Decision-Theoretic Control of Planetary Rovers

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Decision-Theoretic Control of Planetary Rovers
Planetary rovers are small unmanned vehicles equipped with cameras and a variety of sensors used for scientific experiments. They must operate under tight constraints over such resources as operation time, power, storage capacity, and communication bandwidth. Moreover, the limited computational resources of the rover limit the complexity of on-line planning and scheduling. We describe two decision-theoretic approaches to maximize the productivity of planetary rovers: one based on adaptive planning and the other on hierarchical reinforcement learning. Both approaches map the problem into a Markov decision problem and attempt to solve a large part of the problem off-line, exploiting the structure of the plan and independence between plan components. We examine the advantages and limitations of these techniques and their scalability. 1 Towards Autonomous Planetary Rovers The power of a mobile platform to perform science and explore the surface of distant planetary surfaces has long attrac...
Shlomo Zilberstein, Richard Washington, Daniel S.
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Authors Shlomo Zilberstein, Richard Washington, Daniel S. Bernstein, Abdel-Illah Mouaddib
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