This study proposes an optimization strategy for a tail-sitter unmanned aerial vehicle (UAV) landing on an autonomous surface vessel (ASV), aiming for minimal risk, shortest time, and lowest energy consumption. Sliding mode control ensures system stability under nonlinear and dynamic conditions. A two-player game model between the UAV and ASV is established, achieving Nash equilibrium to minimize risk and energy. Pareto optimal theory provides a trade-off among multiple objectives, while Karush-Kuhn-Tucker conditions verify the optimality of the solution. An Actor-Critic network generates and evaluates landing strategies through adaptive online learning based on temporal difference errors. Results show that the UAV can dynamically adjust to ship motion, achieving precise, energy-efficient, and low-risk landings. This approach offers practical technical support and theoretical foundations for cooperative operations between UAVs and unmanned ships in diverse environments.
Lin et al. (Fri,) studied this question.