To improve the accuracy of ship track prediction, the improved Grey Wolf Optimizer (GWO) and Support Vector Regression (SVR) models are incorporated for ship track prediction. The hunting strategy of dimensional learning was used to optimize the move search process of GWO and balance exploration and exploitation while maintaining population diversity. Selection and updating procedures keep GWO from being stuck in locally optimal solutions. The optimal parameters obtained by modified GWO were substituted into the SVR model to predict ship trajectory. Dimension Learning Grey Wolf Optimizer and Support Vector Regression (DLGWO-SVR), Grey Wolf Optimized Support Vector Regression (GWO-SVR), and Differential Evolution Grey Wolf Optimized Support Vector Regression (DEGWO-SVR) model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on DLGWO-SVR has higher prediction accuracy and meets the requirements of ship track prediction. The results of ship track prediction can not only improve the efficiency of marine traffic management but also prevent the occurrence of traffic accidents and maintain marine safety.

Decision-making for collision avoidance in complex maritime environments is a critical technology in the field of autonomous ship navigation. However, existing collision avoidance decision algorithms still suffer from unstable strategy exploration and poor compliance with regulations. To address these issues, this paper proposes a novel autonomous ship collision avoidance algorithm, the dynamically adjusted entropy proximal policy optimization (DAE-PPO). Firstly, a reward system suitable for complex maritime encounter scenarios is established, integrating the International Regulations for Preventing Collisions at Sea (COLREGs) with collision risk assessment. Secondly, the exploration mechanism is optimized using a quadratically decreasing entropy method to effectively avoid local optima and enhance strategic performance. Finally, a simulation testing environment based on Unreal Engine 5 (UE5) was developed to conduct experiments and validate the proposed algorithm. Experimental results demonstrate that the DAE-PPO algorithm exhibits significant improvements in efficiency, success rate, and stability in collision avoidance tests. Specifically, it shows a 45% improvement in success rate per hundred collision avoidance attempts compared to the classic PPO algorithm and a reduction of 0.35 in the maximum collision risk (CR) value during individual collision avoidance tasks.

Marine accidents such as collision, running aground and striking a reef not only bring on personnel casualty, but also make great threat to ocean environment. For making "the safer the vessel sail, the cleaner the ocean is", the problem of vessel automatic anti-collision should be resolved as soon as possible. Automatic generation of intelligent decision-making is one of the key technologies to realize automatic collision avoidance for vessel at sea. How to utilize the present ship- handling simulator's technology to check up the present research production is a valuable research work. The authors firstly described the basic idea of intelligent automatic method for vessel collision avoidance according to its researching actualities. And then discussed the construction of Simulation and Testing Platform for Navigation Safety and Automatic Collision Avoidance(NSACA for short), which was based on the ship's maneuvering mathematic model and Electronic Chart Display and Information System(ECDIS for short), and also dissertated the test method. Finally gave some typical examples from a great deal of simulating tests and analyzed them. The results indicated that this kind of simulating approach possessed the excellence on the flexibility, efficiency, safety and economy compared to the sea test on real ship.

Existing research on auto-berthing of ships has mainly focused on the design and implementation of controllers for automatic berthing. For the real automatic docking processes, not only do external environmental perturbations need to be taken into account but also motion paths, docking strategies and ship mechanical constraints, which are important influential factors to measure autonomous docking methods. Through a literature review of ship path planning and motion control for automatic berthing, it is found that many studies ignore the interference of the actual navigational environment, especially for ships sailing at slow speed when berthing, or do not consider the physical constraints of the steering gear and the main engine. In this paper, we propose a hybrid approach for autonomous berthing control systems based on a Linear Quadratic Regulator (LQR) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which systematically addresses the problems involved in the berthing process, such as path planning, optimal control, adaptive berthing strategies, dynamic environmental perturbations and physically enforced structural constraints. The berthing control system based on the LQR and modified LQR-CMA-ES have been validated by simulation work. The simulation results show that the proposed method is able to achieve the automatic docking of the ship well and the system is robust and well adapted to environmental disturbances at slow speed when docking.

There has been significant interest in the research field of ship automatic navigation, particularly in the area of autonomous berthing. To address the key challenges of path planning and control during ship berthing, we propose an enhanced Linear−Quadratic Regulator (LQR) control approach, reinforced by the Covariance Matrix Adaptation Evolution Strategy (CMA−ES), along with an adaptive berthing strategy decision model. This integrated framework encompasses ship motion control, path planning, and berthing strategy selection to facilitate adaptive and autonomous ship berthing. Initially, a dynamic mathematical model of ship motion is established, taking into account wind and current interference effects. Subsequently, an adaptive environment−aware berthing strategy model is introduced to enable automatic selection of berthing strategies based on spatial relationships between environmental factors and the berth. By utilizing the refined LQR method, autonomous motion control for ship berthing is achieved. To validate the effectiveness of our controller, comprehensive simulation analyses are conducted under varying operating conditions to encompass crucial factors such as large drift angle characteristics of ships, shallow water effects, and bank effects across seven diverse working conditions. The simulation results underscore the robustness of our proposed method in responding to environmental interference while demonstrating its capability to select appropriate berthing strategies based on varying operational scenarios.