Unmanned Surface Vehicles (USVs) are intelligent machines that have been widely studied in recent years. The safety of USVs’ activities is a priority issue in their applications; one effective method is to delimit an exclusive safety domain around the USV. Besides considering collision avoidance, the safety domain should satisfy the requirements of encounter situations in the COLREGs (International Regulations for Preventing Collisions at Sea) as well. Whereas the model providing the safety domain for the USVs is defined through the experience of the manned ships, a specific model for USVs has been rarely studied. A dynamic navigation safety domain (DNSD) for USVs was proposed in this paper. To construct the model, the essential factors that could affect the navigation safety of the USVs were extracted via a rough set, and the extension functions of these factors were carried out. The DNSD was employed in various situations and compared with the ship domain models of common ships. It was found that the domain boundary can be automatically corrected according to the change in the working conditions when the DNSD is in use. Compared with the Fujii and Coldwell models, the DNSD can provide a larger safety area for a USV’s action of collision avoidance.

It is essential to promote the intelligence and autonomy of Maritime Autonomous Surface Ships (MASSs). This study proposed an automatic collision-avoidance method based on an improved Artificial Potential Field (APF) with the formation of MASSs (F-MASSs). Firstly, the navigation environment model was constructed by the S-57 Electronic Navigation Chart (ENC) data in Tianjin Port. The Formation Ship State Parameter (FSSP) definition was proposed for the port environment under multiple constraints that considered the navigation conditions of the MASSs. The formation pattern transformation was settled by changing the formation ship state parameter. Considering the constraints of an ‘unmanned–manned’ encounter situation, the static obstacles, and the design of the channel area improved artificial potential method for the formation. Finally, the simulation experiment was carried out in the sea near Tianjin Port to verify the effectiveness of the algorithm under multiple constraints. The results indicate that the method can satisfy the integrated operation of collision avoidance and path following in a constrained environment, and it can support the application of merchant F-MASS autonomous navigation in the future.

Maneuver simulation of a standard ship model gives indication of numerical accuracy. In the numerical calculation of ship maneuvering, uncertainty analysis is a necessary step to ensure the accuracy of the calculation. In this study, uncertainty pair analysis is carried out in the simulation of the turning circle motion of the standard ship model ONRT in waves. According to the uncertainty analysis procedure recommended by the International Towing Tank Conference (ITTC), the change of ship resistance caused by the number of grids is studied to determine the influence of grid density on the numerical prediction. The simulation of turning motion in waves is carried out based on the uncertainty analysis. It is found that the minimum number of overset grids for this simulation is 1.4 million. The numerical results are fairly accurate compared to experimental results, and this technique provides a method with low calculated cost for this simulation.

Field observations revealed that a mooring buoy rapidly drifts in a reciprocating motion along an arcuate path between two extreme positions. When the anchor point is considered the origin and viewed from an aerial perspective, this movement resembles a pendulum. The implications of this motion for data acquisition efficiency prompted our inquiry into this phenomenon. The comparative analysis of the model’s different movements under wave-only, current-only, and wave–current conditions demonstrates that currents are the source inducing this pendulum-like motion. To investigate the mechanism of this current-driven motion, the flow field around the buoy was visualized through numerical simulations. Specifically, the CFD results aligned with the field data and confirmed that periodic vortex shedding induces oscillatory forces, which dominate the rapid reciprocating movement. The findings emphasize the significant impact of fluid viscosity and the resulting vortex effects on the motion characteristics of buoys. They can provide a foundation for addressing more applied problems of data error-correcting and trajectory predictions.