Intelligent Adaptive Mobile Robot Navigation
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
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.
- Acosta, C., Moras, R. G. (1990) Path planning simulation for a mobile robot. Computer and Industrial Engineering 19: pp. 346-350
- Bezdek, J.: Pattern Recognition With Fuzzy Objective Function: Algorithms, Plenum Press, 1981.
- Chiu, S. L. (1994) Fuzzy model identification based on cluster estimation. J. Intelligent and Fuzzy Systems 2: pp. 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., Murray, R. M. (1994) A motion planner for non-holonomic mobile robot. IEEE Trans. Robotics Automat. 10: pp. 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. (1999) Approche neuro-floue pour la modelisation et la commande des systèmes nonlinéaires multi-variables ‘application à la robotique’. University of Paris XII, France
- Nguyen, D. H., Widrow, B. (1990) Neural networks for self-learning control systems. IEEE Control System Magazine 10: pp. 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, , Micaelli, A. (1996) Modelling and Feedback control of mobile robots equipped with several steering wheels. IEEE Trans. Robotics Automat. 12: pp. 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., Mendel, J. M. (1992) Fuzzy basis functions, universal approximation, and orthogonal least squares learning. IEEE Transaction Neural Networks 3: pp. 807-814
- Wang, L. X., Mendel, J. M. (1995) Design and analysis of fuzzy identifiers of non-linear dynamic systems. IEEE Transaction Automatic Control 40: pp. 11-23
- Williams, R. J., Zipser, D. (1989) A learning algorithm for continually running fully recurrent neural network. Neural Comput. 1: pp. 270-280
- Intelligent Adaptive Mobile Robot Navigation
Journal of Intelligent and Robotic Systems
Volume 30, Issue 4 , pp 311-329
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers
- Additional Links
- fuzzy c-means
- mobile robotics
- Industry Sectors
- Author Affiliations
- 1. Department of Engineering and Technology, Manchester Metropolitan University, John Dalton building, Chester street, Manchester, M1, 5GD, UK
- 2. K.U. Leuven, PMA, Celestijnenlaan 300B, 3001, Heverlee, Belgium
- 3. LIIA (Laboratoire d'informatique industriel et de l'automatique), Université Paris XII, 122, Rue Paul Armangot, 94400, Vitry sur Seine, France