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Adaptive Sensing of Time Series with Application to Remote ExplorationWe address the problem of adaptive informationoptimal data collection in time series. Here a remote sensor or explorer agent throttles its sampling rate in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility -- all collected datapoints lie in the past, but its resource allocation decisions require predicting far into the future. Our solution is to continually fit a Gaussian process model to the latest data and optimize the sampling plan on line to maximize information gain. We compare the performance characteristics of stationary and nonstationary Gaussian process models. We also describe an application based on geologic analysis during planetary rover exploration. Here adaptive sampling can improve coverage of localized anomalies and potentially benefit mission science yield of long autonomous traverses.
Document ID
Document Type
Conference Paper
External Source(s)
Thompson, David R.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Cabrol, Nathalie A.
(NASA Ames Research Center Moffett Field, CA, United States)
Furlong, Michael
(Carnegie-Mellon Univ. Pittsburgh, PA, United States)
Hardgrove, Craig
(Malin Space Science Systems San Diego, CA, United States)
Low, Bryan K. H.
(National Univ. of Singapore Singapore)
Moersch, Jeffrey
(Tennessee Univ. Knoxville, TN, United States)
Wettergreen, David
(Carnegie-Mellon Univ. Pittsburgh, PA, United States)
Date Acquired
May 1, 2015
Publication Date
May 6, 2013
Subject Category
Lunar And Planetary Science And Exploration
Instrumentation And Photography
Statistics And Probability
Meeting Information
Meeting: IEEE International Conference on Robotics and Automation
Location: Karlsruhe
Country: Germany
Start Date: May 6, 2013
End Date: May 10, 2013
Sponsors: Institute of Electrical and Electronics Engineers
Funding Number(s)
Distribution Limits
artificial intelligence
adaptive sensing
robotic exploration
gaussian process
time series
autonomous science

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