  Unmanned aerial vehicles (UAVs), especially fixed-wing ones that withstand
strong winds, have great potential for oceanic exploration and research. This
paper studies a UAV-aided maritime data collection system with a fixed-wing UAV
dispatched to collect data from marine buoys. We aim to minimize the UAV's
energy consumption in completing the task by jointly optimizing the
communication time scheduling among the buoys and the UAV's flight trajectory
subject to wind effect, which is a non-convex problem and difficult to solve
optimally. Existing techniques such as the successive convex approximation
(SCA) method provide efficient sub-optimal solutions for collecting
small/moderate data volume, whereas the solution heavily relies on the
trajectory initialization and has not explicitly considered the wind effect,
while the computational complexity and resulted trajectory complexity both
become prohibitive for the task with large data volume. To this end, we propose
a new cyclical trajectory design framework that can handle arbitrary data
volume efficiently subject to wind effect. Specifically, the proposed UAV
trajectory comprises multiple cyclical laps, each responsible for collecting
only a subset of data and thereby significantly reducing the
computational/trajectory complexity, which allows searching for better
trajectory initialization that fits the buoys' topology and the wind. Numerical
results show that the proposed cyclical scheme outperforms the benchmark
one-flight-only scheme in general. Moreover, the optimized cyclical 8-shape
trajectory can proactively exploit the wind and achieve lower energy
consumption compared with the case without wind.
