Adaptive sensing of time series with application to remote exploration

David R. Thompson, Nathalie A. Cabrol, Michael Furlong, Craig Hardgrove, Bryan Kian Hsiang Low, Jeffrey Moersch, David Wettergreen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

We address the problem of adaptive information-optimal 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.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Robotics and Automation, ICRA 2013
Pages3463-3468
Number of pages6
DOIs
StatePublished - Nov 14 2013
Event2013 IEEE International Conference on Robotics and Automation, ICRA 2013 - Karlsruhe, Germany
Duration: May 6 2013May 10 2013

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2013 IEEE International Conference on Robotics and Automation, ICRA 2013
Country/TerritoryGermany
CityKarlsruhe
Period5/6/135/10/13

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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