Variable resolution decomposition for robotic navigation under a POMDP framework

Abstract

Partially Observable Markov Decision Processes (POMDPs) offer a powerful mathematical framework for making optimal action choices in noisy and/or uncertain environments, in particular, allowing us to merge localization and decision-making for mobile robots. While advancements in POMDP techniques have allowed the use of much larger models, POMDPs for robot… (More)
DOI: 10.1109/ROBOT.2010.5509188

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