  This research is motivated by a scenario where a group of UAVs is assigned to
map an unknown scalar field, with the imperative of maintaining a safe distance
from the sources of the field to evade detection or damage. The location of the
sources is unknown a priori, so the UAVs rely on measurements of the field
intensity to gauge safety. The UAVs estimate the unknown scalar field using
Gaussian process (GP) regression and use the estimate to generate a map of
high-intensity regions using Hough transform (HT), updated online based on the
field measurements. A convergence analysis shows the boundedness of the error
between the actual scalar field and the learned scalar field. The effectiveness
of the method is evaluated through simulations, showcasing its ability to
accurately learn scalar fields with multiple high-intensity regions while
reducing the number of measurements taken inside the high-intensity regions.
