  Motivated by agility, 3D mobility, and low-risk operation compared to
human-operated management systems of autonomous unmanned aerial vehicles
(UAVs), this work studies UAV-based active wildfire monitoring where a UAV
detects fire incidents in remote areas and tracks the fire frontline. A UAV
path planning solution is proposed considering realistic wildfire management
missions, where a single low-altitude drone with limited power and flight time
is available. Noting the limited field of view of commercial low-altitude UAVs,
the problem formulates as a partially observable Markov decision process
(POMDP), in which wildfire progression outside the field of view causes
inaccurate state representation that prevents the UAV from finding the optimal
path to track the fire front in limited time. Common deep reinforcement
learning (DRL)-based trajectory planning solutions require diverse
drone-recorded wildfire data to generalize pre-trained models to real-time
systems, which is not currently available at a diverse and standard scale. To
narrow down the gap caused by partial observability in the space of possible
policies, a belief-based state representation with broad, extensive simulated
data is proposed where the beliefs (i.e., ignition probabilities of different
grid areas) are updated using a Bayesian framework for the cells within the
field of view. The performance of the proposed solution in terms of the ratio
of detected fire cells and monitored ignited area (MIA) is evaluated in a
complex fire scenario with multiple rapidly growing fire batches, indicating
that the belief state representation outperforms the observation state
representation both in fire coverage and the distance to fire frontline.
