  We study the problem of distributed multi-robot coverage over an unknown,
nonuniform sensory field. Modeling the sensory field as a realization of a
Gaussian Process and using Bayesian techniques, we devise a policy which aims
to balance the tradeoff between learning the sensory function and covering the
environment. We propose an adaptive coverage algorithm called Deterministic
Sequencing of Learning and Coverage (DSLC) that schedules learning and coverage
epochs such that its emphasis gradually shifts from exploration to exploitation
while never fully ceasing to learn. Using a novel definition of coverage regret
which characterizes overall coverage performance of a multi-robot team over a
time horizon $T$, we analyze DSLC to provide an upper bound on expected
cumulative coverage regret. Finally, we illustrate the empirical performance of
the algorithm through simulations of the coverage task over an unknown
distribution of wildfires.
