  In recent years, teams of robot and Unmanned Aerial Vehicles (UAVs) have been
commissioned by researchers to enable accurate, online wildfire coverage and
tracking. While the majority of prior work focuses on the coordination and
control of such multi-robot systems, to date, these UAV teams have not been
given the ability to reason about a fire's track (i.e., location and
propagation dynamics) to provide performance guarantee over a time horizon.
Motivated by the problem of aerial wildfire monitoring, we propose a predictive
framework which enables cooperation in multi-UAV teams towards collaborative
field coverage and fire tracking with probabilistic performance guarantee. Our
approach enables UAVs to infer the latent fire propagation dynamics for
time-extended coordination in safety-critical conditions. We derive a set of
novel, analytical temporal, and tracking-error bounds to enable the UAV-team to
distribute their limited resources and cover the entire fire area according to
the case-specific estimated states and provide a probabilistic performance
guarantee. Our results are not limited to the aerial wildfire monitoring
case-study and are generally applicable to problems, such as search-and-rescue,
target tracking and border patrol. We evaluate our approach in simulation and
provide demonstrations of the proposed framework on a physical multi-robot
testbed to account for real robot dynamics and restrictions. Our quantitative
evaluations validate the performance of our method accumulating 7.5x and 9.0x
smaller tracking-error than state-of-the-art model-based and reinforcement
learning benchmarks, respectively.
