With the continuous development of science and technology, unmanned ship has gradually become a hot spot in the field of marine research. In practical applications, unmanned ships need to have long-range navigation and high efficiency, so that they can accurately perform tasks in the marine environment. As one of the key technologies of unmanned ship, path planning is of great significance to improve the endurance of unmanned ship. In order to meet the requirements, this paper proposes a path planning method for long distance unmanned ships based on reinforcement learning angle precedence ant colony improvement algorithm. Firstly, canny operator is used to automatically extract navigation environment information, and then MAKLINK graph theory is applied for environment modelling. Finally, the basic ant colony algorithm is improved and applied to the path planning of unmanned ship to generate an optimal path. The experimental results show that, compared with the traditional ant colony algorithm, the path planning method based on the improved ant colony algorithm can achieve a voyage duration of nearly 7 km for unmanned ships under the same sailing environment, which has certain practicability and popularization value.

Abstract
1. Introduction
    1.1. Research background and significance
    1.2. Research status and trend of path planning
    1.3. Research contents and methods
2. Route planning of long-voyage unmanned ship based on reinforcement learning with priority ant colony improvement algorithm
    2.1. Extraction of navigation environment information of long-voyage unmanned ship
    2.2. Modelling of long-voyage unmanned ship sailing environment based on MAKLINK graph theory
    2.3. Route planning for unmanned captain voyage based on improved ant colony algorithm
3.Route planning simulation of long-voyageunmanned ship based on improved ant colonyalgorithm
    3.1. Parameter setting
    3.2. Experimental procedure
    3.3. Experimental results
4. Conclusion
Acknowledgements
References

• Wu Hengtao(Dept. of Navigation and Shipping College, Shandong Jiaotong University)
• Qiao Zhen(Dept. of Navigation and Shipping College, Shandong Jiaotong University)
• Zhang Suyu(Dept. of Shandong Branch, Postal Savings Bank of China) Corresponding Author