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A New CNN-Based Method of Path Planning in Dynamic Environment

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Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7268))

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Abstract

In this paper the problem of path planning in a dynamic environment is considered. The minimum cost path is planned using Cellular Neural Network (CNN). CNN is a very useful tool for parallel signal processing and can be implemented using VLSI. In the proposed approach the problem of local minima (dead ends on a map) does not exist. Different criteria can be taken into account during path planning for example: the size of the robot, the traversability cost, the occurrence of dynamic obstacles, etc. The method allows us to specify the goal using semantic labels. The experiments performed in a real static environment and simulations in a dynamic environment have shown the efficiency of the proposed method.

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References

  1. Arkin, R.C., Balch, T.: Aura: Principles and practice in review. Journal of Experimental and Theoretical Artificial Intelligence 9, 175–189 (1997)

    Article  Google Scholar 

  2. Behring, C., Bracho, M., Castro, M., Moreno, J.A., Brzakovic, D.: Automatic multilevel halftoning for color images. In: Proc. of the International Conference on Image Processing (1997)

    Google Scholar 

  3. Biswas, R., Limketkai, B., Sanner, S., Thrun, S.: Towards object mapping in non-stationary environments with mobile robots (2002)

    Google Scholar 

  4. Buckley, S.: Fast motion planning for multiple moving robots. In: Proc. of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1419–1424 (1989)

    Google Scholar 

  5. Cembrano, G.L., Rodrguez-Vzquez, A., Espejo-Meana, S., Domnguez-Castro, R.: Ace16k: A 128x128 focal plane analog processor with digital i/o. Intern. Journal Neural Systems 13(6), 427–434 (2003)

    Article  Google Scholar 

  6. Choset, H., Lynch, K.M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L.E., Thrun, S.: Principles of Robot Motion: Theory, Algorithms, and Implementations. MIT Press, Boston (2005)

    MATH  Google Scholar 

  7. Chu, H., Eimaraghy, H.A.: Real-time multi-robot path planner based on a heuristic approach. In: Proc. of the IEEE International Conference on Robotics & Automation, ICRA (1992)

    Google Scholar 

  8. Chua, L., Roska, T.: The cnn paradigm. IEEE Transaction on Circuit Systems 40, 147–156 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chua, L., Young, L.: Cellular neural network. IEEE Transaction on Circuit Systems 35, 1271–1290 (1988)

    Google Scholar 

  10. Dougall, D.W., Archibald, J.K.: A recursive approach to roadmap-based path planning. In: The 12th IASTED Int. Conf. on Robotics and Applications, pp. 537–578 (2006)

    Google Scholar 

  11. Gnatowski, M., Siemiątkowska, B., Szklarski, J.: Extraction of semantic information from the 3d laser range finder. In: Parenti-Castelli, V., Schiehlen, W. (eds.) Proceedings of the 18th CISM-IFToMM Symposium. Robot Design, Dynamics and Control, ROMANSY 18, pp. 383–390 (2010)

    Google Scholar 

  12. Kinget, P., Steyeart, M.: Analogue cmos vlsi implementation of a cellular neural networks with continous programable templates. In: IEEE Symposium on Cercuit System, pp. 376–370 (1994)

    Google Scholar 

  13. Kozek, T., Crounse, K.R., Roska, T., Chua, O.: Multi-scale image analysis on the CNN universal machine. In: Proc. 4th IEEE International Workshop on Cellular Neural Networks and Their Applications, pp. 69–74 (1996)

    Google Scholar 

  14. Kushleyev, A., Likhachev, M.: Time-bounded lattice for efficient planning in dynamic environments. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 4303–4309 (2009)

    Google Scholar 

  15. Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, Boston (1992)

    Google Scholar 

  16. Manganaro, G., Arena, P., Fortuna, L.: Cellular neural networks, chaos, complexity and VLSI processing. Springer (1999)

    Google Scholar 

  17. Reljin, B.D., Bakic, P.R., Kostic, P.D., Brzakovic, D.P., Vujovi, N.S.: Local enhancement of images using cellular neural networks. In: Proc. 8th ISTET, International Symposium on Theoretical Electrical Engineering, pp. 192–195 (1995)

    Google Scholar 

  18. Siegwart, R., Nourbakhsh, I.R.: Autonomous Mobile Robots. MIT Press, London (2002)

    Google Scholar 

  19. Siemiątkowska, B., Chojecki, R., Olszewski, M., Zajac, M.: Mobile robot navigation using the cellular neural network. In: Proceedings of Romansy, pp. 131–138. Springer, Heidelberg (2008)

    Google Scholar 

  20. Smierzchalski, R., Michalewicz, Z.: Path planning in dynamic environments. In: Innovations in Robot Mobility and Control, pp. 135–153 (2005)

    Google Scholar 

  21. Targalio, S., Zanela, A.: Cellular neural networks: a genetic algorithm for parameters optimization in artificial vision application. In: Proc. 4th IEEE International Workshop on Cellular Neural Networks and Their Applications, pp. 315–320 (1996)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Automatic Control and Robotics, Warsaw University of Technology, ul. Boboli 8, 02-525, Warsaw, Poland

    Maciej Przybylski & Barbara Siemiątkowska

Authors
  1. Maciej Przybylski
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  2. Barbara Siemiątkowska
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Przybylski, M., Siemiątkowska, B. (2012). A New CNN-Based Method of Path Planning in Dynamic Environment. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_58

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  • DOI: https://doi.org/10.1007/978-3-642-29350-4_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29349-8

  • Online ISBN: 978-3-642-29350-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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