###### Abstract  
In this paper, an innovative method is proposed to improve the global path planning of Unmanned Surface Vehicles (USV) in complex sea areas, combining fluid mechanic calculations with an improved A* algorithm. This method not only generates smooth paths but also ensures feasible global solutions, significantly enhancing the efficiency and safety of path planning. Firstly, in response to the water depths limitation, this study set up safe water depths, providing strong guarantees for the safe navigation of USVs in complex waters. Secondly, based on the hydrological and geographical characteristics of the study sea area, an accurate ocean environment model was constructed using Ansys Fluent software and computational fluid dynamics (CFD) technology, thus providing USVs with a feasible path solution on a global scale. Then, the local sea area with complex obstacles was converted into a grid map to facilitate detailed planning. Meanwhile, the improved A* algorithm was utilized for meticulous route optimization. Furthermore, by combining the results of local and global planning, the approach generated a comprehensive route that accounts for the complexities of the maritime environment while avoiding local optima. Finally, simulation results demonstrated that the algorithm proposed in this study shows faster pathfinding speed, shorter route distances, and higher route safety compared to other algorithms. Moreover, it remains stable and effective in real-world scenarios. unmanned surface vehicles; complex sea areas; safe water depths; path planning; computational fluid dynamics; improved A* algorithm +
Footnote †: journal: Marine Science and Engineering  
the optimal solution using the RRT algorithm is not guaranteed, and the searched paths often contain many sharp turns. During navigation, the path should be smooth to enhance USV efficiency and ensure safety. Therefore, [PERSON] (2018) proposed a multi-target artificial potential field algorithm based on an improved artificial potential field that complies with USV constraints and ensures smooth paths but struggles with local minima and jitter in complex environments. In addition, the algorithm needs to take into account the requirements of the ship's characteristics. To cope with the limitations of USVs, such as insufficient propulsion, large inertia, and time delays, and to ensure that they can accurately follow their intended routes, path planning must consider multiple objectives simultaneously, such as shortest path, safety, smoothness, and control constraints. In interdisciplinary applications, multi-objective optimization algorithms and strategies for Unmanned Aerial Vehicles can be borrowed to improve the efficiency and adaptability of the path-planning system ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019). Intelligent optimization algorithms solve the optimal path problem by constructing a cost function applicable to multiple constraints ([PERSON] et al., 2016; [PERSON] et al., 2018; [PERSON] et al., 2019). However, in practical applications, they may face challenges such as premature convergence, local optimality, difficult parameter tuning, large computational complexity, and slow convergence speeds. In recent years, a streamline method incorporating the concept of ideal fluid from fluid mechanics has been integrated into path planning ([PERSON] et al., 2018). By constructing potential fields and generating smooth paths, Fluid dynamics methods effectively resolve issues of local minima and oscillations found in traditional algorithms and avoid the path distance issues associated with graph search algorithms. These methods have been applied to underwater gliders and USVs ([PERSON] et al., 2018; [PERSON] et al., 2019). [PERSON] (2019) combined two-dimensional potential flow theory and computational fluid dynamics (CFD) to develop a traffic separation scheme for dynamic route planning in narrow channels.