  Fighting wildfires is a precarious task, imperiling the lives of engaging
firefighters and those who reside in the fire's path. Firefighters need online
and dynamic observation of the firefront to anticipate a wildfire's unknown
characteristics, such as size, scale, and propagation velocity, and to plan
accordingly. In this paper, we propose a distributed control framework to
coordinate a team of unmanned aerial vehicles (UAVs) for a human-centered
active sensing of wildfires. We develop a dual-criterion objective function
based on Kalman uncertainty residual propagation and weighted multi-agent
consensus protocol, which enables the UAVs to actively infer the wildfire
dynamics and parameters, track and monitor the fire transition, and safely
manage human firefighters on the ground using acquired information. We evaluate
our approach relative to prior work, showing significant improvements by
reducing the environment's cumulative uncertainty residual by more than $ 10^2
$ and $ 10^5 $ times in firefront coverage performance to support human-robot
teaming for firefighting. We also demonstrate our method on physical robots in
a mock firefighting exercise.
