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A Bayesian framework for optimal motion planning with uncertainty

Censi, Andrea and Calisi, D. and De Luca, A. and Oriolo, G. (2008) A Bayesian framework for optimal motion planning with uncertainty. In: Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on. IEEE , New York, NY, pp. 1798-1805. ISBN 978-1-4244-1646-2.

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Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a separate implementation of control, localization and planning. In the end, we reduce the stochastic control problem to path- planning in the extended space of poses x covariances; the transitions between states are modeled through the use of the Fisher information matrix. In this framework, we consider two problems: minimizing the execution time, and minimizing the final covariance, with an upper bound on the execution time. Two correct and complete algorithms are presented. The first is the direct extension of classical graph-search algorithms in the extended space. The second one is a back-projection algorithm: uncertainty constraints are propagated backward from the goal towards the start state.

Item Type:Book Section
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Censi, Andrea0000-0001-5162-0398
Additional Information:© 2008 IEEE. Issue Date: 19-23 May 2008; Date of Current Version: 13 June 2008.
Subject Keywords:Bayes methods; graph theory; matrix algebra; minimisation; mobile robots; optimal control; path planning; search problems; stochastic processes
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INSPEC Accession Number10014278
Record Number:CaltechAUTHORS:20100512-152619370
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Official Citation:Censi, A.; Calisi, D.; De Luca, A.; Oriolo, G.; , "A Bayesian framework for optimal motion planning with uncertainty," Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on , vol., no., pp.1798-1805, 19-23 May 2008 doi: 10.1109/ROBOT.2008.4543469 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:18273
Deposited On:24 Jun 2010 20:45
Last Modified:08 Nov 2021 23:42

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