## 1 Introduction  
As a sort of autonomous ocean vehicles, USV (unmanned surface vehicles) can autonomously perform tasks in various environments without human intervention [1]. USV with non-linear dynamic characteristics have better endurance ability and payload ability than other unmanned vehicles, which makes them to be essential tools in the area of marine scientific research, marine resources development, marine environment monitoring and national marine security [2; 3; 4]. Autonomy and intellectualization are the crucial manifestations of the development of USV. Research on intelligent planning and controlling can improve the working performance of USV [5]. In the area of intelligent planning and control, path planning of single objectives, such as shortest distance, time optimality, energy consumption optimality, smoothness optimality and minimal risk, has gradually been studied furtherly [6]. Multi-objective motion planning and intelligent decision-making [7; 8] considering dynamic constraints of mobile robots is the difficulty of current research, where it is necessary to obtain more comprehensive and accurate environmental information by using environmental comprehensive perception methods, such as maps to optimize the path planning results. In this context, it is of great significance to study the path planning algorithm based on ENC (electronic navigation chart) considering the hydrodynamic characteristics of USVs for improving the USV's autonomous environmental awareness, optimizing the path planning results and improving the autonomy and intelligence level of USVs. A star algorithm, (A* algorithm) is a deterministic algorithm with outstanding consistency and completeness which is widely applied in the research of path planning. It was firstly proposed by [PERSON] et al. [9] and usually combined with grid method [10]. The minimum cost function is used to search the minimum cost path from the starting node to the end node. The grid-based A* path search methods are mainly based on the center point of the grid [11; 12; 13; 14; 15; 16] and grid vertexes [7; 10; 17; 18; 19]. Grid method can be divided into a uniform grid and non-uniform grid with diverse shapes and sizes of the grid. Aiming at the deficiencies of the grid-based A* algorithm, such as huge memory expenditure, large path turning angle and unsmooth path, studies have been done to improve it. [PERSON] et al. [20] found the optimal path with the least nodes in limited time by using A*. [PERSON] et al. [21] combined the A* and locally bounded plan, [PERSON] et al. [22] proved A* by direction priority sequential selection (DPSS), Improving path quality and computational capacity. [PERSON] et al. [23] introduced a model prediction algorithm and combined it with A* algorithm. The above work concentrates on improving the computational performance and path quality of traditional A* algorithm. [PERSON] [16] and [PERSON] [24] carried out the Theta* which released the limited that the center points of the grid can only moves along the center points within A*. [PERSON] et al. [13] solved the problem of unlimited direction with the theory of limit-cycle circle set. [PERSON], [PERSON] et al. [15] carried out Lazy Theta*, it was found that vertexes visibility check decreases by using this method. Improved dynamic search D* was put forward by [PERSON], [PERSON] et al. [17] based on A*, it was demonstrated that this algorithm fits the dynamic map very well. [PERSON], [PERSON] et al. [7] carried out a multi-objective A*, it improves the real-time performance of A*. [PERSON] et al. [25] applied A* to search the shortest and safest trajectory in electronic chart. [PERSON] et al. [26] carried out a curvature path planning algorithm which considering the turning ability of USV through perfecting A*. [PERSON], [PERSON] et al. [18] established the surface obstacles risk model considering current, traffic separation and maneuverability and improved the practicability of A* algorithm, but ignored the effect of water depth on navigation safety.