The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Acting under uncertainty: discrete Bayesian models for mobile-robot navigation
Abstract
Discrete Bayesian models have been used to model uncertainty for mobile-robot navigation, but the question of how actions should be chosen remains largely unexplored. This paper presents the optimal solution to the problem, formulated as a partially observable Markov decision process. Since solving for the optimal control policy is intractable, in general, it goes on to explore a variety of heuristic control strategies. The control strategies are compared experimentally, both in simulation and in runs on a robot.
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Citations
Cites
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Moravec, H., & Elfes, A. (2005) · Proceedings. 1985 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.1985.1087316 · Cited by 1,772
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The Optimal Control of Partially Observable Markov Processes over a Finite Horizon
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Learning policies for partially observable environments: Scaling up
Littman, M. L., Cassandra, A. R., & Kaelbling, L. P. (1995) · Machine Learning Proceedings 1995 · doi:10.1016/b978-1-55860-377-6.50052-9 · Cited by 650
Self-citationActing Optimally in Partially Observable Stochastic Domains
Cassandra, A. R., Kaelbling, L. P., & Littman, M. L. (1994) · Cited by 558
Self-citationA survey of algorithmic methods for partially observed Markov decision processes
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Research works printed but not linked: 2
- 10.1007/978-1-4471-3087-1_26 · doi:10.1007/978-1-4471-3087-1_26
- littman, 1995, An Efficient Algorithm for Dynamic Programming in Partially Observable Markov Decision Processes
Cited by
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.10045 · Cited by 11,321
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
“Bayesian models have been shown to lead to great insights in assorted applications such as cognitive modeling [201,202] , fishery [196,203] , gaming [204] , climate [205] , economet-rics [206] or robotics [207] .”
Robust Monte Carlo localization for mobile robots
Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00069-8 · Cited by 1,809
Information Gain-based Exploration Using Rao-Blackwellized Particle Filters
Stachniss, C., Grisetti, G., & Burgard, W. (2005) · Robotics: Science and Systems I · doi:10.15607/rss.2005.i.009 · Cited by 542
Finding approximate POMDP solutions through belief compression
Roy, N., Gordon, G., & Thrun, S. (2005) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1496 · Cited by 254
Metadata
- Authors
- Cassandra, A. R., Kaelbling, L. P., & Kurien, J. A.
- Venue
- Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96
- Publisher
- IEEE
- Published
- 2002-12-24
- Volume
- 2
- Pages
- 963-972
- OpenAlex
- W2115356364
- Cited by
- 462
- References
- 17
- FWCI
- 75.32
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Cassandra, A. R., Kaelbling, L. P., & Kurien, J. A. (2002). Acting under uncertainty: discrete Bayesian models for mobile-robot navigation. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96. https://doi.org/10.1109/iros.1996.571080
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