Reinforcement Learning for Landing the Variable Skew Quad Plane on a Moving Platform

Abstract

UAVs have become increasingly popular, finding applications in diverse areas such as military operations, search and rescue, delivery services, wireless communication, and aerial surveillance. A critical aspect of UAV operations is autonomous landing, especially on moving targets like ships, which presents significant challenges due to the dynamic and unpredictable maritime environment. This research explores the potential of reinforcement learning as a strategy for achieving optimal guidance during the autonomous landing process of the VSQP. The study illustrates that the reinforcement learning learning framework can effectively steer the VSQP, ensuring safe and accurate landings on a moving ship, and outperforming the benchmark controller in a more intelligent manner. The proposed approach not only improves landing performance but also extends the operational capability of the VSQP.