World Modeling in Disaster Environments with Constructive Self-Organizing Maps for Autonomous Search and Rescue Robots

  • Çetin Meriçli
  • I. Osman Tufanoğulları
  • H. Levent Akın
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3276)


This paper proposes a novel approach for a Constructive Self-Organizing Map (SOM) based world modeling for search and rescue operations in disaster environments. In our approach, nodes of the self organizing network consist of victim and waypoint classes where victim denotes a human being waiting to be rescued and waypoint denotes a free space that can be reached from the entrance of debris. The proposed approach performed better than traditional self-organizing maps in terms of both the accuracy of the output and the learning speed. In this paper the detailed explanation of the approach and some experimental results are given.


Search & Rescue Robotics Self-organizing Maps Mobile Robotics World Modeling 


  1. 1.
    Arsenio, A.: Active Laser Range Sensing for Natural Landmark Based Localization of Mobile Robots, M.Sc. Thesis, IST, Tech. U Lisbon (1997)Google Scholar
  2. 2.
    Ribeiro, M.I., Goncalves, J.G.M.: Natural Landmark Based Localization of Mobile Robots Using Laser Range Data. In: Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots, Kaiserslautern, Germany (1996)Google Scholar
  3. 3.
    Akın, H.L., et al.: Cerberus 2003 Team Report, Bogazici University, Istanbul (2003)Google Scholar
  4. 4.
    Marques, R., Zalama, E., García-Bermejo, J.G., Peran, J.R.: World Modeling and Position Estimation for a Mobile Robot Using Self-Organizing Networks. In: 4th IFAC International Symposium on Intelligent Components and Instruments for Control Applications, SICICA 2000, Buenos Aires, Argentina (2000)Google Scholar
  5. 5.
    Nehmzow, U., Smithers, T., Hallam, J.: Locatio Recognition in a Mobile Robot Using Self-Organising Feature Maps, Information Processing. In: Autonomous Mobile Robots. Springer, Heidelberg (1991)Google Scholar
  6. 6.
    Zrehen, S., Gaussier, P.: Why Topological Maps Are Useful for Learning in an Autonomous Agent. In: Proceedings PerAc, September 1994. IEEE Press, Lausanne (1994)Google Scholar
  7. 7.
    Kohonen, T.: Self-Organization and Associative Memory. Series in Information Science, vol. 8. Springer, Heidelberg (1984)zbMATHGoogle Scholar
  8. 8.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley & Sons, Chichester (2001)zbMATHGoogle Scholar
  9. 9.
    Webots Mobile Robot Simulator (2003),

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Çetin Meriçli
    • 1
  • I. Osman Tufanoğulları
    • 1
  • H. Levent Akın
    • 1
  1. 1.Department of Computer EngineeringBoğaziçi UniversityBebek, IstanbulTurkey

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