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World Wisepersons Workshop

WWW 1994: Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms pp 148–172Cite as

Genetic algorithms for the development of fuzzy controllers for mobile robots

  • Fuzzy — Genetic Algorithms
  • Conference paper
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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1011))

Abstract

In this paper we present a novel genetic algorithm (GA) for designing fuzzy systems for mobile robot control, in which the meaning of a section of chromosome is determined by surrounding genes, much like a language description. We demonstrate that this leads to much improved convergence speed over conventional GA codings, and discuss the mechanisms that lead to this improvement. The algorithm also uses a simple chromosome reordering operator which uses the algorithm to maximise its own efficiency.

Our results show the performance of our algorithm when applied to designing controllers for the commonly used benchmark inverted pendulum problem, as well as problems in mobile robotics. We discuss the application of this method to robotics generally, and some of the difficulties faced when the algorithm is used for more complicated applications, and how these problems could be approached.

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References

  1. Altenberg, L.; The Evolution of Evolvability Ch. 3 in Ed. Kinnear, K. E.; Advances in Genetic Programming; MIT Press, Cambridge, MA, 1994.

    Google Scholar 

  2. Angeline, P. J.; Genetic Programming and Emergent Intelligence Ch. 4 in Ed. Kinnear, K. E.; Advances in Genetic Programming; MIT Press, Cambridge, MA, 1994.

    Google Scholar 

  3. Beasley, D., Bull, D. R. and Martin, R. R.; An Overview of Genetic Algorithms, Part 1, Fundamentals; University Computing, Vol. 15, No. 2, 1993.

    Google Scholar 

  4. Brooks, R.; A Robust Layered Control System for a Mobile Robot; IEEE Trans. Robotics and Automation, Vol. 2, No. 1, Mar. 1986.

    Google Scholar 

  5. Cooper, M. G. and Vidal, J. J.; Genetic Design of Fuzzy Controllers; Proc. 2nd Int. Conf. on Fuzzy Theory and Technology, Durham, NC, 1993.

    Google Scholar 

  6. Davidor, Y.; Genetic Algorithms and Robotics; World Scientific, Singapore, 1991.

    Google Scholar 

  7. Goldberg, D. E.; Genetic Algorithms in Search, Optimisation and Machine Learning; Addison-Wesley, 1989.

    Google Scholar 

  8. Hoffmann, F. and Pfister, G.; Automatic Design of Hierarchical Fuzzy Controllers Using Genetic Algorithms; Proc. 2nd European Congress on Intelligent Techniques and Soft Computing (EUFIT '94), Aachen, Germany, 1994.

    Google Scholar 

  9. Holland, J. H.; Adaptation in Natural and Artificial Systems (2nd ed.); MIT Press, Cambridge, MA, 1992.

    Google Scholar 

  10. Karr, C. L.; Design of a Cart-Pole Balancing Fuzzy Logic Controller using a Genetic Algorithm; SPIE Conf. on Applications of Artificial Intelligence, Bellingham, WA, 1991.

    Google Scholar 

  11. Kosko, B.; Neural Networks and Fuzzy Systems; Prentice Hall, Englewood Cliffs, NJ, 1992.

    Google Scholar 

  12. Leitch, D. and Probert, P.; Context Dependent Coding in Genetic Algorithms for the Design of Fuzzy Systems; Proc. IEEE/Nagoya University WWW on Fuzzy Logic and Neural Nets / Genetic Algorithms, Nagoya, Japan, 1994.

    Google Scholar 

  13. Levenick, J. R.; Inserting Introns Improves Genetic Algorithm Success Rate: Taking a Cue from Biology; Proc. 4th Int. Conf. on Genetic Algorithms, 1991.

    Google Scholar 

  14. Nomura, H., Hayashi, I. and Wakami, N.; A Self-Tuning Method of Fuzzy Reasoning by Genetic Algorithm; Proc. Int. Fuzzy Systems and Intelligent Control Conf., Louisville, KY, 1992.

    Google Scholar 

  15. Pedrycz, W.; Fuzzy Sets and Systems; Research Studies Press, 1989.

    Google Scholar 

  16. Takagi, H. and Lee, M.; Neural Networks and Genetic Algorithm Approaches to Auto-Design of Fuzzy Systems; Proc. 8th Austrian Artificial Intelligence Conference, FLAI '93, Springer-Verlag, Berlin, 1993.

    Google Scholar 

  17. Varšek, A., Urbančič, T. and Filipič, B.; Genetic Algorithms in Controller Design and Tuning; IEEE Trans. on Systems, Man and Cybernetics, Vol. 23, No. 5, 1993.

    Google Scholar 

  18. Wang, L. X. and Mendel, J. M.; Generating Fuzzy Rules by Learning from Examples; IEEE Trans. Systems, Man and Cybernetics, Vol. 22, No. 6, 1992.

    Google Scholar 

  19. Zadeh, L.; Fuzzy Sets; J Information and Control, Vol. 8, pp. 338–353, 1965.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Robotics Research Group, University of Oxford, UK

    Donald Leitch & Penelope Probert

Authors
  1. Donald Leitch
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  2. Penelope Probert
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Editor information

Takeshi Furuhashi

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© 1995 Springer-Verlag Berlin Heidelberg

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Leitch, D., Probert, P. (1995). Genetic algorithms for the development of fuzzy controllers for mobile robots. In: Furuhashi, T. (eds) Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms. WWW 1994. Lecture Notes in Computer Science, vol 1011. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60607-6_11

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  • DOI: https://doi.org/10.1007/3-540-60607-6_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60607-9

  • Online ISBN: 978-3-540-48457-8

  • eBook Packages: Springer Book Archive

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