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Neural Networks (NN) Using Genetic Algorithms (GA) and Gradient Back-Propagation (GBP) for an Intelligent Obstacle Avoidance Behavior

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Distributed Autonomous Robotic Systems 5

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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 Neural Networks (NN) is necessary to bring this behavior near to that of humans in the recognition, learning, adaptation, reasoning and decision-making, and action. In this paper, three (03) supervised learning algorithms namely Gradient Back-Propagation (GBP), Genetic Algorithms (GA) and GA-GBP are suggested to train a NN to learn spatial obstacle avoidance situations. A synthesis of the suggested NN/GBP, NN/GA and NN/GA-GBP is presented where their results and performances are discussed. Finally, a Field-Programmable Gate Array (FPGA) architecture, characterized by its high flexibility and compactness, is suggested for the NN implementation.

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Author information

Authors and Affiliations

  1. Laboratoire de Vision et Robotique (LVR), Ecole Nationale Supérieure d’Ingénieurs (ENSI) de Bourges, 10 Boulevard Lahitolle, 18020, Bourges, France

    A. Chohra

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

    O. Azouaoui

Authors
  1. A. Chohra
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  2. O. Azouaoui
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Editor information

Editors and Affiliations

  1. Advanced Engineering Center, RIKEN (The Institute of Physical and Chemical Research), 2-1 Hirosawa, 351-0198, Wako-shi, Saitama, Japan

    Hajime Asama (Head of Instrumentation Project Promotion Division) (Head of Instrumentation Project Promotion Division)

  2. Department of Precision Machinery Engineering, The Unviersity of Tokyo, 7-3-1 Hongo, 113-8656, Bunkyo-ku, Tokyo, Japan

    Tamio Arai (Professor) (Professor)

  3. Department of Micro System Engineering, Nagoya University, 464-8603, Furo-cho, Chikusa-ku, Nagoya, Japan

    Toshio Fukuda (Professor) (Professor)

  4. Department of Intelligent Systems Graduate School of Information and Electrical Engineering, Kyushu University, 6-10-1 Hakozaki, 812-8581, Higaschi-ku, Fukuoka, Japan

    Tsutomu Hasegawa (Professor) (Professor)

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© 2002 Springer-Verlag Tokyo

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Chohra, A., Azouaoui, O. (2002). Neural Networks (NN) Using Genetic Algorithms (GA) and Gradient Back-Propagation (GBP) for an Intelligent Obstacle Avoidance Behavior. In: Asama, H., Arai, T., Fukuda, T., Hasegawa, T. (eds) Distributed Autonomous Robotic Systems 5. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65941-9_46

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  • DOI: https://doi.org/10.1007/978-4-431-65941-9_46

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-65943-3

  • Online ISBN: 978-4-431-65941-9

  • eBook Packages: Springer Book Archive

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