Neuro-Fuzzy Control for Basic Mobile Robot Behaviours

  • Jelena Godjevac
  • Nigel Steele
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 61)


The work described here was originally conceived in conjunction with employing a robotic system for cleaning the interior of railway carriages, although the ideas clearly extend to other industrial operations.


Membership Function Mobile Robot Fuzzy Control Fuzzy Controller Obstacle Avoidance 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    B. Beaufrere and S. Zeghloul. Utilisation de la logique floue dans la navigation d’un robot mobile en milieu inconnu. In 4ème Journées Nationales - Les Applications des Ensembles Flous, pages 147 - 155, Lille, Dec. 1994. In French.Google Scholar
  2. 2.
    V. Braitenberg. Vehicles. MIT, 1984.Google Scholar
  3. 3.
    P. Y. Glorennec and L. Jouffe. A Reinforcement Learning Method for an Autonomous Robot. In Fourth European Congres on Intelligent Techniques and Soft Computing, EUFIT ’96, volume 2, pages 1100 - 1104, Aachen, Germany, Sept. 1996.Google Scholar
  4. 4.
    J. Godjevac. A Learning Procedure for a Fuzzy System: Application to Obstacle Avoidance. In Proceedings ICSC Symposium on Fuzzy Logic (ISFL95), pages C142–148, Zuerich, Switzerland. May 1995.Google Scholar
  5. 5.
    J. Godjevac. Comparative study of Fuzzy Control, Neural Network Control and Neuro-Fuzzy Control. In D. Ruan, editor, Fuzzy Set Theory and Advanced Mathematical Applications, chapter 12, pages 291–322. Kluwer Academic, June 1995.CrossRefGoogle Scholar
  6. 6.
    J. Godjevac. Neuro-Fuzzy Controllers; an Application in Robot Learning. PPUR - EPFL, 1997.Google Scholar
  7. 7.
    S. G. Goodridge, R. C. Luo, and M. G. Kay. Multi-Layered Fuzzy Behavior Fusion for Real-Time Control of Systems with Many Sensors. In IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI ’94, pages 272 - 279, Las Vegas, Oct. 1994.Google Scholar
  8. 8.
    F. Michaud, G. Lachiver, and C. T. Le Dinh. Fuzzy Selection and Blending of Behaviors for Situated Autonomous Agent. In Conference Fuzz-IEEE 96, volume 1. nazes 258 - 264. New Orleans. 1996.Google Scholar
  9. 9.
    F. Mondada, E. Franzi, and P. Ienne. Mobile robot miniaturization: A tool for investigation in control algorithms. Informatik, pages 17–20, Feb. 1994.Google Scholar
  10. 10.
    A. G. Pipe, B. Carse, and A. Winfield. Automatic Generation of Fuzzy Sensorimotor Rules for Mobile Robotics. In Conference Fuzz-IEEE 96, volume 3, pages 2053 - 2058, New Orleans, 1996.Google Scholar
  11. 11.
    R. Rojas. Neural Networks, A systematic Introduction. Springer, 1996.MATHGoogle Scholar
  12. 12.
    M. Sugeno and K. Murakami. An Experimental Study on Fuzzy Parking Control Using a Model Car. In M. Sugeno, editor, Industrial Applications of Fuzzy Control, chapter 8, pages 125–138. North-Holland, 1985.Google Scholar
  13. 13.
    L.-X. Wang. Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Prentice Hall, 1994.Google Scholar
  14. 14.
    S. Yasunobu and N. Minamiyama. A Proposal of Intelligent Vehicle Control System by Predictive Fuzzy Control with Hierarchical temporary Target Setting. In Conference Fuzz-IEEE 96, volume 2, pages 873 – 878, New Orleans, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Jelena Godjevac
  • Nigel Steele

There are no affiliations available

Personalised recommendations