2011 | OriginalPaper | Buchkapitel
Monte Carlo Value Iteration for Continuous-State POMDPs
verfasst von : Haoyu Bai, David Hsu, Wee Sun Lee, Vien A. Ngo
Erschienen in: Algorithmic Foundations of Robotics IX
Verlag: Springer Berlin Heidelberg
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Partially observable Markov decision processes (POMDPs) have been successfully applied to various robot motion planning tasks under uncertainty. However, most existing POMDP algorithms assume a discrete state space, while the natural state space of a robot is often continuous. This paper presents
Monte
Carlo
Value
Iteration
(MCVI) for continuous-state POMDPs. MCVI samples both a robot’s state space and the corresponding belief space, and avoids inefficient a priori discretization of the state space as a grid. Both theoretical results and preliminary experimental results indicate that MCVI is a promising new approach for robot motion planning under uncertainty.