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Analysis of the scenery perceived by a real mobile robot Khepera

  • Ryoichi Odagiri
  • Wei Yu
  • Tatsuya Asai
  • Kazuyuki Murase
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1478)

Abstract

In order to understand the dynamic interactions of autonomous robots with their environments, the flow of the sensory information perceived by a real mobile robot Khepera was analyzed with the return map. The plot of Xn+1 v.s. X n of chaotic time series X n , called the return map, have been widely used to reveal the hidden structure. A real mobile robot Khepera was evolved in three different environments with various levels of complexity. The fitness function of GA operations included the complexity measure of the control structure, i.e., individuals with a simpler structure obtained a higher score. The evolution lead to develop the robot with the minimal structure sufficient to live and perform tasks in the given environment. The return maps of these robots differed each other considerably. However, when the robot evolved in the most complex environment was asked to navigate in the other two environments, the return maps obtained there were similar to (or a substructure of) the one in the most complex environment. These results indicated that the autonomous robot behaved such a way that the flow of sensory information did not depend much on the environment where he situated but largely on the one where he had evolved.

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References

  1. 1.
    Degan, H., Holden, A. V., Olsen, L. F.: Chaos in Biological Systems, Plenum, New York. (1987)Google Scholar
  2. 2.
    Holden, A.V.: Chaos, Manchesterand Princeton Univ. Press., Princeton. (1986)Google Scholar
  3. 3.
    Langton, C.: Life at the Edge of Chaos. In Langton, C., Tayler, C., Farmer, J.D., Rasmussen, S. (eds.): Artificial Life II. Santa Fe Institute Studies in the Science of Complexity, Proceedings Volume X. Addison-Wesley, Redwood City, CA, (1992) 41–91Google Scholar
  4. 4.
    Mondada, F., Franzi, E., Ienne, P.: Mobile robot miniaturization: A tool for investigation in control algorithms, Proceedings of the Third International Symposium on Experiment Robotics. Kyoto, Japan, (1993)Google Scholar
  5. 5.
    Nolfi, S., Floreano, D., Miglino, O., Mondada, F.: How to evolve autonomous robots: Different approaches in evolutionary robotics, In Brooks, R., Maes, P. (eds.): Artificial Life IV Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems. MIT Press, Cambridge, MA, (1994)Google Scholar
  6. 6.
    Odagiri, R., Wei, Y., Asai T., Murase, K.: Measuring the complexity of the real environment with evolutionary robot: Evolution of a real mobile robot Khepera to have a minimal structure, Proceedings of the IEEE International Conference on Evolutionary Computation, Anchorage, AK, (1998 in press)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Ryoichi Odagiri
    • 1
  • Wei Yu
    • 1
  • Tatsuya Asai
    • 1
  • Kazuyuki Murase
    • 1
  1. 1.Department of Information ScienceFukui UniversityFukuiJapan

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