  This paper presents two algorithms for multi-agent dynamic coverage in
spatiotemporal environments, where the coverage algorithms are informed by the
method of data assimilation. In particular, we show that by considering the
information assimilation algorithm, here a Numerical Gaussian Process Kalman
Filter, the influence of measurements taken at one position on the uncertainty
of the estimate at another location can be computed. We use this relationship
to propose new coverage algorithms. Furthermore, we show that the controllers
naturally extend to the multi-agent context, allowing for a distributed-control
central-information paradigm for multi-agent coverage. Finally, we demonstrate
the algorithms through a realistic simulation of a team of UAVs collecting wind
data over a region in Austria.
