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Integration of Enhanced Adaptive Fuzzy Clustering Algorithm with Probabilistic Technique for Dynamic Map Building

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Soft Computing and Industry

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Abstract

This paper addresses the problem of incremental and on-line learning of indoor dynamic environments by mobile robots. The proposed method further improves the Enhanced Adaptive Fuzzy Clustering (EAFC) algorithm for segment detection by using probabilistic techniques. In this study, the environment boundaries is extracted by the AFC algorithm and the probabilistic technique is used to estimate and update the state of dynamic objects in the mobile robot workplace. The method has been implemented and tested in a Pioneer II mobile robot.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Y. L. Ip, A. B. Rad, K. M. Chow & Y. K. Wong

Authors
  1. Y. L. Ip
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  2. A. B. Rad
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  3. K. M. Chow
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  4. Y. K. Wong
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Editor information

Editors and Affiliations

  1. Enterprise Integration, Cranfield University, Cranfield, Bedford, MK43 0AL, UK

    Rajkumar Roy PhD

  2. Department of Pattern Recognition, Fraunhofer IPK-Berlin, Pascalstrasse 8-9, Berlin, 10587, Germany

    Mario Köppen Diplom-Phys

  3. Institute of Intelligent Electronics, Helsinki University of Technology, Otakaari 5 A, FIN-02150, Espoo, Finland

    Seppo Ovaska PhD

  4. Department of Information Engineering, Mie University, 1515 Kamihama-cho, Tsu, 514-8507, Japan

    Takeshi Furuhashi PhD

  5. Royal Institute of Technology, NADA/CV AP, 10044, Stockholm, Sweden

    Frank Hoffmann

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© 2002 Springer-Verlag London

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Ip, Y.L., Rad, A.B., Chow, K.M., Wong, Y.K. (2002). Integration of Enhanced Adaptive Fuzzy Clustering Algorithm with Probabilistic Technique for Dynamic Map Building. In: Roy, R., Köppen, M., Ovaska, S., Furuhashi, T., Hoffmann, F. (eds) Soft Computing and Industry. Springer, London. https://doi.org/10.1007/978-1-4471-0123-9_23

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  • DOI: https://doi.org/10.1007/978-1-4471-0123-9_23

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-1101-6

  • Online ISBN: 978-1-4471-0123-9

  • eBook Packages: Springer Book Archive

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