Skip to main content

Nested Networks for Robot Control

  • Chapter

Abstract

Sensor based robot control systems can overcome many of the difficulties which are caused by unknown or uncertain models of the environment. Conventional sensor based control systems require explicit knowledge of the kinematics and dynamics of the robot arm and a careful calibration of the sensor system. In-stead, adaptive neural controllers can be used to build an internal representation of the camera-motor correspondence from exemplar behaviour and adapt where necessary. In that case, static models are not necessary anymore, and the system can cope with changing behaviour of the robot (wear and tear, payload correction) and its sensors (changing lighting conditions, calibration and re-calibration).

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. T. Martinetz, H. Ritter, and K. Schulten. Three-dimensional neural net for learning visuomotor coordination of a robot arm. IEEE Transactions on Neural Networks, 1(1):131–136, March 1990.

    Article  Google Scholar 

  2. T. Martinetz and K. Schulten. A “neural-gas” network learns topologies. In Proceedings of the 1991 International Conference on Artificial Neural Networks, volume 1, pages 397-402, Espoo, Finland, June 1991.

    Google Scholar 

  3. T. Hesselroth, K. Sarkar, P. van der Smagt, and K. Schulten. Neural network control of a pneumatic robot arm. Technical Report UIUC-BI-TB-92-15 (IEEE Systems, Man, and Cybernetics, to be published), Theoretical Biophysics, University of Illinois at Urbana/Champaign, August 1992.

    Google Scholar 

  4. P. van der Smagt and B. Kröse. A real-time learning neural robot controller. In Proceedings of the 1991 International Conference on Artificial Neural Networks, pages 351-356, Espoo, Finland, June 1991.

    Google Scholar 

  5. P. van der Smagt, A. Jansen, and F. Groen. Interpolative robot control with the nested network approach. In Proceedings of the 1992 IEEE International Symposium on Intelligent Control, pages 475-480. Glasgow, Scotland, U.K., August 1992.

    Google Scholar 

  6. A. Jansen, P. van der Smagt, and F. Groen. High-precision robot control: The nested network. In I. Aleksander and J. Taylor, editors, Artificial Neural Networks 2, pages 583-586. North-Holland/Elsevier Science Publishers, September 1992.

    Google Scholar 

  7. P. van der Smagt, B. Kröse, and F. Groen. Using time-to-contact to guide a robot manipulator. In Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 177-182. Raleigh, N. C, June 1992.

    Google Scholar 

  8. D. Psaltis, A. Sideris, and A. Yamamura. A multilayer neural network controller. Control Systems Magazine, 8(2):17–21, April 1988.

    Article  Google Scholar 

  9. C. Brice and C. Fennema. Scene analysis using regions. Artificial Intelligence, 1:205–226, 1970.

    Article  Google Scholar 

  10. V. Vysniauskas, F. Groen, and B. Kröse. Function approximation with a feedforward network: the optimal number of learning samples and hidden units. Technical report, Department of Computer Systems, University of Amsterdam, Amsterdam, Netherlands, 1992.

    Google Scholar 

  11. P. van der Smagt. Minimisation methods for training feed-forward networks. Technical Report UIUC-BI-TB-92-17 (submitted to Neural Networks), Theoretical Biophysics, University of Illinois at Ur-bana/Champaign, November 1992.

    Google Scholar 

  12. Arjen Jansen. Neural Approaches in the Approximation of the Inverse Kinematics Function: A Comparative Study. Universiteit van Amsterdam, Faculteit Computer Systemen, April 1992. graduation thesis.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer Science+Business Media New York

About this chapter

Cite this chapter

Jansen, A., van der Smagt, P., Groen, F. (1995). Nested Networks for Robot Control. In: Murray, A.F. (eds) Applications of Neural Networks. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2379-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-2379-3_9

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-5140-3

  • Online ISBN: 978-1-4757-2379-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics