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Unmanned aerial vehicles (UAVs), especially fixed-wing ones that withstandstrong winds, have great potential for oceanic exploration and research. Thispaper studies a UAV-aided maritime data collection system with a fixed-wing UAVdispatched to collect data from marine buoys. We aim to minimize the UAV'senergy consumption in completing the task by jointly optimizing thecommunication time scheduling among the buoys and the UAV's flight trajectorysubject to wind effect. The conventional successive convex approximation (SCA)method can provide efficient sub-optimal solutions for collectingsmall/moderate data volume, whereas the solution heavily relies on trajectoryinitialization and has not explicitly considered wind effect, while thecomputational/trajectory complexity both become prohibitive for the task withlarge data volume. To this end, we propose a new cyclical trajectory designframework with tailored initialization algorithm that can handle arbitrary datavolume efficiently, as well as a hybrid offline-online (HO2) design thatleverages 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 tovarious setups with different target data volume and buoys' topology as well asvarious wind speed/direction/variance compared with benchmark schemes.

Energy-Efficient Trajectory Design for UAV-Aided Maritime Data Collection in Wind