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Abstract
This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles with more autonomy and intelligence is discussed. Second, the system for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN) obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.
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References
- ANDERSON, J.A. 1995, An Introduction to Neural Networks MIT Press, Cambridge, MA.
- BERNS, K. and DILLMANN, R. 1991: A neural network approach for the control of a tracking behavior. IEEE 7803-0078/0600-0500.
- BOSACCHI and I. MASAKI, I. 1993, Fuzzy logic technology & the intelligent highway system (IHS), in: Proc. 2nd int. IEEE conf. Fuzzy Systems, Vol. 1, pp. 65—70. IEEE Inc., ISBN Soft bound:0-7803-0614-7, San Francisco, CA.
- CHOHRA, A., FARAH, A. and BENMEHREZ, C. 1998, Neural navigation approach for Intelligent Autonomous Vehicles (IAV) in partially structured environments, int. J,Applied. Intelligence, 8 (3): 219-233.
- CHOHRA, A., FARAH, A. and BENABBAS, R. 1996, Neuro-fuzzy navigation approach for autonomous mobile robots in partially structured environments, in: Proc. int. conf. Application of Fuzz Systems and Soft Computing, Siegen, Germany, pp.304-3 13.
- CHOHRA, A., FARAH, A. 1996, Hybrid navigation approach combining neural networks and fuzzy logic for autonomous mobile robots, in: Proc. 3rd int. conf. Motion and Vibration Control. Chiba, Japan.
- CHOHRA, A., FARAH, A. and BELLOUCIF, M. 1999, Neuro-fuzzy expert systm E_S_CO_V for the obstacle avoidance behavior of intelligent autonomous vehicles. Advanced Robotics, 12(6): 667-678.
- CHUEN, C.L. 1990. Fuzzy logic in control systems: fuzzy logic controller, part I & part II.IEEE Trans. on Sys. Man and Cyb., 20(2): 404-435.
- GLORENNEC, P.Y. 1991. Les réseaux neuro-flous évolutifs: un pont entre le flou et le neuronal. INSA, France,.
- HUNG, C.C. 1993. Building a neuro-fuzzy learning control system. AI EXPERT, 40-49.
- MAEDA, Y. 1990: Collision avoidance control among moving obstacles for a mobile robot on the fuzzy reasoning. Eight CISM IFToMM Symp. on Theo. & Prac. of Rob. & Man.
- PIGNON , HASEGAWA and LAUMOND, J.P. 19994. Structuration de l’espace pour les robots mobiles. Revue Scicntifique et Technique de Ia Defense, 2: 17-31.
- ZADEH, L.A. 1992. The calculus of fuzzy If/Then rules. AI Expert, mar.23-27.
References
ANDERSON, J.A. 1995, An Introduction to Neural Networks MIT Press, Cambridge, MA.
BERNS, K. and DILLMANN, R. 1991: A neural network approach for the control of a tracking behavior. IEEE 7803-0078/0600-0500.
BOSACCHI and I. MASAKI, I. 1993, Fuzzy logic technology & the intelligent highway system (IHS), in: Proc. 2nd int. IEEE conf. Fuzzy Systems, Vol. 1, pp. 65—70. IEEE Inc., ISBN Soft bound:0-7803-0614-7, San Francisco, CA.
CHOHRA, A., FARAH, A. and BENMEHREZ, C. 1998, Neural navigation approach for Intelligent Autonomous Vehicles (IAV) in partially structured environments, int. J,Applied. Intelligence, 8 (3): 219-233.
CHOHRA, A., FARAH, A. and BENABBAS, R. 1996, Neuro-fuzzy navigation approach for autonomous mobile robots in partially structured environments, in: Proc. int. conf. Application of Fuzz Systems and Soft Computing, Siegen, Germany, pp.304-3 13.
CHOHRA, A., FARAH, A. 1996, Hybrid navigation approach combining neural networks and fuzzy logic for autonomous mobile robots, in: Proc. 3rd int. conf. Motion and Vibration Control. Chiba, Japan.
CHOHRA, A., FARAH, A. and BELLOUCIF, M. 1999, Neuro-fuzzy expert systm E_S_CO_V for the obstacle avoidance behavior of intelligent autonomous vehicles. Advanced Robotics, 12(6): 667-678.
CHUEN, C.L. 1990. Fuzzy logic in control systems: fuzzy logic controller, part I & part II.IEEE Trans. on Sys. Man and Cyb., 20(2): 404-435.
GLORENNEC, P.Y. 1991. Les réseaux neuro-flous évolutifs: un pont entre le flou et le neuronal. INSA, France,.
HUNG, C.C. 1993. Building a neuro-fuzzy learning control system. AI EXPERT, 40-49.
MAEDA, Y. 1990: Collision avoidance control among moving obstacles for a mobile robot on the fuzzy reasoning. Eight CISM IFToMM Symp. on Theo. & Prac. of Rob. & Man.
PIGNON , HASEGAWA and LAUMOND, J.P. 19994. Structuration de l’espace pour les robots mobiles. Revue Scicntifique et Technique de Ia Defense, 2: 17-31.
ZADEH, L.A. 1992. The calculus of fuzzy If/Then rules. AI Expert, mar.23-27.