In the last few years, autonomous ships have attracted increasing attention in the maritime industry. Autonomous ships with an autonomous collision avoidance capability are the development trend for future ships. In this study, a ship manoeuvring process deduction-based dynamic adaptive autonomous collision avoidance decision support method for autonomous ships is presented. Firstly, the dynamic motion parameters of the own ship relative to the target ship are calculated by using the dynamic mathematical model. Then the fuzzy set theory is adopted to construct collision risk models, which combine the spatial collision risk index (SCRI) and time collision risk index (TCRI) in different encountered situations. After that, the ship movement model and fuzzy adaptive PID method are used to derive the ships’ manoeuvre motion process. On this basis, the feasible avoidance range and the optimal steering angle for ship collision avoidance are calculated by deducting the manoeuvring process and the modified velocity obstacle (VO) method. Moreover, to address the issue of resuming sailing after the ship collision avoidance is completed, the Line of Sight (LOS) guidance system is adopted to resume normal navigation for the own ship in this study. Finally, the dynamic adaptive autonomous collision avoidance model is developed by combining the ship movement model, the fuzzy adaptive PID control model, the modified VO method and the resume-sailing model. The results of the simulation show that the proposed methodology can effectively avoid collisions between the own ship and the moving TSs for situations involving two or multiple ships, and the own ship can resume its original route after collision avoidance is completed. Additionally, it is also proved that this method can be applied to complex situations with various encountered ships, and it exhibits excellent adaptability and effectiveness when encountering multiple objects and complex situations.

The inherent complexity of traffic patterns in inland river tributary waterways presents significant challenges in predicting ship behavior, resulting in elevated accident risks compared to general waterways. With the intelligent development of inland navigation, conducting research on autonomous navigation decision-making for tributary waterway ships is crucial to improving navigation safety and efficiency. Based on the characteristics of the navigation environment, a digital traffic environment model for inland waterways with tributaries is constructed to meet the information requirements of autonomous navigation decision-making. The ship encounter process is analyzed, and a collision risk identification model based on trajectory derivation is proposed, which accounts for the uncertainty of ship maneuvering in tributary waterways. Under the premise of compliance with the “Rules of the People’s Republic of China for Inland River Collision Avoidance” (RPRCIRCA) and adherence to good seamanship, an autonomous navigation decision-making method is developed by integrating an improved Line-of-Sight tracking model with a collision avoidance strategy based on exhaustive course-speed maneuver combinations. The system’s performance is validated through simulation experiments, with trajectory tracking deviations demonstrated to remain below 49 m under wind-current disturbances while minimum encounter distances with target ships are maintained above 48 m. Adaptive response capabilities to maneuvering variations of target ships are confirmed, along with the preservation of navigation precision in complex tributary environments.

For the motion control of ships in confined and curved waterways, from broad coastal channels to narrow river bends, conventional methods often struggle to ensure both tracking accuracy and navigational safety. A key deficiency is the inability of standard algorithms to incorporate the nuanced principles of good seamanship. To address this, a novel, hierarchical adaptive control framework is proposed. The core novelty of this framework lies in its versatile and adaptive guidance rules, which embed maritime practice into the control loop for different navigating scenarios. In general maritime channels with wind and current, these rules function to ensure robust, high-fidelity route tracking. For the most challenging inland river curved channels, it is further enhanced to generate a strategic, non-centerline trajectory that replicates the crucial inland navigational practice of “holding high and taking low”. This is complemented by a reinforcement learning-based strategy at the control layer, which performs real-time tuning of PID gains to adapt to the vessel’s dynamics. The framework’s dual capabilities were systematically validated. The core adaptive algorithms proved effective for robust control in curved channels under wind and current disturbances. Furthermore, the full framework, including the seamanship-informed strategy, demonstrated superior performance in the most complex inland river scenarios. Compared to a conventional controller, the proposed method reduced the peak cross-track error by over 40% and increased the minimum safety margin from the bank by more than 49% under a strong 3 m/s cross-current. An effective solution for motion control is thus provided, bridging the gap between modern control theory and the context-dependent expertise of practical pilotage.