Skip to main content
SpringerLink
Log in

Near-Optimal Fuzzy Systems Using Polar Clustering: Application to Control of Vision-Based Arm-Robot

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3684))

Abstract

This paper presents a design algorithm to near-optimal fuzzy systems using polar clustering method for vision-based robot control systems. The complexity of the optimal fuzzy system for a vision-based control system is so great that it can not be applied to real systems or can not be useful. Therefore we generally use clustering method, to reduce the complexity of optimal fuzzy systems. In the class of near-optimal fuzzy systems, for more efficient use of clustering, we propose the polar clustering method using polar quantization. In order to verify the effectiveness of the proposed method, experimentally, it is applied to a vision-based arm robot control system.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Zadeh, L.A.: Fuzzy sets. Informat. Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  2. Bellman, R.E., Zadeh, L.A.: Decision-making in a fuzzy environment. Menagement Science 17(4), 141–164 (1970)

    MathSciNet  Google Scholar 

  3. Zadeh, L.A.: Outline of a new approach to the analysis of complex systems ad decision processes. IEEE Trans. System, Man, and Cybernetics 3(1), 28–44 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  4. Sugeno, M., Nishida, M.: Fuzzy control of model car. Fuzzy Sets and Systems, 103–113 (1985)

    Google Scholar 

  5. Wang, L.X.: A Course in Fuzzy Systems and Control. Prentice-Hall, Englewood Cliffs (1997)

    MATH  Google Scholar 

  6. Baraldi, A., Blonda, A.: A Survey of Fuzzy Clustering Algorithms for Pattern Recognition-Part I. IEEE Trans. System, Man, and Cybernetics-Part B: Cybernectics 29(6), 778–786 (1999)

    Article  Google Scholar 

  7. Baraldi, A., Blonda, A.: A Survey of Fuzzy Clustering Algorithms for Pattern Recognition-Part II. IEEE Trans. System, Man, and Cybernetics-Part B: Cybernectics 29(6), 787–800 (1999)

    Google Scholar 

  8. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall Inc., Englewood Cliffs (2002)

    Google Scholar 

  9. Wilson, W.J., Williams Hulls, C.C., Bell, G.S.: Relative End-Effector Control Using Cartesian Position Based Visual Servoing. IEEE Trans. Robotics and Automation 12(5), 684–696 (1996)

    Article  Google Scholar 

  10. Chen, W., Mills, J.K., Chu, J., Sun, D.: A Fuzzy Compensator for Uncertainty of Industrial Robots. In: Proc. IEEE Int. Conf. on Robotics and Automation, May 2001, vol. 3, pp. 2968–2973 (2001)

    Google Scholar 

  11. Shakunaga, T.: An Object Pose Estimation System Using a Single Camera. In: Proc. IEEE Int. Conf. on Intelligent Robots and Systems, July 1992, vol. 2, pp. 1053–1060 (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Korea University, 1, 5-ka, Anam-dong, Seoul, 136-701, Korea

    Young-Joong Kim & Myo-Taeg Lim

Authors
  1. Young-Joong Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Myo-Taeg Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, Moulsecoomb, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kim, YJ., Lim, MT. (2005). Near-Optimal Fuzzy Systems Using Polar Clustering: Application to Control of Vision-Based Arm-Robot. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_72

Download citation

  • DOI: https://doi.org/10.1007/11554028_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28897-8

  • Online ISBN: 978-3-540-31997-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation