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Soft Computing Based Pattern Classifiers for the Obstacle Avoidance Behavior of Intelligent Autonomous Vehicles (IAV)

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Abstract

To ensure more autonomy and intelligence with real-time processing capabilities for the obstacle avoidance behavior of Intelligent Autonomous Vehicles (IAV), the use of soft computing is necessary to bring this behavior near to that of humans in the recognition, learning, adaptation, generalization, reasoning and decision-making, and action. In this paper, pattern classifiers of spatial obstacle avoidance situations using Neural Networks (NN), Fuzzy Logic (FL), Genetic Algorithms (GA) and Adaptive Resonance Theory (ART) individually or in combination are suggested. These classifiers are based on supervised learning and adaptation paradigms as Gradient Back-Propagation (GBP), FL, GA and Simplified Fuzzy ArtMap (SFAM) resulting in NN/GBP and FL as Intelligent Systems (IS) and in NN/GA, NN/GA-GBP, NN-FL/GBP and NN-FL-ART/SFAM as Hybrid Intelligent Systems (HIS). Afterwards, a synthesis of the suggested pattern classifiers is presented where their results and performances are discussed as well as the Field Programmable Gate Array (FPGA) architectures, characterized by their high flexibility and compactness, for their implementation.

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References

  1. O. Azouaoui and A. Chohra, “Evolution, behavior, and intelligence of autonomous robotic systems (ARS),” in Proc. 3rd Int. IFAC Conf. Intelligent Autonomous Vehicles, Madrid, Spain, March 25–27, 1998, pp. 139–145.

  2. B. Bosacchi and I. Masaki, “Fuzzy logic technology & the intelligent highway system (IHS),” in Proc. 2nd Int. IEEE Conf. Fuzzy Systems, vol. I, San Francisco, CA, 1993, pp. 65–70.

  3. T. Fukuda, F. Arai, and K. Shimojima, “Intelligent robotic system,” in Proc. Int. Multiconf. Computational Engineering in Systems Applications, France, 1996, pp. 1–10.

  4. T. Shibata and T. Fukuda, “Coordinative behavior in evolutionary multi-agent system by genetic algorithm,” in Proc. Int. IEEE Conf. Neural Networks, vol. I, San Francisco, CA, 1993, pp. 209–214.

  5. T. Tanaka, Y. Kojima, J. Ohwi, K. Yamafuji, and S.V. Ulyanov, “Intelligent control of technology operations for robot of service use with manipulator,” in Proc. Int. IMACS IEEE-SMC Multiconf. Computational Engineering in Systems Applications, France, 1996, pp. 788–793.

  6. T. Tanaka, J. Ohwi, L.V. Litvintseva, K. Yamafuji, and S.V. Ulyanov, “Soft computing algorithms for intelligent control of a mobile robot for service use Part I and Part II,” Soft Computing, vol. 1, no. 2, pp. 88–106, June 1997.

    Google Scholar 

  7. T. Fujii and T. Ura, “Development of an autonomous underwater robot 'Twin-Burger' for testing intelligent behaviors in realistic environments,” Autonomous Robots, vol. 3, pp. 285–296, 1996.

    Google Scholar 

  8. C. Kujawski, “Deciding the behaviour of an autonomous mobile road vehicle,” in Proc. 2nd Int. IFAC Conf. Intelligent Autonomous Vehicles, Helsinki, Finland, 1995, pp. 404–409.

  9. D.B. Marco, A.J. Healey, and R.B. McGhee, “Autonomous underwater vehicles: Hybrid control of mission and motion,” Autonomous Robots, vol. 3, no. 2/3, pp. 169–186, 1996.

    Google Scholar 

  10. S. McMillan, D.E. Orin, and R.B. McGhee, “Efficient dynamic simulation of an unmanned underwater vehicle with a manipulator,” in Proc. Int. Conf. On Robotics and Automation, vol. 2, CA, May 8–13, 1994, pp. 1133–1140.

    Google Scholar 

  11. W. Niegel, “Methodical structuring of knowledge used in an intelligent driving system,” in Proc. 2nd Int. Conf. Intelligent Autonomous Vehicles, Finland, 1995, pp. 398–403.

  12. K. Schilling and C. Jungius, “Mobile robots for planetary exploration,” in Proc. 2nd Int. IFAC Conf. Intelligent Autonomous Vehicles, Helsinki, Finland, 1995, pp. 110–120.

  13. M. Vainio, P. Appelqvist, T. Schönberg, and A. Halme, “Group behavior of a mobile underwater robot society destroying distributed targets in a closed process environment,” in Proc. 3rd Int. IFAC Conf. Intelligent Autonomous Vehicles, Madrid, Spain, March 25–27, 1998, pp. 112–117.

  14. H.H.Wang, S.M. Rock, and M.J. Lee, “OTTER: The design and development of an intelligent underwater robot,” Autonomous Robots, vol. 3, pp. 297–320, 1996.

  15. I. Ashiru, C. Czarnecki, and T. Routen, “Characteristics of a genetic based approach to path planning for mobile robots,” J. of Network and Computer Applications, vol. 19, pp. 149–169, 1996.

    Google Scholar 

  16. O. Azouaoui and A. Chohra. “Neural group navigation approach for autonomous robotic systems (ARS),” in Proc. Second International ICSC Symposium on Engineering of Intelligent Systems, University of Paisley, Scotland, June 27–30, 2000.

    Google Scholar 

  17. A. Chohra, A. Farah, and C. Benmehrez, “Neural navigation approach for intelligent autonomous vehicles (IAV) in partially structured environments,” Int. J. of Applied Intelligence, vol. 8, no. 3, pp. 219–233, 1998.

    Google Scholar 

  18. A. Chohra, R. Tiar, and O. Azouaoui. “Fuzzy motion controller (FMC) for intelligent autonomous vehicles (IAV).” in Proc. Second International ICSC Symposium on Engineering of Intelligent Systems, University of Paisley, Scotland, June 27–30, 2000.

    Google Scholar 

  19. H. Herbstreith, L. Gmeiner, and P. Preuß, “Atarget-directed neurally controlled vehicle,” in Proc. Int. IFAC Conf. Artificial Intelligence in Real-Time Control, Delft, The Netherlands, 1992, pp. 67–71.

  20. A.W. Ho and G.C. Fox, “Neural network near-optimal motion planning for a mobile robot on binary and varied terrains,” IEEE Int. Work. on Int. Rob. and Sys., IROS' 90, 1990, pp. 593–600.

  21. Y. Maeda, “Collision avoidance control amongmoving obstacles for a mobile robot on the fuzzy reasoning,” in Proc. Eight Symp. Theo. and Prac. of Rob. & Man., Cracow, Poland, 1990

  22. M. Maeda, Y. Maeda, and S. Murakami, “Fuzzy drive control of an autonomous mobile robot,” Fuz. Sets and Sys., vol. 39, pp. 195–204, 1991.

    Google Scholar 

  23. M. Maeda, M. Shimakawa, and S. Murakami, “Predictive fuzzy control of an autonomous mobile robot with forecast learning function,”Fuzzy Sets and Systems, vol. 72, no. 1, pp. 51–60, 1995.

    Google Scholar 

  24. M. Meng and A.C. Kak, “Mobile robot navigation using neural networks and nonmetrical environment models,” IEEE Control Systems, pp. 30–39, October 1993.

  25. E. Sorouchyari, “Mobile robot navigation: A neural network approach,” in Proc. Art Coll. Neuro. Eco. Poly., Lausanne, 1989, pp. 159–175.

  26. T. Takeuchi, “An autonomous fuzzy mobile robot,” Advanced Robotics, vol. 5, no. 2, pp. 215–230, 1991.

    Google Scholar 

  27. J. Xiao, Z. Michalewicz, L. Zhang, and K. Trojanowski, “Adaptive evolutionary planner/navigator for mobile robots,” IEEE Trans. on Evolutionary Computation, vol. 1, no. 1, pp. 18–28, 1997.

    Google Scholar 

  28. A.A. Baloch and A.M. Waxman, “Visual learning, adaptive expectations, and behavioral conditioning of the mobile robot MAVIN,” Neural Networks, vol. 4, pp. 271–302, 1991.

    Google Scholar 

  29. A. Chohra, “Fuzzy ArtMap Neural Networks (FAMNN) based navigation for intelligent autonomous vehicles (IAV) in partially structured environments,” in Proc. ICSC Int. IFAC-IEEE Conf. on Neural Computing, Vienna, Austria, Sept. 23–25, 1998, pp. 747–754.

  30. A. Chohra, A. Farah, and M. Belloucif, “Neuro-fuzzy expert system E_S_C_O_V for the obstacle avoidance behavior of intelligent autonomous vehicles (IAV),” Int. J. of Advanced Robotics, vol. 12, no. 6, pp. 629–650, 1999.

    Google Scholar 

  31. A. Dubrawski and J.L. Crowley, “Self-supervised neural system for reactive navigation,” in Proc. Int. IEEE Conf. on Robotics and Automation, vol. 3, San Diego, CA, May 8–13, 1994, pp. 2076–2081.

    Google Scholar 

  32. I. Hiraga, T. Furuhashi, Y. Uchikawa, and S. Nakayama, “An acquisition of operator's rules for collision avoidance using fuzzy neural networks,” IEEE Trans. on Fuzzy Systems, vol. 3, no. 3, pp. 280–287, 1995.

    Google Scholar 

  33. P. Szynkarczyk and A. Masiowski, “The fuzzy artmap neural networks as a controller for the mobile robot,” in Proc. 3rd Int. Symp. on Methods and Models in Automation and Robotics, Miedzyzdroje, Poland, Sept. 10–13, 1996, pp. 1201–1206.

  34. J.A. Anderson, An Introduction to Neural Networks, The MIT Press: Cambridge, MA, London, England, 1995.

    Google Scholar 

  35. J.A. Freeman and D.M. Skapura, Neural Networks: Algorithms, Applications, and Programming Techniques, Addison-Wesley: New York, 1992.

    Google Scholar 

  36. D.E. Goldberg, Algorithmes Génétiques: Exploration, Optimisation et Apprentissage Automatique, Addison-Wesley: France, 1994.

    Google Scholar 

  37. T. Khanna, Foundations of Neural Networks, Addison-Wesley: New York, 1990.

    Google Scholar 

  38. B. Kosko, Neural Networks and Fuzzy Systems, Prentice Hall: Englewood Cliffs, NJ, 1992.

    Google Scholar 

  39. L.R. Medsker, Hybrid Intelligent Systems, Kluwer Academic Publishers: Dordrecht, 1995.

    Google Scholar 

  40. D.W. Patterson, Artificial Neural Networks: Theory and Applications, Prentice Hall, Simon & Schuster (Asia) Pte Ltd: Englewood Cliffs, NJ/Singapore, 1996.

    Google Scholar 

  41. S.T.Welstead, Neural Network and Fuzzy Logic Applications in C/C++, John Wiley & Sons: Toronto, 1994.

    Google Scholar 

  42. P.J. Werbos, “Neurocontrol and fuzzy logic: Connections and designs,” Int. J. of Approximate Reasoning, vol. 6, pp. 185–219, 1992.

    Google Scholar 

  43. S. Cherian and W. Troxell, “Intelligent behavior in machines emerging from a collection of interactive control structures,” Computational Intelligence, vol. 11, no. 4, pp. 565–592, 1995.

    Google Scholar 

  44. S. Thrun and T.M. Mitchell, “Lifelong robot learning,” Robotics and Autonomous Systems, vol. 15, pp. 25–46, 1995.

    Google Scholar 

  45. O. Aycard, F. Charpillet, and D. Fohr, “Place learning and recognition using hidden Markov models,” in Proc. Int. IEEE/RSJ Conf. on Intelligent Robots and Systems, Grenoble, France, 1997, pp. 1741–1746.

  46. Y.S. Kim, I.H. Hwang, J.G. Lee, and H. Chung, “Spatial learning of an autonomous mobile robot using model-based approach,” in Proc. 2nd Int. IFAC Conf. Intelligent Autonomous Vehicles, Helsinki, Finland, 1995, pp. 250–255.

  47. M. Agarwal, “A systematic classification of neural-networkbased control,” IEEE Control Systems, vol. 17, no. 2, pp. 75–93, 1997.

    Google Scholar 

  48. L.A. Zadeh, “Fuzzy sets,” Information & Control, vol. 8, pp. 338–353, 1965.

    Google Scholar 

  49. L.A. Zadeh, “The calculus of fuzzy if/then rules,” AI Expert, pp. 23–27, 1992.

  50. C.C. Lee, “Fuzzy logic in control systems: Fuzzy logic controller, Part I and Part II,” IEEE Trans. on Systems, Man and Cybernetics, vol. 20, no. 2, pp. 404–435, 1990.

    Google Scholar 

  51. M.K. Ciliz and C. Isik, “Fuzzy rule-based motion controller for an autonomous mobile robot,” Robotica, vol. 7, pp. 37–42, 1989.

