Learning geometrically-constrained Hidden Markov models for robot navigation: Bridging the topological-geometrical gap

Journal of Artificial Intelligence Research

Volumn 16, Issue , 2002, Pages 167-207

  Shatkay, Hagit ^a   Kaelbling, Leslie Pack ^b  

^a CELERA GENOMICS   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION MAKING; ITERATIVE METHODS; MARKOV PROCESSES; MATHEMATICAL MODELS; PLANNING; ROBUSTNESS (CONTROL SYSTEMS); TOPOLOGY;

MODELING DYNAMICAL SYSTEMS; ROAD NETWORKS;

LEARNING SYSTEMS;

EID: 2442467094     PISSN: 10769757     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.874     Document Type: Article

References (49)

1. Abe, N., & Warmuth, M. K. (1992). On the computational complexity of approximating distributions by probabilistic automata. Machine Learning, 9(2), 205-260.
2. Angluin, D. (1987). Learning regular sets from queries and counterexamples. Information and Computation, 75, 87-106.
3. Asada, M. (1991). Map building for a mobile robot from sensory data. In Iyengar, S. S., & Elfes, A. (Eds.), Autonomous Mobile Robots, pp. 312-322. IEEE Computer Society Press.
4. Bartels, R. (1984). Estimation in a bidirectional mixture of von Mises distributions. Biometrics, 40, 777-784.
5. Basye, K., Dean, T., & Kaelbling, L. P. (1995). Learning dynamics: System identification for perceptually challenged agents. Artificial Intelligence, 72(1).
6. Baum, L. E., Petrie, T., Soules, G., & Weiss, N. (1970). A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The Annals of Mathematical Statistics, 41(1), 164-171.
7. Cassandra, A. R., Kaelbling, L. P., & Kurien, J. A. (1996). Acting under uncertainty: Discrete Bayesian models for mobile-robot navigation. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems.
8. DeGroot, M. H. (1986). Probability and Statistics (2nd edition). Addison-Wesley.
9. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(1), 1-38.
10. Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H. F., & Csorba, M. (2001). A solution to the simultaneous localization and map building (SLAM) problem. IEEE Transactions on Robotics and Automation, 17(3).
11. Duda, R. O., & Hart, P. E. (1973). Unsupervised Learning and Clustering, chap. 6. John Wiley and Sons.
12. Elfes, A. (1989). Using occupancy grids for mobile robot perception and navigation. Computer, Special Issue on Autonomous Intelligent Machines, 22(6), 46-57.
13. Engelson, S. P., &: McDermott, D. V. (1992). Error correction in mobile robot map learning. In Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2555-2560, Nice, France.
14. Gold, E. M. (1978). Complexity of automaton identification from given data. Information and Control, 37, 302-320.
15. Gumbel, E. G., Greenwood, J. A., &: Durand, D. (1953). The circular normal distribution: Theory and tables. American Statistical Society Journal, 48, 131-152.
16. Hopcroft, J. E., & Ullman, J. D. (1979). Introduction to Automata Theory, Languages, and Computation. Addison & Wesley.
17. Juang, B. H. (1985). Maximum likelihood estimation for mixture multivariate stochastic observations of Markov chains. AT&T Technical Journal, 64(6).
18. Juang, B. H., & Rabiner, L. R. (1985). A probabilistic distance measure for hidden Markov models. AT&T Technical Journal, 64(2), 391-408.
19. Koenig, S., & Simmons, R. G. (1996a). Passive distance learning for robot navigation. In Proceedings of the Thirteenth International Conference on Machine Learning, pp. 266-274.
20. Koenig, S., & Simmons, R. G. (1996b). Unsupervised learning of probabilistic models for robot navigation. In Proceedings of the IEEE International Conference on Robotics and Automation.
21. Kuipers, B., & Byun, Y.-T. (1991). A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. Journal of Robotics and Autonomous Systems, 8, 47-63.
22. Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.
23. Leonard, J., Durrant-Whyte, H. F., & Cox, I. J. (1991). Dynamic map building for an autonomous mobile robot. In Iyengar, S. S., & Elfes, A. (Eds.), Autonomous Mobile Robots, pp. 331-338. IEEE Computer Society Press.
