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• W45542973 abstract "We present a sample-based replanning strategy for driving partially-observable, high-dimensional robotic systems to a desired goal. At each time step, it uses forward simulation of randomly-sampled open-loop controls to construct a belief-space search tree rooted at its current belief state. Then, it executes the action at the root that leads to the best node in the tree. As a node quality metric we use Monte Carlo simulation to estimate the likelihood of success under the QMDP belief-space feedback policy, which encourages the robot to take information-gathering actions as needed to reach the goal. The technique is demonstrated on target-finding and localization examples in up to 5D state spacess." @default.
• W45542973 created "2016-06-24" @default.
• W45542973 creator A5027878263 @default.
• W45542973 date "2010-01-01" @default.
• W45542973 modified "2023-09-23" @default.
• W45542973 title "Randomized Belief-Space Replanning in Partially-Observable Continuous Spaces" @default.
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• W45542973 doi "https://doi.org/10.1007/978-3-642-17452-0_12" @default.
• W45542973 hasPublicationYear "2010" @default.
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