    Google Scholar 

  52. H. Farreny and H. Prade, “Tackling uncertainty and imprecision in robotics,” in Proc. 3rd Int. Symp. on Robotics Research, Gonvieux, 1985, pp. 85–91.

  53. J. Holland, “Les algorithmes génétiques,” Pour La Science, no. 179, pp. 44–51, Sept. 1992.

  54. H. Ishigami, T. Fukuda, T. Shibata, and F. Arai, “Structure optimization of fuzzy neural network by genetic algorithm,” Fuzzy Sets and Systems, vol. 71, pp. 257–264, 1995.

    Google Scholar 

  55. H. Kitano, “Empirical studies on the speed of convergence of neural network training using genetic algorithms,” in Proc. 8th JMIT National Conf. in Artificial Intelligence, Boston, MA, vol. 2, 1990, pp. 789–795.

    Google Scholar 

  56. D.J. Montana and L. Davis, “Training feedforward neural networks using genetic algorithms,” in Proc. 11th Int. Joint Conf. on Artificial Intelligence, Detroit, MI, Morgan Kaufman: San Mateo, CA, 1989, pp. 762–767.

    Google Scholar 

  57. M. McInerney and A.P. Dhawan, “Use of genetic algorithms with back propagation in training of feed-forward neural networks,” in Proc. Int. IEEE Conf. Neural Networks, vol. I, San Francisco, CA, March 28–April 1 1993, pp. 203–208.

    Google Scholar 

  58. G.A. Carpenter, S. Grossberg, and D.B. Rosen, “Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system,” Neural Networks, vol. 4, pp. 759–771, 1991.

    Google Scholar 

  59. G.A. Carpenter, S. Grossberg, N. Markuzon, J.H. Reynolds, and D.B. Rosen, “Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps,” IEEE Trans. on Neural Networks, vol. 3, no. 5, pp. 698–713, 1992.

    Google Scholar 

  60. T. Kasuba, “Simplified fuzzy artmap,” AI Expert, pp. 18–25, November 1993.

  61. R.K. Awalt, “Making the ASIC/FPGA design,” Integrated System Design, pp. 22–28, July 1999.

  62. O. Azouaoui, “Neural networks based approach for the manipulators inverse Jacobian problem,” in Proc. ICSC Int. IFAC-IEEE Conf. on Neural Computing,Vienna, Austria, Sept. 23–25, 1998, pp. 966–972.

  63. Y.L. El-Haffaf, A.K. Oudjida, and A. Chohra, “Digital artifi-cial neural network architecture for obstacle avoidance suitable for FPGA,” in Proc. Int. Conf. on Engineering Applications of Neural Networks, Sweden, 1997, pp. 313–316.

  64. A.K. Oudjida, “High speed and very compact two's complement serial/parallel multipliers using FPGA,” Int. Conf. on Signal Processing Application and Technology ICSPAT' 96, Boston, USA, Oct. 7–10, 1996, pp. 924–928.

  65. K. Hwang, Computer Arithmetic, Principles, Architecture and Design, John Wiley & Sons: New York, 1979.

    Google Scholar 

  66. H. Ossoinig, E. Reisinger, C. Steger, and R.Weiss, “Design and FPGA-implementation of a neural network,” Int. Conf. on Signal Processing Application and Technology ICSPAT' 96, Boston, USA, Oct. 7–10, 1996, pp. 939–943.

  67. G.R. Goslin, “16-Tap, 8-bit FIR filter application guide,” Xcell Journal, Xilinx, 1994.

  68. G.R. Goslin, “Using FPGAs in digital signal processing application,” Int. Conf. on Signal Processing Application and Technology, 1995, pp. 145–149.

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Authors and Affiliations

  1. Laboratoire de Robotique et d'Intelligence Artificielle, CDTA—Centre de Développement des Technologies Avancées, 128, Chemin Mohamed Gacem, BP 245 El-Madania, 16075, Algiers, Algeria

    Ouahiba Azouaoui

  2. GMD—German National Research Center for Information Technology, Institute for Autonomous Intelligent Systems, Schloss Birlinghoven, D-53754, Sankt Augustin, Germany

    Amine Chohra

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  1. Ouahiba Azouaoui
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  2. Amine Chohra
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Azouaoui, O., Chohra, A. Soft Computing Based Pattern Classifiers for the Obstacle Avoidance Behavior of Intelligent Autonomous Vehicles (IAV). Applied Intelligence 16, 249–272 (2002). https://doi.org/10.1023/A:1014394117908

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