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Probabilistic Algorithms in Robotics (2000)

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by Sebastian Thrun
Venue:AI Magazine
Citations:147 - 7 self
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BibTeX

@ARTICLE{Thrun00probabilisticalgorithms,
    author = {Sebastian Thrun},
    title = {Probabilistic Algorithms in Robotics},
    journal = {AI Magazine},
    year = {2000},
    volume = {21},
    pages = {93--109}
}

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Abstract

This article describes a methodology for programming robots known as probabilistic robotics. The probabilistic paradigm pays tribute to the inherent uncertainty in robot perception, relying on explicit representations of uncertainty when determining what to do. This article surveys some of the progress in the field, using in-depth examples to illustrate some of the nuts and bolts of the basic approach. Our central conjecture is that the probabilistic approach to robotics scales better to complex real-world applications than approaches that ignore a robot's uncertainty.

Citations

6232 Maximum likelihood from incomplete data via the em algorithm - Dempster, Laird, et al. - 1977
5665 Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference - Pearl - 1988
2959 Artificial Intelligence: a Modern Approach - Russell, Norvig - 1995
2827 Reinforcement Learning: An Introduction - Sutton, Barto - 1998
1964 Dynamic Programming - Bellman - 1957
1789 Robot Motion Planning - Latombe - 1991
1446 A new approach to linear filtering and prediction problems - Kalman - 1960
1134 Reinforcement learning: a survey - Kaelbling, Littman, et al. - 1996
911 Condensation - conditional density propagation for visual tracking - Isard, Blake - 1998
726 The EM Algorithm and Extensions - McLachlan, Krishnan - 1997
710 A tutorial on learning with Bayesian networks - Heckerman - 1995
666 An introduction to hidden Markov models - Rabiner, Juang - 1986
629 Planning and acting in partially observable stochastic domains - Kaelbling, Littman, et al. - 1998
490 Robust monte carlo localization for mobile robots - Thrun, Fox, et al. - 2000
488 A: Visual tracking by stochastic propagation of conditional density - Isard, Blake - 1996
473 Intractability and Time-Dependent Planning - Dean - 1987
443 The Complexity of Robot Motion Planning - Canny - 1987
434 Dynamic programming and Markov processes - Howard - 1960
396 A model for reasoning about persistence and causation - Dean, Kanazawa - 1990
378 A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations - Kuipers, Byun - 1991
360 Filtering via simulation: Auxiliary particle filter - Pitt, Shephard - 1999
359 A probabilistic approach to concurrent mapping and localization for mobile robots - Thrun, Burgard, et al. - 1998
347 Globally consistent range scan alignment for environment mapping - Lu, Milios - 1997
339 Sequential Monte Carlo methods for dynamic systems - Liu, Chen - 1998
315 Fusion in Certainty Grids for Mobile Robots - Moravec - 1988
307 Estimating Uncertain Spatial Relationships in Robotics - Smith, Self, et al. - 1987
296 Elephants don’t play chess - Brooks - 1990
285 The Optimal Control of Partially Observable Markov Processes - Sondik - 1971
267 Sonar-based real-world mapping and navigation - Elfes - 1990
255 Probabilistic Horn abduction and Bayesian networks - Poole - 1993
246 Incremental mapping of large cyclic environments - Gutmann, Konolige
242 Markov Localization for Mobile Robots in Dynamic Environments - Fox, Burgard, et al.
241 Monte carlo localization: efficient position estimation for mobile robots - Fox, Burgard, et al. - 1999
239 An analysis of first-order logics of probability - Halpern - 1990
235 The optimal control of partially observable Markov processes over a finite horizon - Smallwood, Sondik - 1973
231 Probabilistic robot navigation in partially observable environments - Simmons, Koenig - 1995
222 Learning metric-topological maps for indoor mobile robot navigation - Thrun - 1998
217 Experiences with an interactive museum tour-guide robot - Burgard, Cremers, et al. - 1999
215 A real-time algorithm for mobile robot mapping with applications to multi-robot and 3d mapping - Thrun, Burgard, et al. - 2000
214 A Robot That Walks; Emergent Behaviors from a Carefully Evolved Network. Memo 1091 - Brooks - 1989
212 Directed Sonar Sensing for Mobile Robot Navigation - Leonard, Durrant-Whyte - 1992
202 Learning policies for partially observable environments: Scaling up - Littman, Cassandra, et al.
184 Collaborative multi-robot exploration - Burgard, Moors, et al.
169 On sequential simulation-based methods for Bayesian filtering - Doucet - 1998
165 Survey of partially observable markov decision processes: Theory, models, and algorithms - Monahan - 1982
164 Dynamic map building for an autonomous mobile robot - Leonard, Durrant-Whyte, et al. - 1992
160 Estimating the absolute position of a mobile robot using position probability grids - Burgard, Fox, et al. - 1996
157 Blanche: An Experiment in Guidance and Navigation of an Autonomous Robot Vehicle - Cox - 1991
131 Using the SIR algorithm to simulate posterior distributions - Rubin - 1988
125 A computationally efficient method for large-scale concurrent mapping and localization - Leonard, Feder - 2000
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