Adaptive Sampling for Multi-Robot Wide-Area Exploration

Kian Hsiang Low, Geoffrey Gordon, John M. Dolan, and Pradeep Khosla
Proceedings of the IEEE 2007 International Conference on Robotics and Automation (ICRA '07), April, 2007.


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Abstract
The exploration problem is a central issue in mobile robotics. A complete coverage is not practical if the environment is large with a few small hotspots, and the sampling cost is high. So, it is desirable to build robot teams that can coordinate to maximize sampling at these hotspots while minimizing resource costs, and consequently learn more accurately about properties of such environmental phenomena. An important issue in designing such teams is the exploration strategy. The contribution of this paper is in the evaluation of an adaptive exploration strategy called Adaptive Cluster Sampling (ACS), which is demonstrated to reduce the resource costs (i.e., mission time and energy consumption) of a robot team, and yield more information about the environment by directing robot exploration towards hotspots. Due to the adaptive nature of the strategy, it is not obvious how the sampled data can be used to provide unbiased, low-variance estimates of the properties. This paper therefore discusses how estimators that are Rao-Blackwellized can be used to achieve low error. This paper also presents the first analysis of the characteristics of the environmental phenomena that favor the ACS strategy and estimators. Quantitative experimental results in a mineral prospecting task simulation show that our approach is more efficient in exploration by yielding more minerals and information with fewer resources and providing more precise mineral density estimates than previous methods.

Notes
Associated Lab(s) / Group(s): Tele-Supervised Autonomous Robotics
Associated Project(s): Telesupervised Adaptive Ocean Sensor Fleet
Number of pages: 6

Text Reference
Kian Hsiang Low, Geoffrey Gordon, John M. Dolan, and Pradeep Khosla, "Adaptive Sampling for Multi-Robot Wide-Area Exploration," Proceedings of the IEEE 2007 International Conference on Robotics and Automation (ICRA '07), April, 2007.

BibTeX Reference
@inproceedings{Low_2007_5679,
   author = "Kian Hsiang Low and Geoffrey Gordon and John M Dolan and Pradeep Khosla",
   title = "Adaptive Sampling for Multi-Robot Wide-Area Exploration",
   booktitle = "Proceedings of the IEEE 2007 International Conference on Robotics and Automation (ICRA '07)",
   month = "April",
   year = "2007",
}