Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing

Kian Hsiang Low, John M. Dolan, and Pradeep Khosla
Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS-09), September, 2009.


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Abstract
Recent research in robot exploration and mapping has focused on sampling environmental hotspot fields. This exploration task is formalized by Low, Dolan, and Khosla (2008) in a sequential decision-theoretic planning under uncertainty framework called MASP. The time complexity of solving MASP 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 MASP (iMASP) for efficient adaptive path planning; by reformulating the cost-minimizing iMASP as a reward-maximizing problem, its time complexity becomes independent of map resolution and is less sensitive to increasing robot team size as demonstrated both theoretically and empirically. Using the reward-maximizing dual, we derive a novel adaptive variant of maximum entropy sampling, thus improving the induced exploration policy performance. It also allows us to establish theoretical bounds quantifying the performance advantage of optimal adaptive over non-adaptive policies and the performance quality of approximately optimal vs. optimal adaptive policies. We show analytically and empirically the superior performance of iMASP-based policies for sampling the log-Gaussian process to that of policies for the widely-used 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.

Notes
Associated Lab(s) / Group(s): Tele-Supervised Autonomous Robotics
Associated Project(s): Telesupervised Adaptive Ocean Sensor Fleet
Number of pages: 10
Note: (to appear)

Text Reference
Kian Hsiang Low, John M. Dolan, and Pradeep Khosla, "Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing," Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS-09), September, 2009.

BibTeX Reference
@inproceedings{Low_2009_6424,
   author = "Kian Hsiang Low and John M Dolan and Pradeep Khosla",
   title = "Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing",
   booktitle = "Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS-09)",
   month = "September",
   year = "2009",
   Notes = "(to appear)"
}