World Academy of Science, Engineering and Technology
Published June 21, 2007 | Version 9475
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Mobile Robot Navigation Using Local Model Networks

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Developing techniques for mobile robot navigation constitutes one of the major trends in the current research on mobile robotics. This paper develops a local model network (LMN) for mobile robot navigation. The LMN represents the mobile robot by a set of locally valid submodels that are Multi-Layer Perceptrons (MLPs). Training these submodels employs Back Propagation (BP) algorithm. The paper proposes the fuzzy C-means (FCM) in this scheme to divide the input space to sub regions, and then a submodel (MLP) is identified to represent a particular region. The submodels then are combined in a unified structure. In run time phase, Radial Basis Functions (RBFs) are employed as windows for the activated submodels. This proposed structure overcomes the problem of changing operating regions of mobile robots. Read data are used in all experiments. Results for mobile robot navigation using the proposed LMN reflect the soundness of the proposed scheme.

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References

  • T. S. Levitt and D. T. Lawton, "Qualitative navigation for mobile robots, "Artificial Intelligence, vol. 44, pp. 305-360, 1990.
  • R. P. DAYAL, Navigation of multiple mobile robots in an unknown environment. Systems Engineering, University of Wales, Cardiff, 2000.
  • O. Trullier, S. I. Wiener and A. Berthoz, "Biologicall based artificial navigation systems: review and prospects, " Progress in Neurobiology, vol. 5, pp. 483-544, 1997.
  • N. Tomatis, I. Nourbakhsh, K. Arras and R. Siegwart, "A Hybrid approach for robust and precise mobile robot navigation with compact environment modeling, " Proceedings of the IEEE, 2001.
  • L. Feng, J. Borenstein and H. R. Everett, "Where am I ? Sensors and methods for autonomous mobile robot positioning, "Technical Report, University of Michigan, MI, 1994.
  • J. Borenstein and Y. Koren, "Noise reflection for ultrasonic sensors in mobile robot application, "Proceedings of IEEE Conference on Robotics and Automation, Nice, France, pp. 1727-1732, 1992.
  • M. Buchberger, K. Jorg and E. V. Puttkamer, " Laserradar and sonar based world modeling and motion control for fast obstacle avoidance of the autonomous mobile robot MOBOT-IV, "Proceedings of IEEE Conference on Robotics and Automation, Atlanta, Georgia, pp. 534-540, 1993.
  • E. Garcia and P. G. de Santos, "Mobile-robot navigation with complete coverage of unstructured environment, "Robotics and Autonomous Systems, vol. 46, pp. 195-204, 2004.
  • I. Aleksander and H. Morton, An Introduction to Neural Computing. London: Chapman & Hall, 1990. [10] K. J. Hunt, D. Sbarbaro, R. Zbikowski and P. J. Gawthrop, "Neural networks for control systems-A survey, "Automatica, vol. 28, pp. 1083- 1112, 1992. [11] S. Haykin, Neural networks: A comprehensive foundation. Macmillan college Publishing Company, Inc., USA, 1994. [12] G. G. Lendaris and C. Paintz, "Training strategies for critic and action neural networks in dual heuristic programming methods,"Proceedings of International Conference on Neural Networks 97, Huston, pp. 712-717, 1997. [13] I. Rivals, D. Canas, L. Personnaz and G. Dreyfus, "Modeling and control of mobile robots and intelligent vehicles by neural network, " IEEE conference on intelligent Vehicles, Paris, France, 1994. [14] H. A. Awad, Fuzzy neural networks for modeling and controlling dynamic systems. Ph.D. Thesis, Cardiff School of Eng., Cardiff Unv., UK, 2001. [15] A. Fink and O. Nelles, "Nonlinear internal model control Based on Local Linear Neural Networks, "Proceedings of the 2001 IEEE, Man, and cybernetics Conference, 2001. [16] R. Murry-Smith, A local model network Approach to nonlinear modeling. PhD thesis, University of Strathclyde, Strathclyde, UK, 1994 [17] M. D. Brown, G. Lightbody and G. W. Irwin, "Nonlinear internal model control using local model networks, "IEE Proc.-Control Theory Appl., Vol. 144, No. 6, November 1997. [18] J. C. Bezdek, Pattern recognition with fuzzy objective functionalgorithms. New York: Plenum Press, 1981. [19] J. Moody and C. Darken, "Fast learning in networks of locally-tuned processing units, "Neural Computation, vol. 1, pp. 281-294, 1989. [20] D. S. Broomhead and D. Lowe, "Multivariable functional interpolation and adaptive networks, "Complex systems, vol. 2, pp. 321-355, 1988. [21] C. L. Chen, W. C. Chen and F. Y. Chang "Hybrid learning algorithm for Gaussian potential function networks, "IEE Proc. D, vol. 140, pp. 442- 448, 1993. [22] H. Huttenrauch, A. Green, M. Nornan, L. Oestreicher and K. S. Eklundh, " Involving users in the design of a mobile office robot, "IEEE Transactions on Systems, Man, and Cybernetics-Part-C: Applications and Reviews, vol. 34, pp. 113-124, 2004.