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.
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Hauser, K. (2010). Randomized Belief-Space Replanning in Partially-Observable Continuous Spaces. In: Hsu, D., Isler, V., Latombe, JC., Lin, M.C. (eds) Algorithmic Foundations of Robotics IX. Springer Tracts in Advanced Robotics, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17452-0_12
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DOI: https://doi.org/10.1007/978-3-642-17452-0_12
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