Environmental Hotspot Identification in Limited Time with a UAV Equipped with a Downward-Facing Camera
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We are motivated by environmental monitoring tasks where finding the global maxima (i.e., hotspot) of a spatially varying field is crucial. We investigate the problem of identifying the hotspot for fields that can be sensed using an Unmanned Aerial Vehicle (UAV) equipped with a downward-facing camera. The UAV has a limited time budget which it must use for learning the unknown field and identifying the hotspot. Our first contribution is to show how this problem can be formulated as a novel variant of the Gaussian Process (GP) multi-armed bandit problem. The novelty is two-fold: (i) unlike standard multi-armed bandit settings, the arms ; and (ii) unlike standard GP regression, the measurements in our problem are image (i.e., vector measurements) whose quality depends on the altitude at which the UAV flies. We present a strategy for finding the sequence of UAV sensing locations and empirically compare it with a number of baselines. We also present experimental results using images gathered onboard a UAV.
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