T. Jarmo and . Alander, An indexed bibliography of genetic algorithms and simulated annealing : Hybrids and comparisons An essay towards solving a problem in the doctrine of chance, Philosophical Transactions of the Royal Society of London, vol.39, issue.45, pp.72370-418, 1763.

. Bdl-+-99a-]-p, E. Bessì-ere, O. Dedieu, E. Lebeltel, K. Mazer et al., Interprétation ou description (i) : Proposition pour une théorie probabiliste des systèmes cognitifs sensori-moteurs. Intellectica, p.15, 1999.

. Bdl-+-99b-]-p, E. Bessì-ere, O. Dedieu, E. Lebeltel, K. Mazer et al., Interprétation ou description (ii) : Fondements mathématiques de l'approche f+d, Intellectica, vol.8, p.15, 1999.

B. [. Borenstein, L. Everett, and . Feng, Navigating mobile robots : Systems and techniques, Statistical decision theory and bayesian analysis, pp.26-140, 1985.

W. Burgard, D. Fox, D. Hennig, and T. Schmidt, Estimating the absolute position of a mobile robot using position probability grids, AAAI/IAAI, pp.896-901, 1996.

M. [. Box and . Muller, A Note on the Generation of Random Normal Deviates, The Annals of Mathematical Statistics, vol.29, issue.2, pp.610-611, 0191.
DOI : 10.1214/aoms/1177706645

]. G. Bre90 and . Bretthorst, An introduction of parameter estimation using bayesian probability, Maximum Entropy and Bayesian Methods, pp.53-79, 1990.

M. Jose, A. F. Bernardo, and . Smith, Bayesian Theory Coué and P.Bessì ere. Chasing an elusive target with a mobile robot, the IEEE-RSJ Int. Conf. on Intelligent Robots and Systems, Hawaii, HI (USA), pp.140-155, 1994.

C. Coué, . Th, P. Fraichard, E. Bessì, and . Mazer, Multi-sensor data fusion using Bayesian programming: An automotive application, Intelligent Vehicle Symposium, 2002. IEEE, p.15, 2002.
DOI : 10.1109/IVS.2002.1187989

R. T. Coxcox61-]-r, . T. Coxcox78-]-r, and . Cox, Probability, frequency and reasonable expectation The algebra of probable inference Of inference and inquiry : An essay in inductive logic, The Maximum Entropy Formalism, pp.1-131, 1946.

F. Dellaert, W. Burgard, D. Fox, and S. Thrun, Using the CONDENSATION algorithm for robust, vision-based mobile robot localization, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), p.72, 1999.
DOI : 10.1109/CVPR.1999.784976

F. Dellaert, D. Fox, W. Burgard, and S. Thrun, Monte Carlo localization for mobile robots, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C), p.184, 1999.
DOI : 10.1109/ROBOT.1999.772544

A. Doucet, N. J. Gordon, J. F. De-freitasdij59, ]. E. Dijkstradl99, ]. J. Diard et al., Sequential monte carlo methods in practice A note on two problems in connection with graphs Bayesian learning experiments with a khepera robot Bayesian programming and hierarchical learning in robotics, Experiments with the Mini-Robot Khepera : Proceedings of the 1st International Khepera Workshop SAB2000 Proceedings Supplement Book, Honolulu, 2000. Publication of the International Society for Adaptive Behavior. 15 [DLR77] A. Dempster, A. Laird, and D. Rubin. Maximun likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, pp.269-271, 1959.

M. F. De-la-rosa-roseroecr92, ]. B. Espiau, F. Chaumette, P. Rivesfb91-]-e, S. Falkenauer et al., Planification de mouvements avec prise en compte explicite des incertitudes géometriques A new approach to visual servoing in robotics A genetic algorithm for job shop Markov localization for mobile robots in dynamic environments, Proceedings 1991 IEEE International Conference on Robotics and Automation, number Cat. No. 91CH2969-4 in 1, pp.138313-326, 1991.

R. J. Alexandre, G. G. François, and . Medioni, Adaptive color background modeling for real-time segmentation of video streams, the International on Imaging Science, Systems, and Technology, p.67, 1990.

J. J. Grefenstette, Credit assignment in rule discovery systems based on genetic algorithms 72 [Har94] I. Harvey. The artificial evolution of adaptive behaviour, Machine LearningHM95] Radu Horaud and Olivier Monga. Vision par ordinateur, Outils fondamentaux. Editions Hermès, pp.225-264, 1988.

