  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. The conventional successive convex approximation (SCA)
method can provide efficient sub-optimal solutions for collecting
small/moderate data volume, whereas the solution heavily relies on trajectory
initialization and has not explicitly considered wind effect, while the
computational/trajectory complexity both become prohibitive for the task with
large data volume. To this end, we propose a new cyclical trajectory design
framework with tailored initialization algorithm that can handle arbitrary data
volume efficiently, as well as a hybrid offline-online (HO2) design that
leverages convex stochastic programming (CSP) offline based on wind statistics,
and refines the solution by adapting online to real-time wind velocity.
Numerical results show that our optimized trajectory can better adapt to
various setups with different target data volume and buoys' topology as well as
various wind speed/direction/variance compared with benchmark schemes.
