Multiscale sensing with stochastic modeling

Authors:
Diane Budzik;Amarjeet Singh;Maxim A. Batalin;William J. Kaiser
Affiliations:
Center for Embedded Networked Sensing, University of California, Los Angeles;Center for Embedded Networked Sensing, University of California, Los Angeles;Center for Embedded Networked Sensing, University of California, Los Angeles;Center for Embedded Networked Sensing, University of California, Los Angeles
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

Citing 4
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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.