Intelligent ship navigation has become a core technological enabler for accelerating the digital, intelligent and low-carbon transformation of the global shipping industry. Driven by the International Maritime Organization's greenhouse gas emission reduction targets and China's strategic policies for intelligent shipping development, ship intelligent navigation systems are evolving from experimental validation toward engineering applications. However, several key challenges remain in practical implementation. In particular, the coordination mechanism among navigation decision-making, propulsion response, and energy-efficiency constraints lacks a unified architectural representation. In addition, the adaptive mapping logic between shipboard intelligent capability levels and shore-based hierarchical operation modes has not yet been systematically established. To address these gaps, this study focuses on the architectural design of a bridge–engine integrated ship intelligent navigation system and investigates the adaptive mechanisms of ship–shore collaborative operation.

In terms of research methods, this paper firstly defines the functional connotation, system boundaries, and essential characteristics of bridge–engine integration based on existing regulations for intelligent ships and relevant domestic and international research outcomes. Secondly, a hierarchical intelligent navigation architecture is established. A three-stage evolution pathway for shipboard autonomy is proposed, including enhanced navigation, assisted navigation and autonomous navigation. In parallel, three shore-based operation modes are defined, namely monitoring, remote control, and supervisory navigation. On this basis, an asymmetric ship–shore functional adaptation matrix is developed to systematically clarify recommended combinations, restricted feasible combinations, and inapplicable combinations, with typical application scenarios clearly specified. Finally, key enabling technologies are comprehensively analyzed, including environmental perception and situational awareness, navigation decision-making and path planning control, intelligent engine room operation and energy efficiency management, system testing and evaluation, as well as ship–shore human-machine collaborative control.

The research results systematically present the overall hierarchical architecture of a bridge–engine integrated ship intelligent navigation system under ship–shore collaboration. They clarify the functional connotation and operational boundaries of each shipboard capability level and shore-based operation mode, and reveal the asymmetric adaptation rules and principles of control authority allocation between shipboard and shore-end systems.

It is concluded that the proposed system architecture and asymmetric adaptation matrix can provide a solid theoretical foundation and technical reference for the engineering implementation of intelligent ship navigation systems and the development of relevant industrial standards. Furthermore, the findings offer important theoretical guidance for improving ship–shore collaborative operation mechanisms, standardizing control authority switching, and promoting the development and application of a new-generation intelligent and green maritime transportation system.

Intelligent ships at sea are influenced by environmental interference, and the influence of the uncertainty of model parameters leads to the problem of low ship motion control accuracy, so it is necessary to improve the resistance of the ship's control algorithm to such interference.

Based on an L1 adaptive control algorithm and Gaussian process regression (GPR) model, an L1 adaptive controller combined with a GPR model controller for underactuated ship path tracking control is proposed, and the control law is derived using the Lyapunov control function. The L1 adaptive controller is a new technique that considers both robustness and fast adaptivity. The closed-loop control system has proven to be consistently globally asymptotically stable, while the GPR model is used to model sudden disturbances and environmental disturbances during ship navigation, and achieve the rapid elimination of the effects of such disturbances in combination with the adaptive law.

The simulation results show that adding the GPR model reduces the average rudder amplitude by 14.9%, average absolute heading error by 23.2%, and maximum absolute heading error by 12.1%. The effects of environmental disturbances can be cancelled out faster and a stable state reached more rapidly than in cases without added disturbances.

The proposed L1-GPR adaptive controller can effectively resist various disturbances during navigation.

Aiming at the problem of the autonomous berthing control of tugboats, this study focuses on the application of a target tracking control strategy based on virtual leadership.

First, the autonomous berthing process of the tugboat is transformed into the target tracking control process of the virtual tugboat and actual tugboat. A kinematics model of the berthing system is then designed. Considering special environmental interference in the berthing scenario, the backstepping method and sliding mode control method are used to design an autonomous berthing controller for an azimuth stern drive tugboat. Three different sliding mode surfaces are provided and the stability is verified by the Lyapunov function. Finally, a simulation test is used to verify the control effect through the berthing trajectory, speed error and distance error.

The simulation results show that the designed tugboat autonomous berthing control strategy and controller have a good effect in the tugboat autonomous berthing scenario and good performance in the face of uncertain disturbances of the system.

The control strategy and berthing controller designed in this paper have good applicability and robustness. The autonomous berthing control of tugboats is realized from a new angle, providing a new direction for subsequent research on the berthing control of tugboats.

The navigation control system is the brain and center of an intelligent ship, and the safety and economy of the ship's navigation are directly determined by its control performance. Therefore, it is necessary to validate the ship intelligent navigation control system. This paper proposes a general verification platform, the variable stabilized ship, to simulate the sailing state of the target ship at different scales and with different hydrodynamic characteristics.

First, the structure of the variable stability ship is proposed on the basis of the model-following principle, and an analysis is made of the three degrees of freedom movement characteristics. The steady error between the variable stability ship and the target ship is determined by their position errors, and the variable stability controller is built on the basis of sliding mode control. Finally, Matlab simulations are carried which verify that the proposed method can maneuver the variable stability ship to follow the states of the target ship.

Simulation results show that the proposed method maneuver the variable stability ship to follow the states of the target ship.

The results of this study can provide references for the validation and verification of navigational control systems for intelligent ships.

To meet the requirements of remotely controlling ship in curved, narrow and crowded inland waterways, this paper proposes an approach that consists of CNN-based algorithms and knowledge based models under ship-shore cooperation conditions.

On the basis of analyzing the characteristics of ship-shore cooperation, the proposed approach realizes autonomous perception of the environment with visual simulation at the core and navigation decision-making control based on deep reinforcement learning, and finally constructs an artificial intelligence system composed of image deep learning processing, navigation situation cognition, route steady-state control and other functions. Remote control and short-time autonomous navigation of operating ships are realized under inland waterway conditions, and remote control of container ships and ferries is carried out.

The proposed approach is capable of replacing manual work by remote orders or independent decision-making, as well as realizing independent obstacle avoidance, with a consistent deviation of less than 20 meters.

The developed prototype system carries out the remote control operation demonstration of the above ship types in such waterways as the Changhu Canal Shenzhou line and the Yangtze River, proving that a complete set of algorithms with a CNN and reinforcement learning at the core can independently extract key navigation information, construct obstacle avoidance and control awareness, and lay the foundation for inland river intelligent navigation systems.