J. H. Holland, Adaptation in natural artificial systems, p.39, 1975.

]. E. Jay and . Jaynes, Probability theory -the logic of science. inachevé, p.12

F. Jurie, P. Rives, J. Gallice, J. L. Bramekal60-]-r, and . Kalman, High-speed vehicle guidance based on vision, Control Engineering Practice, vol.2, issue.2, pp.289-299, 1960.
DOI : 10.1016/0967-0661(94)90210-0
URL : https://hal.archives-ouvertes.fr/inria-00548426

. D. Kbm, R. Kortenkamp, R. Bonassi, and . Murphy, AI-based mobile robots : Case studies of successful robot systems, p.26

A. [. Kaelbling, J. A. Cassandra, and . Kurien, Acting under uncertainty : Discrete bayesian models for mobile-robot navigation Modélisation identification et commande des robots, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.28-56, 1996.

M. [. Kaelbling, A. Littman, ]. S. Cassandraks96, R. Koenig, and . Simmons, Planning and acting in partially observable stochastic domains Passive distance learning for robot navigation, Proceedings of the Thirteenth International Conference on Machine LearningLap95] P.-S. Laplace. Philosophical essay on probabilities, pp.29-56, 1995.

R. [. Liu and . Chen, Sequential Monte Carlo Methods for Dynamic Systems, Journal of the American Statistical Association, vol.24, issue.443, pp.93-132, 1998.
DOI : 10.1073/pnas.94.26.14220

O. Lebeltel, J. Diard, P. Bessì, E. Mazerldh98-]-g, K. M. Lacey et al., A bayesian framework for robotic programming Twentieth International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering The application of robotics to a mobility aid for the elderly blind, MaxEnt, pp.15245-252, 1998.

O. Lebeltellv00-]-s, M. Lenser, and . Veloso, Programmation Bayèsienne des Robots Sensor resetting localization for poorly modelled mobile robots, Procedings of the IEEE International Conference on Robotics & Automation (ICRA), pp.31-84, 1999.

E. Marchand, F. Chaumette, F. Spindler, and M. Perrier, Controlling the manipulator of an underwater rov using a coarse calibrated pan tilt camera Méthodes Probabilistes Bayesiennes pour la prise en compte des Incertitudes Géometriques : ApplicationàApplication`Applicationà la CAO-Robotique, IEEE Int. Conf. on Robotics and Automation, pp.2773-2778, 1999.

H. Moravec, D. B. Gennerymk97-]-g, T. Mclachlan, and . Krishnan, Cart project progress report NASA contract NASW 2916, pp.79-147, 1976.

E. George, ]. M. Monahanmuh92, and . Neal, A survey of partially observable Markov decision processes : Theory, models and algorithms How genetic algorithms really work Probabilistic inference using Markov chain Monte Carlo methods, Management Science, vol.28, issue.28, pp.1-16, 1982.

R. Pissard-gibollet, P. Rives, K. Kapellos, and J. J. Borrelly, Real-time programming of a mobile robot actions using advanced control technics Vérification-Correction de programme pour la prise en compte des incertitudes en programmation automatique des robots Towards personal service robots for the elderly, 4th Int. Symposium on Experimental Robotics Workshop on Interactive Robots and Entertainment, p.184, 1989.

A. Ruiz, P. E. Lopez-de-teruel, and M. C. Garrido, Probabilistic inference from arbitrary uncertainty using mixtures of factorized generalized gaussians, Journal of Artificial Intelligence Research, vol.9, issue.25, pp.167-217, 1998.

R. Y. Rubinstein, Simulation and the Monte Carlo method, 0191.

]. D. Rub88 and . Rubin, Using the SIR algorithm to simulate posterior distribution, Bayesian Statistics, p.27, 1988.

H. [. Redner, . S. Walker-]-r, A. G. Sutton, and . Barto, Mixture Densities, Maximum Likelihood and the EM Algorithm, SIAM Review, vol.26, issue.2, pp.195-239, 1984.
DOI : 10.1137/1026034

S. [. Simmons and . Koenig, Probabilistic robot navigation in partially observable environment, Proceedings of IJCAI-95, p.27, 1995.

]. A. Smi78 and . Smith, Color gamut transformation pairs, Computer Graphics, vol.3, issue.12, pp.12-19, 1978.

]. E. Son78, . W. Sondikss73-]-r, E. J. Smallwood, and . Sondik, The optimal control of partially observable markov processes over the infinite horizont : Discounted costs The optimal control of partially observable markov processes over a finite horizon, Operations Research Operations Research, vol.26, issue.21, pp.282-304, 1973.

T. Thrun, M. Beetz, M. Bennewitz, W. Burgard, A. B. Creemers et al., Probabilistic algorithms and the interactive museum tour-guide robot minerva Monte carlo localization with mixture proposal distribution Learning metric-topological maps for indoor mobile robot navigation, Proceedings of the AAAI National Conference on Artificial IntelligenceThr98a] S. Thrun. A framework for programming embedded systems : Initial design and results, pp.29-184, 1998.

]. S. Thr00 and . Thrun, Probabilistic algorithms in robotics, p.31, 2000.