The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Recent research in robot exploration and mapping has focused on sampling hotspot fields. This exploration task is formalized by [3] in a decision-theoretic planning framework called MAXP. The time complexity of solving MAXP approximately depends on the map resolution, which limits its use in large-scale, high-resolution exploration and mapping. To alleviate this computational difficulty, this paper presents an information-theoretic approach to MAXP (iMAXP); by reformulating the cost-minimizing iMAXP as a reward-maximizing problem, its time complexity becomes independent of map resolution and is less sensitive to increasing robot team size. Using the reward-maximizing dual, we derive a novel adaptive variant of maximum entropy sampling, thus improving the induced policy performance. We also demonstrate the superior performance of exploration policies for sampling the log-Gaussian process to that of policies for the Gaussian process in mapping the hotspot field. Lastly, we provide sufficient conditions that, when met, guarantee adaptivity has no benefit under an assumed environment model.

Keywords

convex programming, stochastic processes, dynamic programming, autonomous vehicles

Notes

Associated Lab(s) / Group(s): Tele-Supervised Autonomous Robotics

Associated Project(s): Robot Sensor Boat

Number of pages: 8

Text Reference

Kian Hsiang Low, John M. Dolan, and Pradeep Khosla, "Information-Theoretic Multi-Robot Adaptive Exploration and Mapping of Environmental Hotspot Fields," 2009 International Conference on Information Processing in Sensor Networks (IPSN) Workshop on Sensor Networks for Earth and Space Science Applications: ESSA 2009, April, 2009.

BibTeX Reference

@inproceedings{Low_2009_6741,
   author = "Kian Hsiang Low and John M Dolan and Pradeep Khosla",
   title = "Information-Theoretic Multi-Robot Adaptive Exploration and Mapping of Environmental Hotspot Fields",
   booktitle = "2009 International Conference on Information Processing in Sensor Networks (IPSN) Workshop on Sensor Networks for Earth and Space Science Applications: ESSA 2009",
   month = "April",
   year = "2009",
}

The Robotics Institute

Menu