The field of ship autonomous navigation has always garnered significant interest due to its future development potential for intelligent ships and unmanned ships. While there has been extensive research on autonomous navigation in open waters, less focus has been given to coastal waters due to the complexity of the environment and traffic flow. In order to resolve this problem, the dynamic adaptive decision-making method for ship autonomous navigation in coastal waters is presented. A digital twin environment model tailored to the characteristics of coastal waters has been developed, which can dynamically replicate the current ship navigation environment by incorporating multi-source heterogeneous information from ship equipment. The autonomous navigation decision-making method is obtained by integrating an Improved Velocity Obstacle (IVO) for ship collision avoidance and a Line of Sight (LOS) algorithm for ship trajectory tracking. Moreover, a time-rolling algorithm is employed to facilitate specific navigation decision-making in time-varying environments and to account for the uncertainty of target ship motion over time. This comprehensive algorithm has been tested and validated in two different scenarios. The results demonstrate that the proposed navigation decision-making method is reasonable and effective for the ship navigating in the coastal water, particularly in multi-ship encounter situations of target ships suddenly altering course or changing speed.

A maneuvering decision-making model based on time series rolling and feedback compensation methods is proposed to solve the problem of high traffic risk in Chengshantou traffic separation scheme (TSS) waters. Firstly, a digital traffic environment model suitable for the TSS waters is proposed. Secondly, a navigation risk identification method in these waters is constructed based on the digitized traffic environment and situation identification model in the Chengshantou TSS waters. Thirdly, considering the requirements of the rules and good seamanship, minimum course altering is obtained by combining the collision avoidance mechanism. Lastly, a maneuvering decision-making model in the TSS waters based on time series rolling and feedback compensation methods is developed. The simulation results show that the ship can correctly identify the collision risk and appropriately obtain maneuvering decisions, and can resume the planned route under the premise of ensuring safety. When the target ships alter course or change speed, the ship can also make adaptive maneuvering decisions. In summary, the proposed method meets the requirement of safe navigation in Chengshantou waters and provides a theoretical basis for the realization of intelligent navigation in waters similar to TSS.

Path planning for unmanned ships has become an important research topic in recent years. To enhance navigation safety and reduce collision risk, this study proposes an improved artificial potential field (IAPF) method. A route gravitational force is introduced to guide the ship back to the planned route after collision avoidance, while the repulsive force is optimized to improve path smoothness and obstacle-avoidance stability. A collision-risk-index-based repulsive force is further developed for dynamic obstacle avoidance, and its direction is modified according to the COLREGs. In addition, a time-sequential rolling path-planning framework integrating the APF and velocity obstacle algorithms is proposed to suppress path oscillation and adapt to target-ship maneuvers. The method is validated in head-on, crossing, and multiple-obstacle scenarios. The minimum passing distances are 1064.07 m, 1072.98 m, and 1109.66 m, respectively, and the maximum decision time is 148 ms. The results demonstrate that the proposed method can generate smooth, COLREGs-compliant paths, avoid local minima, adapt to dynamic encounters, and satisfy real-time collision-avoidance requirements.