Multiscale sensing with stochastic modeling
IROS'09: Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Pages 4637 - 4643
Abstract
Many sensing applications require monitoring phenomena with complex spatio-temporal dynamics spread over large spatial domains. Efficient monitoring of such phenomena would require an impractically large number of static sensors; therefore, actuated sensing - mobile robots carrying sensors - is required. Path planning for these robots, i.e., deciding on a subset of locations to observe, is critical for high fidelity monitoring of expansive areas with complex dynamics. We propose MUST - a MUltiscale approach with STochastic modeling. MUST is a hierarchical approach that models the phenomena as a stochastic Gaussian Process that is exploited to select a near-optimal subset of observation locations. We discuss in detail our proposed algorithm for the application of monitoring light intensity in a forest understory. We performed extensive empirical evaluations both in simulation using field data and on an actual cabled robotic system to validate the effectiveness of our proposed algorithm.
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Index Terms
- Multiscale sensing with stochastic modeling
Index terms have been assigned to the content through auto-classification.
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Published In
October 2009
5973 pages
ISBN:9781424438037
Sponsors
- SICE: Society of Instrument and Control Engineers
- RA: IEEE Robotics and Automation Society
- ICROS: Institute of Control, Robotics and Systems in Korea
Publisher
IEEE Press
Publication History
Published: 10 October 2009
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