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.

Collision prevention is critical for navigational safety at sea, which has developed rapidly in the past decade and attracted a lot of attention. In this article, an improved velocity obstacle (IVO) algorithm for intelligent collision avoidance of ocean-going ships is proposed in various operating conditions, taking into count both a ship’s manoeuvrability and Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). An integrated model combines a three-degree-of-freedom manoeuvring model with ship propeller characteristics to provide a precise prediction of ships in various manoeuvring circumstances. In the given case, what is different to present studies, this improved algorithm allows for decision-making in two ways: altering course and changing speed. The proposed technique is demonstrated in a variety of scenarios through simulation. The findings reveal that collision-avoidance decision-making can intelligently avoid collisions with the target ships (TSs) in multi-ship situations.