Abstract
Possibility theory offers a nice setting for information combination or data fusion. This attractiveness arises from the elastic constraints that govern the basic concepts pertaining to this theory. Consequently, many combination modes are available ranging from the conjunctive to the disjunctive passing through the compromise mode. Therefore the problem of what is the suitable combination mode for a given situation is still open. The adaptive rule proposed by Dubois and Prade contributes partly to this problem, and has been successfully employed in several applications like robotics. In this paper we apply the recently new combination rule referred to as progressive rule, which permits us to handle robustness with respect to shape modelling and takes account for a possible presence of erroneous information, to mobile robotics context. The rule explicitly accounts for the distance between each alternative and the consensus zone. The rule is then incorporated into a general scheme of fusion methodology, which allows a transformation of raw inputs into meaningful and homogeneous information that will be refined by the progressive rule. A robotics application corresponding to mobile robot localization in a structured environment is carried out. The feasibility of the possibilistic approach is demonstrated by a comparison with a standard method based on Kalman filter.
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References
Abidi, M. A. and Gonzalez, R. C.: Data Fusion in Robotics and Machine Intelligence, Academic Press, New York, 1992.
Arkin, R. C.: Behavior-Based Robotics, MIT Press, Cambridge, MA, 1998.
Bar-Shalom, Y. and Li, X. R.: Estimation and Tracking: Principles, Techniques and Software, Artech House, Boston, 1993.
Benreguig, M., Hopenot, P., Maaref, H., Colle, E., and Barret, C.: Fuzzy navigation strategy: Application to two distinct autonomous mobile robots, Robotica 15 (1997), 609–615.
Bezdek, J. C.: Pattern Rrecognition with Fuzzy Objective Function: Algorithms, Plenum Press, New York, 1981.
Bender, H. and Näther, W.: Fuzzy Data Analysis, Kluwer Academic Publishers, Dordrecht, 1992.
Boinissone, P. P., Kanal, L. N., and Lemmer, J. F.: Uncertainty in Artificial Intelligence, North-Holland, Amsterdam, 1991.
Coolen, F. P. A.: Statistical modelling of expert opinions using imprecise probabilities, PhD Thesis, Eindhoven University of Technology, The Netherlands, 1994.
Cox, I. J.: Blanche, an experiment in guidance and navigation of an autonomous robot vehicle, IEEE Trans. Robotics Automat. 7 (1991), 193–204.
Crowley, J. L.: Word modelling and position estimation for a mobile robot using ultrasonic ranging, in: Proc. of Internat. Conf. on Robotics and Automation, Scottsdale, 1989, pp. 674–680.
Dasarathy, V. B.: Decision fusion strategies in multisensor environments, IEEE Trans. Systems Man Cybernet. 21 (1991), 1140–1154.
Dong, W. and Shah, H. C.: Vertex method for computing functions of fuzzy variables, Fuzzy Sets Systems 24 (1987), 65–78.
Dubois, D. and Prade, H.: Théorie des Possibilities. Application à la Représentation des Connaissances en Informatique, Masson, Paris, 1985.
Dubois, D. and Prade, H.: Fuzzy sets in approximate reasoning: Part 1: Inference with possibility distributions, Fuzzy Sets Systems (25th anniversary memorial volume) 40 (1991), 143–202.
Dubois, D. and Prade, H.: Possibility theory and data fusion in poorly informed environment, Control Engrg. Practice 2 (1994), 812–823.
Durrieu, C., Aldon, M. J., and Meizel, D.: Multisensor data fusion for localization in a mobile robotics, Revue de Traitement de Signal (1997), 143–166 (in French).
Hall, D. and Llinas, J.: An introduction to multisensor data fusion, Proc. IEEE 85 (1997), 6–23.
Leonard, J. L. and Durrant-White, H. F.: Directed Sonar Sensing for Mobile Robot Navigation, Kluwer Academic Publishers, Dordrecht, 1992.
Luo, R. C. and Kay, M. K.: Multisensor integration and fusion in intelligent systems, in: M. A. Abidi and R. C. Gonzalez (eds), Data Fusion in Robotics and Machine Intelligence, Academic Press, New York, 1992, pp. 7–108.
Oussalah, M.: Data fusion in the framework of possibility theory. Application of mobile robot localization, PhD Thesis, University of Evry Val Essonne, France, 1998 (in French).
Oussalah, M.: Study of adaptive combination rules: Algebraical properties, Fuzzy Sets Systems 3(16) (2000), 391–409.
Oussalah, M., Maaref, H., and Barret, C.: Positioning of a mobile robot with landmark-based method, in: Proc. of IROS'97, Grenoble, France, 1997, pp. 865–871.
Oussalah, M., Maaref, H., and Barret, C.: New fusion methodology approach and application to mobile robotics: Investigation in the framework of possibility theory, Internat. J. Inform. Fusion 2(1) (2001), 31–48.
Oussalah, M., Maaref, H., and Barret, C.: From adaptive combination rule to progressive rule, Fuzzy Sets Systems (in press).
Oxenham, M. G., Kewley, D. J., and Nelson, M. J.: Measure of information for multi-level data fusion, in: Proc. of SPIE, Vol. 2755, 1996, pp. 271–282.
Pannerec, T., Oussalah, M., Maaref, H., and Barret, C.: Absolute localisation of a miniature mobile robot using heterogeneous sensors. Comparison between Kalman filter and possibility theory method, in: Proc. of CESA'98, IEEE Symposium on Robotics and Cybernetics, Tunis, April 1998.
Patrouix, O. and Jouvencel, J.: Range information extraction using U_BAT an ultrasonic based aerial telemeter, in: Proc. of Internat. Conf. on Robotics and Automation, 1993, pp. 1460–1465.
Sandri, S. A.: Combination of uncertain information and its algorithmic aspects, PhD Thesis, University Paul Sabatier of Toulouse, 1993 (in French).
Talluri, R. and Aggarwal, J. K.: Position estimation techniques for an autonomous mobile robot – a review, in: C. H. Chen, L. F Pau, and P. S. P. Wang (eds), Handbook of Pattern Recognition & Computer Vision, World Scientific, Singapore, 1993, pp. 769–801.
Yager, R. R.: On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Trans. Systems Man Cybernet. 18 (1988), 183–190.
Yager, R. R.: New modes of OWA Information fusion, Internat. J. Intelligent Systems 13 (1998), 661–681.
Filev, D. and Yager, R. R.: An adaptive approach to defuzzification based on level sets, Fuzzy Sets Systems 54 (1993), 355–360.
Zadeh, L. A.: Fuzzy sets as a basis for a theory of possibility theory, Fuzzy Sets Systems 1 (1978) 3–28.
Zadeh, L. A.: The concept of linguistic variable and its application to approximate reasoning, Part 1, 2 and 3, Inform. Sci. 8 (1975), 43–80, 199–249, 301–357.
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Oussalah, M., Maaref, H. & Barret, C. Application of a Possibilitic-Based Approach to Mobile Robotics. Journal of Intelligent and Robotic Systems 38, 175–195 (2003). https://doi.org/10.1023/A:1027310130169
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DOI: https://doi.org/10.1023/A:1027310130169