## 1 Introduction  
In contrast to manned ships, unmanned surface vessels (USVs) have been widely used as a new type of unmanned maritime platform in many fields such as marine observation, hydrographic survey, and accident search and rescue, leading to innovative changes. The unmanned platform at sea has greatly promoted the transition of the ship industry in the direction of information and unmanned intelligence. Gradually, USVs are replacing manned ships as the backbone in some fields. As an important development in unmanned technology, USV formation provides advantages in terms of lower cost, higher safety, stronger task adaptability, mutual synergy, and stronger data processing capabilities compared with single-boat manned ships. First, USV formation does not need to carry personnel, which can save expenses related to personnel life support, thereby greatly reducing the cost of the formation. Second, USVs are more flexible in dealing with a variety of complex environments. Under harsh maritime working conditions, the safety of the USV equipment is the only consideration, and there are no additional personnel adaptability and personal safety concerns. Third, the form and number of USV formations can be selected according to the requirements of the task to quickly adapt and respond to different task requirements. Fourth, USV formations can realize cooperative control through intelligent technology, and with higher formation synergy, the efficiency of mission execution can further be improved. Fifth, USV formations can carry more types of sensors and data acquisition equipment, allowing the collection, processing, and transmission of a large amount of data in real time, thereby providing support for more efficient and accurate mission execution. In USV navigation control formation, path planning and obstacle avoidance technologies are the keys to intelligent navigation. Obstacle avoidance technology is essential in ensuring the safe and efficient navigation of a USV, and path planning technology can generate efficient and feasible trajectories based on the information collected by various sensors and communication systems, whereby according to a USV's performance and environmental conditions, the USV is guided to reach the target location safely and accurately. Therefore, the usual method of intelligent navigation employs global path planning algorithms to achieve path planning in a known environment, and subsequently uses local path planning algorithms to achieve dynamic obstacle avoidance in a positional environment. The common global path planning algorithms include the reverse encryption algorithm (REA)* based on a grid map [1], A*, Dijkstra, and randomly exploring random tree (RRT)* algorithms based on the sampling method [2], as well as a probabilistic roadmap (PRM) and intelligent AG based on neural networks and ant colony algorithms [3; 4]. Local path planning algorithms include the artificial potential field method (PFM), dynamic window approach (DWA), time elastic band (TEB) [5], and vector field histogram (VFH) algorithms [6]. In integrating the above global and local path planning algorithms, the A* algorithm provides the advantages of easy implementation, wide applicability, and strong scalability, whereas DWA can fully combine the underactuated characteristics of USV and displays stronger adaptability. Therefore, the two were selected as the ideal global and local path planning algorithms for fusion to achieve ideal results in autonomous navigation applications. In the literature, [PERSON] et al. [7] used the key turning point extraction algorithm to eliminate redundant inflection points and redundant nodes in the global path, optimized the traditional A* algorithm, and fused it with DWA to optimize path length, smoothness, and safety. [PERSON] et al. [8] improved the key point selection strategy based on the traditional A* algorithm and fused it with DWA to construct a global optimal path evaluation function to achieve real-time optimal path planning. [PERSON] et al.