## 1 Introduction
#### Current Advances in Related Research
The proposed solution not only encompassed control algorithms and software but also introduced a leaning gear with mechanical self-adaptation as an integral component. Another study by [PERSON] et al. [18] improved landing stability by implementing an adjustable tripod. The proposed tripod included omni-wheels at the end of each arm. Furthermore, the study presented in [19] introduced a dedicated system designed specifically for landing on moving vessels, which could also be used to land on moving vehicles. In this particular approach, the burden of time- and resource-consuming calculations, together with data analysis, was shifted to a ground station situated near the take-off and landing site. This strategy aimed to streamline the operation of the UAV by offloading computational tasks to a centralized system. Thus, the UAV could focus on its primary objectives without being encumbered by extensive onboard processing requirements. The estimation of relative position in these landing systems was achieved through sensor fusion using a stereo camera for the vision system, fiducial markers to mark the object, and position estimation via a deep neural network (DNN). Finally, the position was estimated using a Kalman filter that fused the data from the visual system and telemetry from the inertial measurement unit (IMU). To ensure adaptability, finite-state machine-based logic was implemented, allowing for trajectory adjustments based on a qualitative indicator of the tracking accuracy and the influence of ground effects. The publication by [PERSON] et al. [20] provided a comprehensive review and analysis of the mobile landing platforms that have been developed and presented in the literature. It delved into various aspects, such as take-off and landing procedures, the accuracy and precision of position estimation, control algorithms, fundamental rules and algorithms for trajectory generation, and systems for autonomous docking. The study offered valuable insights and findings in these areas. In a similar vein, the implementation of visual positioning for non-stationary landing pads was explored in [21] using the robotic operating system framework. This approach shares similarities with the solution proposed herein, emphasizing the importance of visual cues for determining the relative position of a UAV in an unmanned sea vehicle (USV)'s local coordinate system (or vice versa). While vision systems are widely popular for relative positioning, alternative methods have also been investigated. In [22], a set of ultrasonic sensors was mounted on a USV to detect the presence of the multirotor over the landing pad. This approach demonstrated an alternative means of achieving relative positioning between the two robots. To facilitate coordinated navigation between a UAV and a USV, a coupled navigation system was introduced in conjunction with a newly developed guide point generation algorithm. This system ensured synchronized movement and enhanced the overall efficiency and effectiveness of the mission. Through these research contributions, a deeper understanding of mobile landing platforms, their functionalities, and their interaction with UAVs and USVs has been achieved. These studies provide valuable insights for the development of robust and efficient systems for UAV and USV collaborations. The incorporation of a manipulator arm and docking terminal on a heterogeneous mobile landing pad was explored in [23]. The docking terminal featured a socket on the bottom specifically designed to match the UAV's configuration. Precise localization was achieved through the utilization of fiducial markers, ensuring accurate positioning during the docking process. The control system implemented in the study combined predictive, reactive, and optimal approaches to manage the landing and docking operations. This fusion of control strategies ensured efficient and reliable coordination between the UAV and the mobile landing pad.