## 1 Introduction
[9] validated and analyzed the A* and DWA fusion algorithm for a single USV. [PERSON] et al. [10] introduced the safety distance factor into the heuristic function of the A* algorithm to improve the safety of the algorithm's planned path and subsequently used the planar structure method to smooth and optimize the global planned path to realize the path planning of the A* and DWA fusion algorithm. [PERSON] et al. [11] quantified and analyzed the obstacle information in the environment, thereby adjusting the weights of the heuristic function of the A* algorithm to improve algorithmic efficiency and flexibility. In addition, they improved the path smoothing based on [PERSON]'s algorithm to realize dynamic path planning in complex environments. [PERSON] et al. [12] optimized the predicted cost function and heuristic function of the A* algorithm to tune the global planning path to the requirements of the task. [PERSON] et al. [13] reduced the turn angle for path planning in the improved A* algorithm by treating travel time as the cost and adjusting the weight of the heuristic function according to obstacle information. [PERSON] et al. [14] incorporated the key track points in the global planning path of A* into the evaluation function of the DWA algorithm to solve the problem of the traditional DWA algorithm which fails to evaluate under a combination of \"C\"-shaped obstacles, and improved the ability to navigate through dense obstacle zones. [PERSON] et al. [15] proposed a hybrid dynamic path planning algorithm for FAGV based on improved A* and improved DWA. This algorithm can avoid obstacles dynamically without being far away from the global optimal path. [PERSON] et al. [16] proposed the improved A* algorithm combined with the greedy algorithm for a multi-objective path planning strategy. This algorithm can achieve a smoother path and reduce the path length by about 5%. [PERSON] et al. [17] proposed a path planning algorithm based on A* and DWA to achieve global path optimization while satisfying security and speed requirements for unmanned aerial vehicles (UAVs). This algorithm shortens the path length, reduces the planning time, improves the smoothness of the UAV path, and enhances the safety of UAV path obstacle avoidance. [PERSON] et al. [18] proposed a distributed approach for multi-robot navigation which combines the concept of reciprocal velocity obstacle (RVO) and the scheme of deep reinforcement learning (DRL) to solve the reciprocal collision avoidance problem with limited information. The above research indicates various application directions for the A* and DWA fusion algorithm, mostly focusing on the path planning of a single intelligent body, and less on aggregation formation-related problems such as collaborative path planning of multiintelligent bodies and cross-encounters. In USV aggregation formation, the traditional A* and DWA fusion algorithm is not applicable to this scenario for the following reasons. First, the global path ramp of the traditional A* algorithm has numerous turning points, which causes the USVs to jitter in the local path planning stage. Second, the USVs are under-driven models, and the traditional fusion algorithm cannot unify the heading range of the USVs when arriving at the end point, which forms a certain obstacle to the cooperative movement after aggregation formation. Third, the intersection of multiple USV paths is not considered because of a lack of an encounter coordination strategy, which can easily lead to collision accidents or reduce obstacle avoidance efficiency. Therefore, this study improves the traditional A* and DWA algorithms to address the three aforementioned problems. The algorithm is improved and optimized by smoothing the global planning path, specifying the end position heading range, and adding an encounter coordination strategy to make it suitable for USV aggregation formation. In this paper, Section 1 mainly analyzes the importance of USV navigation control research and introduces the research status of USV navigation and formation assembly.