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Dynamic Flies: Using Real-Time Parisian Evolution in Robotics

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Applications of Evolutionary Computing (EvoWorkshops 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2037))

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Abstract

The Fly algorithm is a Parisian evolution strategy devised for parameter space exploration in computer vision applications, which has been applied to stereovision. The resulting scene model is a set of 3-D points which concentrate upon the surfaces of obstacles. In this paper, we present how the evolutionary scene analysis can be continuously updated and integrated into a specific real-time mobile robot navigation system. Simulation-based experimental results are presented.

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References

  1. J. Louchet. Using an individual evolution strategy for stereovision. Genetic Programming and Evolvable Machines. Kluwer, to appear, 2001.

    Google Scholar 

  2. P. Collet, E. Lutton, F. Raynal, and M. Schoenauer. Individual GP: an alternative viewpoint for the resolution of complex problems. In Genetic and Evolutionary Computation Conference GECCO99. Morgan Kaufmann, San Francisco, CA, 1999.

    Google Scholar 

  3. R.M. Haralick. Using perspective transformations in scene analysis. Computer Graphics and Image Processing, (13):191–221, 1980.

    Google Scholar 

  4. R.C. Jain, R. Kasturi, and B.G. Schunck. Machine Vision. McGraw-Hill, New York, 1994.

    Google Scholar 

  5. R. Salomon and P. Eggenberger. Adaptation on the evolutionary time scale: a working hypothesis and basic experiments. In Springer Lecture Notes on Computer Science, number 1363, pages 251–262. Springer-Verlag, Berlin, 1997.

    Google Scholar 

  6. O. Khatib. Real time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 5(1):90–99, Spring 1986.

    Article  Google Scholar 

  7. I. Ulrich and J. Borenstein. Vfh+: Reliable obstacle avoidance for fast mobile robots. In Procedings of the IEEE Conference On Robotics and Automation, ICRA’98, pages 1572–1577, Leuven, Belgium, May 1998.

    Google Scholar 

  8. J. Van Dam and B. Krose F. Groen. Transforming occupancy grids under robot motion. In S. Gielen and B. Kappe, editors, Artificial neural networks, page 318. Springer-Verlag, 1993.

    Google Scholar 

  9. M.C. Martin and H.P. Moravec. Robots evidence grid. Technical report, The Robotics Institute, Carnegie Mellon University, March 1996.

    Google Scholar 

  10. Y. Koren and J. Borenstein. Potentialfield methodes and their inherent limitations for mobile robot navigation. In Procedings of the IEEE Conference On Robotics and Automation, ICRA’91, pages 1398–1404, Sacramento, California, April 7-12 1991.

    Google Scholar 

  11. John S. Zelek. Complete real-time path planning during sensor-based discovery. In IEEE/RSJ International Conference on Intelligent Robots and systems, 1998.

    Google Scholar 

  12. B.H. Krogh. A generalized potential field approach to obstacle avoidance control. Robotics Research, August 1984.

    Google Scholar 

  13. J. Borenstein and Y. Koren. Real-time obstacle avoidance for fast mobile robots. In IEEE Transaction on System, Man, and Cybernetics, volume 19, pages 1179–1187, Sept./Oct. 1989.

    Article  Google Scholar 

  14. R.A. Brooks and J.H. Connel. Asynchronous distributed control system for a mobile robot. In SPIE86Cambridge Symposium on Optical and Optoelectronic Engineering, Cambridge, MA, pages 77–84, October 1986.

    Google Scholar 

  15. cumuli project: Computational understanding of multiple images, http://www.inrialpes.fr/cumuli.

  16. I. Rechenberg. Evolution strategy. In J.M. Zurada, R.J. MarksII, and C.J. Robinson, editors, Computational Intelligence imitating life, pages 147–159. IEEE Press, Piscataway, NJ, 1994.

    Google Scholar 

  17. P.K. Ser, S. Clifford, T. Choy, and W.C. Siu. Genetic algorithm for the extraction of nonanalytic objects from multiple dimensional parameter space. Computer Vision and Image Understanding, vol. 73no. 1, Academic Press: Orlando, FL, pages 1–13, 1999.

    Google Scholar 

  18. E. Lutton and P. Martinez. A genetic algorithm for the detection of 2D geometric primitives in images. In The Proceedings of the International Conference on Pattern Recognition, ICPR’94, pages 526–528, Los Alamitos, CA, October 9-13 1994. IEEE Computer Society.

    Google Scholar 

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Author information

Authors and Affiliations

  1. nria, projet fractales Domaine Voluceau, 105, 78153, Le Chesnay cedex, France

    Amine M. Boumaza

  2. ensta, 32 Boulevard Victor, 75739, Paris cedex 15, France

    Jean Louchet

Authors
  1. Amine M. Boumaza
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  2. Jean Louchet
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Editor information

Editors and Affiliations

  1. Leiden University, Niels Bohrweg1, 2333, CA, Leiden, The Netherlands

    Egbert J. W. Boers

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© 2001 Springer-Verlag Berlin Heidelberg

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Boumaza, A.M., Louchet, J. (2001). Dynamic Flies: Using Real-Time Parisian Evolution in Robotics. In: Boers, E.J.W. (eds) Applications of Evolutionary Computing. EvoWorkshops 2001. Lecture Notes in Computer Science, vol 2037. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45365-2_30

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  • DOI: https://doi.org/10.1007/3-540-45365-2_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41920-4

  • Online ISBN: 978-3-540-45365-9

  • eBook Packages: Springer Book Archive

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