]
    Kohonen, T. (1995), Self-Organizing Maps, Springer-Verlag, Berlin.CrossRefGoogle Scholar
  20. [20]
    Burnod, Y. (1989), An Adaptative Neural Network: the Cerebral Cortex, Masson, Paris.Google Scholar
  21. [21]
    Jägersand, M. and Nelson, R. (1994), Adaptative Differential Visual Feedback for Uncalibrated Hand-Eye Coordination and Motor Control, Technical Report TR 579, University of Rochester.Google Scholar
  22. [22]
    Berthoz, A. and Petit, L. (1996), Looking and its Jerky Triggers of Movement, in French, La Recherche, No. 289, pp. 58–67.Google Scholar
  23. [23]
    Ronco, E. and Gawthrop, P. (1995), Modular Neural Networks: A State of the Art, Technical Report CSC-95026, Center of System and Control Un. Glasgow.Google Scholar
  24. [24]
    Littmann, E. and Ritter, H. (1993), Generalization Abilities of Cascade Network Architectures, Advances in Neural Information Processing System, Vol. 5, pp. 188–195.Google Scholar
  25. [25]
    Miyamoto, H., Kawato, M., Setoyama, T. and Suzuki, R. (1988), Feedback Error Learning Neural Network for Trajectory Control of a Robotic Manipulator, Neural Networks, pp. 251–265.Google Scholar
  26. [26]
    Urban, J. P., Buessler, J. L. and Kihl, H. (1996), Parallel Neural Processing for the Visual Servoing of A Robot Arm, in: IEEE Int. Conf on Systems Man and Cybernetics, Beijing, China, Vol. 3, pp. 1806–1811.Google Scholar
  27. [27]
    Urban, J. P., Buessler, J. L. and Wira, P. (1997), NeuroModule-Based Visual Servoing of a Robot Arm with a 2 d.o.f. Camera, to appear in: IEEE Int. Conf. on Systems Man and Cybernetics, Orlando.Google Scholar
  28. [28]
    Jeannerod, M., Paulignan, Y., Mackenzie, C. and Marteniuk, R. M. (1992), Parallel Visuomotor Processing in Human Prehension Movements, in: Control of Arm Movement in Space, Springer-Verlag, Berlin, pp. 27–44.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • J. P. Urban
    • 1
  • J. L. Buessler
    • 1
  • J. Gresser
    • 1
  1. 1.TROP Research GroupUniversité de Haute-AlsaceMulhouseFrance

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