Multiscale sensing with stochastic modeling

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.