Abstract
This paper deals with the application of a neuro-fuzzy inference system to a mobile robot navigation in an unknown, or partially unknown environment. The final aim of the robot is to reach some pre-defined goal. For this purpose, a sort of a co-operation between three main sub-modules is performed. These sub-modules consist in three elementary robot tasks: following a wall, avoiding an obstacle and running towards the goal. Each module acts as a Sugeno–Takagi fuzzy controller where the inputs are the different sensor information and the output corresponds to the orientation of the robot. The rule-base is generated by the controller after some learning process based on a neural architecture close to that used by Wang and Menger. This leads to adaptive neuro-fuzzy inference systems (ANFIS) (one for each module). The adaptive navigation system (ANFIS), based on integrated reactive-cognitive parts, learns and generates the required knowledge for achieving the desired task. However, the generated rule-base suffers from redundancy and abundance of data, most of which are less useful. This makes the assignment of a linguistic label to the associated variable difficult and sometimes counter-intuitive. Consequently, a simplification phase allowing elimination of redundancy is required. For this purpose, an algorithm based on the class of fuzzy c-means algorithm introduced by Bezdek and we have developed an inclusion structure. Experimental results confirm the meaningfulness of the elaborated methodology when dealing with navigation of a mobile robot in unknown, or partially unknown environment.
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References
Acosta, C. and Moras, R. G.: Path planning simulation for a mobile robot, Computer and Industrial Engineering 19(1990), 346-350.
Bezdek, J.: Pattern Recognition With Fuzzy Objective Function: Algorithms, Plenum Press, 1981.
Chiu, S. L.: Fuzzy model identification based on cluster estimation, J. Intelligent and Fuzzy Systems 2(1994), 267-278.
Jang, J. S. R., Sun, C. T., and Mizutani, E.: Neuro-Fuzzy and Soft Computing, Prentice-Hall, 1997.
Latombe, J. C.: Robot Motion Planning, Kluwer Academic Publishers, 1991.
Laumond, J. P., Jacobs, P. E., Tiax, M., and Murray, R. M.: A motion planner for non-holonomic mobile robot, IEEE Trans. Robotics Automat. 10(5) (1994), 577-593.
Merklinger, A.: Performance data of dead recording procedures for non-guided vehicles, in: Proc. of the Int. Workshop on Information Processing in Autonomous Mobile Robots, Munchen, Germany, 1991.
Nefti, S.: Approche neuro-floue pour la modelisation et la commande des systèmes nonlinéaires multi-variables ‘application à la robotique’, PhD thesis, University of Paris XII, France, July 1999.
Nguyen, D. H. and Widrow, B.: Neural networks for self-learning control systems, IEEE Control System Magazine 10(3) (1990), 18-23.
Rohwer, R.: The moving targets training algorithm, in: Touretzkey (ed), Advances in Neural Information Processing Systems, Vol. 2, Morgan Kaufmann, 1990.
Takagi, T. and Sugeno, M.: Derivation of fuzzy logic control rules from human operated control actions, in: IFAC Symposium on fuzzy Information, Knowledge Representation and Decision Analysi, France, 1990, pp. 55-60.
Thuilot, B. B., Andrea-Novel, and Micaelli, A.: Modelling and Feedback control of mobile robots equipped with several steering wheels, IEEE Trans. Robotics Automat. 12(3) (1996), 375-390.
Wang, L. X. and Mendel, J. M.: Generating fuzzy rules by learning from examples, in: Proc. of 6th IEEE International Symposium on Intelligent Control, Washington D.C. 1991, pp. 263-268.
Wang, L. X. and Mendel, J. M.: Backpropagation fuzzy systems as non-linear dynamic system identifiers, in: Proc. of IEEE International Conference on Fuzzy Systems, 1992, pp. 1409-1418.
Wang, L. X. and Mendel, J. M.: Fuzzy basis functions, universal approximation, and orthogonal least squares learning, IEEE Transaction Neural Networks 3(5) (1992), 807-814.
Wang, L. X. and Mendel, J. M.: Design and analysis of fuzzy identifiers of non-linear dynamic systems, IEEE Transaction Automatic Control 40(1) (1995), 11-23.
Williams, R. J. and Zipser, D.: A learning algorithm for continually running fully recurrent neural network, Neural Comput. 1(1989), 270-280.
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Nefti, S., Oussalah, M., Djouani, K. et al. Intelligent Adaptive Mobile Robot Navigation. Journal of Intelligent and Robotic Systems 30, 311–329 (2001). https://doi.org/10.1023/A:1011190306492
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DOI: https://doi.org/10.1023/A:1011190306492