## 1 Introduction
### Literature Review  
Internationally, the mainstream path planning methods include: [PERSON] [21], A\({}^{\star}\)[13; 15; 16; 20] and its variant bidirectional A\({}^{\star}\) (BA\({}^{\star}\)) [22; 23] based on node search; path planning based on ant colony optimization (ACO) [24; 25], Harris hawks optimization (HHO) [26; 27] and sparrow search algorithm (SSA) [28; 29; 30]; and path planning based on Q-Learning. At present, many scholars have applied IMO to maritime route planning. Some scholars have achieved some results by using the Martin algorithm [14], which provides a reference for applying IMO rules to route planning. Its principle is to search and optimize through preset rows of nodes pointing to the end point. Other scholars use A * to plan routes under the standard of IMO [13; 15; 16]. Some scholars also introduce a swarm intelligence algorithm to solve the problem of route planning when the amount of data is small [21; 24; 26]. However, the above algorithms still have the following defects. A class represented by Martin, due to the preset node arrangement, is very sparse; although the algorithm's running time is relatively short, the planning route is fuzzy. It is difficult to achieve theoretical results by projecting such routes into actual navigation scenarios; the calculation time of Dijkstra and A * is unacceptable to the industry because of their low search efficiency due to the rapid increase in data volume. However, based on the comparison of the results of multiple papers, the path they planned has high accuracy and is better applied in actual scenarios. The swarm intelligence algorithm is to complete the initialization of the population according to certain rules and continuously update the population through iteration to obtain the optimal solution to the problem. Maritime route planning is a non-deterministic polynomial (NP) hard problem just like route planning. The swarm intelligence algorithms have obvious advantages in solving most of the NP hard problems, but there are still some problems in solving the grid shortest path problem. When the number of grids is large, its performance is relatively unstable. We found that the main reason is that the swarm intelligence algorithm plans the key points of the path first and then connects all the key points into the planned path correctly using a connection algorithm. The performance of the connection algorithm will be severely affected when the number of grids increases. There are still many problems in the application of the swarm intelligence path planning algorithm, such as early convergence, falling into a local optimal solution, too many parameters to be adjusted, and a large amount of calculation for the grid data. Similarly, the current path planning based on Q-Learning [31; 32] is still subject to many obstacles in the application, such as the need for a huge amount of simulation and feedback to generate the path. When considering the synergy of multi-agents, Q-Learning can achieve a comprehensive and better effect. But in the path planning of only one individual, scholars prefer to use traditional path planning. In summary, the current engineering aspects believe that route planning is more effective using some variant algorithms based on A*. The essence of solving long-distance route planning under IMO and high-resolution standards is to improve the performance and efficiency of the algorithm. In the process of continuous planning of the algorithm, a large amount of grid data is continuously sent to the planning terminal according to the time period. At the same time, the algorithm also needs to retrieve whether the grid meets some quantitative suggestions in IMO, which makes the traditional algorithm and related improved algorithms insufficient to support its normal operation, and the time consumption is multiplied. It is noted that parallel technology is an effective means to improve the performance and efficiency of the algorithm.