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A New Approach of BK products of Fuzzy Relations for Obstacle Avoidance of Autonomous Underwater Vehicles

  • Bui, Le-Diem (Department of Computer Science and Research Institute of Computer and Information Communication Gyeongsang National University) ;
  • Kim, Yong-Gi (Department of Computer Science and Research Institute of Computer and Information Communication Gyeongsang National University)
  • Published : 2004.09.01

Abstract

This paper proposes a new heuristic search technique for obstacle avoidance of autonomous underwater vehicles equipped with a looking ahead obstacle avoidance sonar. We suggest the fuzzy relation between the sonar sections and the properties of real world environment. Bandler and Kohout's fuzzy relational method are used as the mathematical implementation for the analysis and synthesis of relations between the partitioned sections of sonar over the real-world environmental properties. The direction of the section with optimal characteristics would be selected as the successive heading of AUVs for obstacle avoidance. For the technique using in this paper, sonar range must be partitioned into multi equal sections; membership functions of the properties and the corresponding fuzzy rule bases are estimated heuristically. With the two properties Safety, Remoteness and sonar range partitioned in seven sections, this study gives the good result that enables AUVs to navigate through obstacles in the optimal way to goal.

Keywords

References

  1. Antonelli, G., Chiaverini, S., Finotello, R. and Schiavon, R., 'Real-time path planning and obstacle avoidance for RAIS: an autonomous underwater vehicle', Oceanic Engineering, IEEE Journal of, Volume: 26, Issue: 2, April 2001, pp. 216-227
  2. Anvar, A. M., 'Intelligent navigation process for autonomous underwater vehicles (AUVs) using time-based fuzzy temporal reasoning', Temporal Representation and Reasoning, 2003 and Fourth International Conference on Temporal Logic, Proceedings, 10th International Symposium on, 8-10 July 2003, pp. 56-61
  3. Bandler, W. and Kohout, L. J., 'Fuzzy Relational Products as a Tool for Analysis and Synthesis of the Behavior of Complex natural and Artificial System', in: Wang, S. K, and Chang, P. P. eds., Fuzzy Sets: Theory and Application to Analysis and Information Systems, Plenum Press, New York, 1980, pp. 341-367
  4. Bandler, W. and Kohout, L. J., 'Semantics of Implication Operators and Fuzzy Relational Products', Inti. Journal of Man-Machine Studies, 1980
  5. Bandler, W. and Kohout, L. J., 'Special properties. closures and interiors of crisp and fuzzy relations', Fuzzy sets and Systems 26(3), June 1988, pp. 317-332 https://doi.org/10.1016/0165-0114(88)90126-1
  6. Hyeokki Kwon, II Im and Van de Walle, B., 'Are you thinking what I am thinking? A comparison of decision makers' cognitive maps by means of a new similarity measure', System Sciences, 2002, HICSS, ]Proceedings of the 35th Annual Hawaii International Conference on, 7-10 Jan. 2002
  7. Hyland, J. C. and Taylor, F. J., 'Mine avoidance techniques for underwater vehicles', Oceanic Engineering, IEEE Journal of, Volume: 18, Issue: 3, July 1993, pp. 340-350
  8. Kohout L. J and Kim E., 'Semiotic descriptors in fuzzy relational computations', In: Albus JH, Meystel A (eds) Proc IEEE Int Symp Intelligent Control, IEEE Int Symp Computational Intelligence in Robotics and Autonomous and Intelligent Systems and Semiotic (A Joint Conf Science and Technology of Intelligent Systems), Piscataway, 1998, pp. 828-833
  9. Kohout L. J and Kim E., 'The role of BK-products of Relations in Soft Computing', Soft Computing 6, SpringerVerlas, 2002, pp.92-115 https://doi.org/10.1007/s005000100146
  10. Kohout L. J., Keravnou, E., and Bandler, W., 'Automatic Documentary Information Retrieval by Means of Fuzzy Relational Products', In Gaines, B. R., Zadeh, L. A. and Zimmermann, H. J., editors, Fuzzy Sets in Decision Analysis, North-Holland, Amsterdam, 1984, pp. 308-404
  11. Kohout, L. J. and Bandler, W., 'Fuzzy relational products in knowledge engineering', In V. Novak et aI., editor, Fuzzy Approach to Reasoning and Decision Making, pp. 51-66, Academia and Kluwer, Prague and Dordrecht, Zchec 1992
  12. Kohout, L. J. and Harris, M., 'Computer Representation of Fuzzy and crisp relations by means ofthreaded trees using foresets and aftersets', Journal of fuzzy logic and intelligent System, Vol.3, no. 1,1993
  13. Kohout, L. J., Granville, B., and Kim, E., 'Granular Relational Computing with Semiotic Descriptors Using BK-Products ofFuzzy Relations', Computing with Words. John Wiley & Sons, Inc., New York, NY, USA 2001
  14. Lee, Young-II and Kim, Yong-Gi, 'An intelligent navigation system for AUVs using fuzzy relational products', IFSA World Congress and 20th NAFIPS International Conference, 2001, Joint 9th Volume: 2, 25-28 July 2001, pp. 709-714 vol.2
  15. Lee, Young-I1, Kim, Yong-Gi and Kohout, L. J, 'An Intelligent Collision Avoidance System for AUVs using Fuzzy Relational Products', Information Sciences, Elsevier, Vol. 158 (2004) pp. 209-232 https://doi.org/10.1016/j.ins.2003.07.003
  16. Lee, Young-il, Noe, Chan-Sook and Kim, Yong-Gi, 'Implication operators in fuzzy relational products for a local path-planning of AUVs', Fuzzy Information Processing Society, 2002, Proceedings, NAFIPS, 2002 Annual Meeting of the North American, 27-29 June 2002, pp. 221-226
  17. Liu, Xuemin, Pengm, Liang, Li, Jiawei, and Xu, Yuru, 'Obstacle Avoidance using fuzzy neural networks', Underwater Technology, 1998. Proceedings of the 1998 International Symposium on, 15-17 April 1998, pp. 282-286
  18. Ong, S. M., A Mission Planning knowledge-based system with Three-Dimensional Path Optimization for the NPS Model 2 Autonomous Underwater Vehicle, Master's Thesis, Naval Postgraduate School, 1990
  19. Petillot, Y., Tena Ruiz, I, and Lane, D. M, 'Underwater Vehicle Obstacle Avoidance and Path Planning Using a Multi-Beam Forward Looking Sonar', IEEE journal of, Volume: 26, No: 2, April 2001, pp.240-251
  20. Saffiotti, A. 'The Uses of Fuzzy Logic in Autonomous Robot Navigation'. Soft Computing 1, Springer-Verlas, 1997, pp.180-197 https://doi.org/10.1007/s005000050020
  21. Sayyaadi, H., Ura, T. and Fujii, T., 'Collision avoidance controller for AUV systems using stochastic real value reinforcement learning method', SICE 2000, Proceedings of the 39th SICE Annual Conference, International Session Papers, 26-28 July 2000, pp. 165-170
  22. Zadeh, Lotti: 'Fuzzy Sets' Information and Control. 8(3), pp.338-353, 1965 (Cited by [Klir 1995, 1997], [Bonissone 1997]) https://doi.org/10.1016/S0019-9958(65)90241-X