## 1 Introduction  
The autonomous surface vessel (ASV) technologies have grown rapidly and obtained more attention than ever both in military and commercial applications [1]. Among them, path planning in complex navigation area is one of most difficult and challenging topics that need to be developed. The aim of path planning is to find the optimal navigation path from the start location to the destination location [2]. Generally, path planning can be divided to two classes, i.e., global path planning and local path planning (real-time) [3]. A global path planner usually calculates paths off-line in static environments with stationary obstacles. In contrast, a local path planner usually calculates paths on-line in dynamic environments with moving obstacles. For the global planning, path length, risk factor, dynamic constraint, etc. should be considered at the same time. Specifically, current effect and navigation rules should also be included during the path planning for a vessel. In this article, we try to propose a global path planning method to satisfy the requirements of safety and efficiency for autonomous surface vessel. For a vessel, especially an autonomous surface vessel, factors of the path length, the obstacle collision risk, the traffic separation rule and the manoeuvrability restriction should be all taken into account when a path is planned for navigation. To solve these problems, a lot of studies for vessel path planning have been conducted. For instance, [PERSON] reported a ship route finding method with turn penalties and collision avoidance [4]; [PERSON] et al. put forward a grid map-based path planning method with the kinematic constraint of the vessels [5]; [PERSON] et al. proposed a path-guided hybrid artificial potential field (PGHAPF) method to generate adaptive, collision-free and International Regulations for Preventing Collisions at Sea (COLREGS)-constrained paths in restricted waters [6; 7]. However, the majority of path planning methods did not consider the current effect, berthing requirements and traffic separation rule together much. In recent years, [PERSON] et al. proposed a dynamic augmented multi-objective particle swarm optimization (PSO) algorithm to obtain a path considering the path length, smoothness, economical efficiency and safety for unmanned surface vehicles [8]. Though this algorithm can get good performance of path planning, the PSO based method usually has the possibility of trapping in a local optima and the berthing requirements were not taken into account. For the berthing problem during path planning, [PERSON] et al. proposed an EEA* algorithm considering hydrodynamic effects, angular constraint and head angle confinement at the goal point [9]. The head angle confinement could be a good attempt to solve the berthing problem, which, however, cannot guarantee the availability of the berthing path accord to the instructions of berthing in [10]. To obey the traffic separation rule for path planning, on the basis of the research of [4], [PERSON] proposed an improved Evolutionary Algorithms (EA) method considering the Traffic Separation Schemes (TSS) [11]. We think that these attempts for solving the vessel path planning problems are beneficial. However, the demands of the vessel path planning proposed in this article have not been all satisfied, e.g., the risk of an obstacle in the upstream of current should be higher than that in the downstream of current, a berthing path should be generated considering the current effects, and the traffic separation rule should be modelled as a risk of path planning. Grid map-based global path planning algorithms have been applied frequently and widely because their advantages in computation time and avoidance of local optimum [12]. A Dijkstra path planning algorithm based on grid map was first proposed in [13], which is to find the shortest paths between nodes in a graph. Dijkstra algorithm creates an open list and a closed list to denote the next alternative nodes and the selected nodes, respectively.