Decision-Theoretic Control of Planetary Rovers

Shlomo Zilberstein, Richard Washington, Daniel S. Bernstein, and Abdel-Illah Mouaddib. Decision-Theoretic Control of Planetary Rovers. In M. Beetz et al. (Eds.), Plan-Based Control of Robotic Agents, LNAI, No. 2466, 270-289, 2002.

Abstract

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.

Bibtex entry:

@inproceedings{ZWBMlnai02,
  author	= {Shlomo Zilberstein and Richard Washington and Daniel S. Bernstein
                   and Abdel-Illah Mouaddib},
  title		= {Decision-Theoretic Control of Planetary Rovers},
  booktitle     = {Revised Papers from the International Seminar on Advances in
                   Plan-Based Control of Robotic Agents},
  year		= {2002},
  pages		= {270-289},
  publisher     = {Springer-Verlag},
  address       = {London, UK},
  url		= {http://rbr.cs.umass.edu/shlomo/papers/ZWBMlnai02.html}
}

shlomo@cs.umass.edu