# Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel
## 1 Introduction  
Currently, large-scale and fast-speed marine vessels are widely used in numerous operations and missions. High-speed ships and large container ships can exceed 28 knots in speed, while existing patrol vessels for maritime surveillance reach lower speeds [1]. The operational conditions of patrol vessels give rise to disadvantages such as short visual range and slow response, rendering them unable to control the overall situation, and to continuously and effectively track illegal vessels for the efficient collection of evidence of illegal acts. The high-speed and high-efficiency of UAVs can effectively compensate for speed limitations of law enforcement vessels. A complete operational cycle of a UAV includes the three stages of launching, mission flight, and recycling. Both launching and autonomous flight technologies are considered relatively mature, whereas recycling is still a heated research topic in UAV technology [2]. Moreover, the autonomous landing of UAVs on moving vessels is a more complex scientific and engineering issue [3]. The landing environment of a ship-borne UAV is rather more complicated than that of a land-based UAV. Since the vessel is constantly moving during the landing of the UAV, a land-based UAV landing methodology should be applicable otherwise the UAV might fall directly into the sea. In particular, the sea cannot provide the necessary landmarks and the space required for landing. Even in aircraft carriers, the flight deck space is very limited, much smaller than the length of an onshore airport runway [4]. In the course of unmanned land or sea monitoring missions, the timestamp and trajectory of the carrying vessel are necessary for the required relay transmission of monitoring information [5]. Furthermore, the motion characteristics of the UAV and the vessel areessential for calculating the position of the vessel while the UAV is landing. Flight path planning of UAVs around dangerous areas have been studied in the literature [6]. Additionally, algorithms for the UAV minimum path length have been also considered in the literature [7; 8]. Researches for optimizing such flight paths, while preventing collision accidents and improving flight safety, have been also proposed [9; 10]. Finally, guidance challenges of UAVs flying along a predetermined trajectory have been presented [11]. [PERSON] et al. [12] proposed a UAV autonomous landing system based on model predictive control. The system dynamically models the UAV through a universal camera and uses the Kalman filter to come to the best location of the mobile platform. A model predictive controller was developed to land safely under various uncertainties and wind disturbances. [PERSON] et al. [13] proposed an autonomous landing algorithm that combines the Kalman filter with vision. By simulating the dynamics of different sea conditions and vessel decks and using a well-designed computer vision system, the ship deck is measured related to the posture of the drone. [PERSON] et al. [14] proposed a network-based UAV ship with a self-adaptive guidance system. By controlling the line-of-sight angle to track the angle of the flight path, the angle guidance law and the reverse guidance law are introduced. The purpose is to reduce the sensitivity of drone motion changes in ship parameters. [PERSON] and other scholars [15] proposed a drone autonomous landing system based on airborne monocular vision, which can improve the ability of autonomous tracking and landing for drones under simulated sea conditions. [PERSON], [PERSON] et al. [16] provided an artificial neural network-based autonomous landing method for UAVs. Using the data generated by simulating the sea motion, the current relative azimuth and distance of the UAV to the landing platform were calculated, and the coordinate pairs were calculated, which normalized processing for training neural networks. Currently, research on the planning of a fixed-wing UAV landing trajectory on moving vessels is very rare.