Maximum-Reward Motion in a Stochastic Environment: The Nonequilibrium Statistical Mechanics Perspective
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How_Maximum-reward.pdf
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658.07 KB
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Author(s)
Ma, Fangchang
Karaman, Sertac
Date Issued
April 2015
Journal
Algorithmic Foundations of Robotics XI
Publisher
Springer International Publishing
Citation
Ma, Fangchang, and Sertac Karaman. “Maximum-Reward Motion in a Stochastic Environment: The Nonequilibrium Statistical Mechanics Perspective.” Algorithmic Foundations of Robotics XI. Ed. H. Levent Akin et al. Vol. 107. Cham: Springer International Publishing, 2015. 389–406.
Version
Author's final manuscript
Abstract
We consider the problem of computing the maximum-reward motion in a reward field in an online setting. We assume that the robot has a limited perception range, and it discovers the reward field on the fly. We analyze the performance of a simple, practical lattice-based algorithm with respect to the perception range. Our main result is that, with very little perception range, the robot can collect as much reward as if it could see the whole reward field, under certain assumptions. Along the way, we establish novel connections between this class of problems and certain fundamental problems of nonequilibrium statistical mechanics . We demonstrate our results in simulation examples.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-16595-0_23
