## 2 Literature Review  
With the development of the shipping industry, more and more academics have focused on the path planning of autonomous ships. Currently, path planning algorithms for ships can be classified into three categories: traditional algorithms, bionic intelligence-based algorithms, and machine learning-based algorithms. The A*, velocity obstacle (VO) and artificial potential field (APF) algorithms are the most common traditional path planning methods. [PERSON] et al. [11] devised a dynamic path planning method for collision avoidance based on the A* algorithm and ship navigation rules. The results of the experiments reveal that the method may provide more reasonable paths in complex scenarios to trade-off navigation risk and economic efficiency. However, the planned routes are rarely smooth enough for ships to follow. [PERSON] et al. [12] combined the VO algorithm and the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) to create a path planning method that allows ships to operate safely in a dynamic environment. The simulation results indicate that the method is capable of giving a deterministic and effective path for collision avoidance in a variety of encounter situations. Nonetheless, in some circumstances, the VO may generate a local minimum solution. [PERSON] et al. [13] used the APF algorithm to construct a virtual gravitational field to steer the ship to the target waypoint in a complex navigation environment. [PERSON] et al. [14] proposed a novel path planning algorithm for ship automatic collision avoidance based on a modified APF method considering the constraints of both the COLREGs and the motion characteristics of the ship. Experimental results reveal that the proposed method is accurate and reliable, and satisfies the demands of engineering applications. Despite the fact that the APF has the advantages of simple algorithm structure and high computational efficiency, it is easy to fall into local minimum and target point oscillation. The genetic algorithm (GA), ant colony optimization (ACO) algorithm and particle swarm optimization (PSO) algorithm are examples of bionic intelligence-based path planning methods. [PERSON] et al. [15] developed a global search path planning technique by merging the multi-objective decision theory with the GA. Experimental results demonstrate that the path planning strategy is quite effective at collision avoidance. [PERSON] [16] developed an improved ACO with fuzzy logic to deal with local path planning of unmanned surface vehicles (USVs) by taking into account wind, current, wave, and dynamic obstacles. Experiments reveal that the modified algorithm achieves a faster convergence speed. [PERSON] et al. [17] demonstrated a hybrid multi-criteria ship routing method based on an improved PSO algorithm. The strategy tries to optimize the meteorological risk, fuel consumption, and navigation time associated with a ship. Experiments show that the route planning algorithm can create a number of possible paths while ensuring safety, environmental friendliness, and economic viability. It serves as a resource for captains and shipping companies when deciding on a route. [PERSON] et al. [18] adopted the PSO algorithm to produce the shortest path from the start point to the goal point. Simulation results show that the algorithm is capable of obtaining a COLREGs-compliant and practical navigation path for all four traffic scenarios. However, such bionic intelligence algorithms typically require more prior information and a considerable amount of calculation, which leads to issues such as long calculation time and local minimum; therefore, they are primarily utilized in auxiliary decision-making systems rather than autonomous navigation systems. Reinforcement learning (RL) based path planning algorithms are the most prevalent form of machine learning-based path planning methods. [PERSON] et al. [19] proposed a Q-learning-based path planning and maneuvering approach for unmanned cargo ships.