Abstract
Cognition and Reasoning with uncertain and partial knowledge is a challenge for autonomous mobile robotics. Previous robotics systems based on a purely logical or geometrical paradigm are limited in their ability to deal with partial or uncertain knowledge, adaptation to new environments and noisy sensors. Representing knowledge as a joint probability distribution increases the possibility for robotics systems to increase their quality of perception on their environment and helps them to take the right actions towards a more realistic and robust behavior. Dealing with uncertainty is thus a major challenge for robotics in a real and unconstrained environment. Here, we propose a new formalism and methodology called Bayesian Programming which aims at the design of efficient robotics systems evolving in a real and uncontrolled environment. The formalism will be exemplified and validated by two interesting experiments.
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References
Lozano-Prez, T., Jones, J., Mazer, E., O’Donnell, P.: HANDEY, A Robot Task Planner. The MIT Press, Cambridge (1992) ISBN 0-262- 12172-7
Lebeltel, O., Bessire, P., Diard, J., Mazer, E.: Bayesian robots programming. Autonomous Robot 16-1, 49–79,1 (2004)
Mekhnacha, K., Mazer, E., Bessire, P.: A robotic cad system using a bayesian framework. In: Int. Conf. on Intelligent Robots and Systems, Takamatsu, Japan, October 2000, vol. 3, pp. 1597–1604 (2000)
Koike, C., Pradalier, C., Bessire, P., Mazer, E.: Proscriptive bayesian programming application for collision avoidance. In: Proc. of the IEEE-RSJ Int. Conf. on Intelligent Robots and Systems, Las Vegas, USA, October 2003, vol. 1, pp. 394–399 (2003)
Bellot, D., Boyer, A., Charpillet, F.: Designing smart agent based telemedicine systems using dynamic bayesian networks: an application to kidney disease people. In: Proc. HealtCom 2002, Nancy, France, pp. 90–97 (2002)
Pearl, J.: Causality - Models, reasoning and inference. Cambrige University Press, Cambrige (2001)
Rish, I.: Efficient Reasoning in Graphical Models. PhD thesis, University of California, Irvine (1999)
Jordan, M.I., Ghahramani, Z., Jaakkola, T., Saul, L.K.: An introduction to variational methods for graphical models. Machine Learning 37(2), 183–233 (1999)
MacKay, D.J.: Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge (2003)
Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House, Norwood (2000)
Jazwinsky, A.H.: Stochastic Processes and Filtering Theory. Academic Press, New York (1970)
Cou, C., Pradalier, C., Laugier, C.: Bayesian programming for multi-target tracking: an automotive application. In: Proceedings of the International Conference on Field and Service Robotics, Lake Yamanaka, Japan, vol. 7 (2003)
Lamon, P., Nourbakhsh, I., Jensen, B., Siegwart, R.: Deriving and matching image fingerprint sequences for mobile robot localization. In: Proceedings of the International Conference on Robotics and Automation, Seoul, Korea, May 2001, vol. 2, pp. 1609–1614 (2001)
Lamon, P., Tapus, A., Glauser, E., Tomatis, N., Siegwart, R.: Environmental modeling with fingerprint sequences for topological global localization. In: Proceedings of the International Conference on Intelligent Robots and Systems, Las Vegas, USA, October 2003, vol. 4, pp. 3781–3786 (2003)
Martinelli, A., Tapus, A., Siegwart, R.: Multi-resolution slam for real world navigation. In: 11th International Symposium of Robotics Research, Siena, Italy (October 2003)
Arras, K.O., Siegwart, R.: Feature extraction and scene interpretation for map-based navigation and map building. In: Proceedings of the Symposium on Intelligent Systems and Advanced Manufacturing, Pittsburgh, USA (October 1997)
Kanade, T., Ohta, Y.: Stereo by intra- and inter- scanline search dynamic programming. IEEE Transactions on pattern analysis and machine intelligence PALMZ (March 1985)
Bilmes, J.A.: A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. ICSI-TR-97-021 (1997)
Needleman, S., Wunsch, C.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal Molecular Biology 48 (1970)
Cassandra, A.R., Kaelbling, L., Kurien, J.: Acting under uncertainty: Discrete bayesian models for mobile robot navigation. In: Proceedings of the International Conference on Robotics and Automation, Osaka, Japan, November 1996, vol. 2, pp. 963–972 (1996)
Tomatis, N., Nourbakhsh, I., Siegwart, R.: Hybrid simultaneous localization and map building: a natural integration of topological and metric. Robotics and Autonomous Systems 44, 3–14 (2003)
Tapus, A., Heinzer, S., Siegwart, R.: Bayesian programming for topological global localization with fingerprints. International Conference on Robotics and Automation , New Orleans, USA (March 2004)
Kalman, R.: A new approach to linear filtering and prediction problems. Journal of basic Engineering 35 (Mars1960)
Smyth, P., Heckerman, D., Jordan, M.: Probabilistic Independance Networks for Hidden Markov Probability Models. Tech. Rep. MSR-TR-96-03, Microsoft Research (June 1996)
Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 257–285 (1989)
Brockwell, P., Davis, R.: Introduction to Time Series and Forecasting. Springer, Heidelberg (2002)
Ghahramani, Z., Jordan, M.: Factorial hidden Markov models. MIT Computational Cognitive Science Report Technical Report 9502, MIT (1995)
Jordan, M.: Graphical models. Statistical Science (Special Issue on Bayesian Statistics), p. in press (2002)
Bellot, D., Bessire, P.: Approximate discrete probability distribution representation using a multi-resolution binary tree. In: ICTAI 2003, Sacramento, California, USA (2003)
Getoor, L., Friedman, N., Koller, D., Taskar, B.: Learning probabilistic models of relational structure. In: Eighteenth International Conference on Machine Learning (ICML), Williams College, vol. 06 (2001)
Tong, S., Koller, D.: Active learning for structure in bayesian networks. In: Seventeenth International Joint Conference on Artificial Intelligence, Seattle, Washington, August 2001, pp. 863–869 (2001)
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Bellot, D., Siegwart, R., Bessière, P., Tapus, A., Coué, C., Diard, J. (2004). Bayesian Modeling and Reasoning for Real World Robotics: Basics and Examples. In: Iida, F., Pfeifer, R., Steels, L., Kuniyoshi, Y. (eds) Embodied Artificial Intelligence. Lecture Notes in Computer Science(), vol 3139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27833-7_14
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DOI: https://doi.org/10.1007/978-3-540-27833-7_14
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