24. Leonard, J. J., & Feder, H. J. S. (2000). A computationally efficient method for large-scale concurrent mapping and localization. In Hollerbach, J., & Kodischek, D. (Eds.), Proceedings of the Ninth International Symposium on Robotics Research.
25. Liporace, L. A. (1982). Maximum likelihood estimation for multivariate observations of Markov sources. IEEE Transactions on Information Theory, 28(5).
26. Mardia, K. V. (1972). Statistics of Directional Data. Academic Press.
27. Mataric, M. J. (1990). A distributed model for mobile robot environment-learning and navigation. Master's thesis, MIT, Artificial Intelligence Laboratory.
28. McLachlan, G. J., & Krishnan, T. (1997). The EM Algorithm and Extensions. John Wiley & Sons.
29. Moravec, H. P. (1988). Sensor fusion in certainty grids for mobile robots. AI Magazine, 9(2), 61-74.
30. Moravec, H. P., & Elfes, A. (1985). High resolution maps from wide angle sonar. In Proceedings of the International Conference on Robotics and Automation, pp. 116-121.
31. Nourbakhsh, I., Powers, R., & Birchfield, S. (1995). Dervish: An office-navigating robot. AI Magazine, 16(1), 53-60.
32. Pierce, D., & Kuipers, B. (1997). Map learning with uninterpreted sensors and effectors. Artificial Intelligence, 92(1-2), 169-227.
33. Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-285.
34. Rivest, R. L., & Schapire, R. E. (1987). Diversity based inference of finite automata. In Proceedings of the IEEE Twenty Eighth Annual Symposium on Foundations of Computer Science, pp. 78-87, Los Angeles, California.
35. Rivest, R. L., & Schapire, R. E. (1989). Inference of finite automata using homing sequences. In Proceedings of the Twenty First Annual Symposium on Theory of Computing, pp. 411-420, Seattle, Washington.
36. Ron, D., Singer, Y., & Tishbi, N. (1994). Learning probabilistic automata with variable memory length. In Proceedings of the Seventh Annual Workshop on Computational Learning Theory, pp. 35-46.
37. Ron, D., Singer, Y., & Tishbi, N. (1995). On the learnability and usage of acyclic probabilistic finite automata. In Proceedings of the Eighth Annual Workshop on Computational Learning Theory, pp. 31-40.
38. Ron, D., Singer, Y., & Tishby, N. (1998). On the learnability and usage of acyclic probabilistic finite automata. Journal of Computer and Systems Science, 56(2).
39. Shatkay, H. (1999). Learning Models for Robot Navigation. Ph.D. thesis, Department of Computer Science, Brown University, Providence, RI.
40. Shatkay, H., & Kaelbling, L. P. (1997). Learning topological maps with weak local odometric information. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, Nagoya, Japan.
41. Shatkay, H., & Kaelbling, L. P. (1998). Heading in the right direction. In Proceedings of the Fifteenth International Conference on Machine Learning, Madison, Wisconsin.
42. Simmons, R. G., & Koenig, S. (1995). Probabilistic navigation in partially observable environments. In Proceedings of the International Joint Conference on Artificial Intelligence.
43. Smith, R., Self, M., &: Cheeseman, P. (1991). A stochastic map for uncertain spatial relationships. In Iyengar, S. S., & Elfes, A. (Eds.), Autonomous Mobile Robots, pp. 323-330. IEEE Computer Society Press.
44. Thrun, S. (1999). Learning metric-topological maps for indoor mobile robot navigation. AI Journal, 1, 21-71.
45. Thrun, S., & Bücken, A. (1996a). Integrating grid-based and topological maps for mobile robot navigation. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 944-950.
46. Thrun, S., & Bücken, A. (1996b). Learning maps for indoor mobile robot navigation. Tech. rep. CMU-CS-96-121, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA.
47. Thrun, S., Burgard, W., & Fox, D. (1998a). A probabilistic approach to concurrent map acquisition and localization for mobile robots. Machine Learning, 31, 29-53.
48. Thrun, S., Gutmann, J.-S., Fox, D., Burgard, W., & Kuipers, B. J. (1998b). Integrating topological and metric maps for mobile robot navigation: A statistical approach. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pp. 989-995.
49. Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer.