  Suitably equipped with cameras and sensors, uncrewed aerial vehicles (UAVs)
can be instrumental for wildfire prediction, tracking, and monitoring, provided
that uninterrupted connectivity can be guaranteed even if some of the ground
access points (APs) are damaged by the fire itself. A cell-free network
structure, with UAVs connecting to a multiplicity of APs, is therefore ideal in
terms of resilience. This work proposes a trajectory optimization framework for
a UAV swarm tracking a wildfire while maintaining cell-free connectivity with
ground APs. Such optimization entails a constant repositioning of the
multiplicity of UAVs as the fire evolves to ensure that the best possible view
is acquired and transmitted reliably, while respecting altitude limits,
avoiding collisions, and proceeding to recharge batteries as needed. Given the
complexity and time-varying nature of this multi-UAV trajectory optimization,
reinforcement learning is leveraged, specifically the twin-delayed deep
deterministic policy gradient algorithm. The approach is shown to be highly
effective for wildfire tracking and coverage and could be likewise applicable
to survey other natural and man-made phenomena, including weather events,
earthquakes, or chemical spills.
