In this work we propose a solution to the Unmanned Surface Vessels (USV) path-following problem by suggesting an improved model predictive control (MPC) based on global course constraint (GCC) and event-triggered mechanism (ETM). The designed GCC guidance law incorporates the LOS concept and overcomes its lack of reference signal near the waypoint. The developed MPC controller involves an ETM that ultimately optimizes the deviation according to its prediction ability and effect on energy saving. We challenge our model against current methods on the evaluation platform vessel “Yukun” of Dalian Maritime University and demonstrate the effectiveness and superiority of the suggested path-following algorithm over existing methods.

In the case of marine accidents, monitoring marine oil spills can provide an important basis for identifying liabilities and assessing the damage. Shipborne radar can ensure large-scale, real-time monitoring, in all weather, with high-resolution. It therefore has the potential for broad applications in oil spill monitoring. Considering the original gray-scale image from the shipborne radar acquired in the case of the Dalian 7.16 oil spill accident, a complete oil spill detection method is proposed. Firstly, the co-frequency interferences and speckles in the original image are eliminated by preprocessing. Secondly, the wave information is classified using a support vector machine (SVM), and the effective wave monitoring area is generated according to the gray distribution matrix. Finally, oil spills are detected by a local adaptive threshold and displayed on an electronic chart based on geographic information system (GIS). The results show that the SVM can extract the effective wave information from the original shipborne radar image, and the local adaptive threshold method has strong applicability for oil film segmentation. This method can provide a technical basis for real-time cleaning and liability determination in oil spill accidents.

In order to further explore more efficient identification algorithms that can solve the ship motion identification modeling problem, a novel identification algorithm for 3-DOF ship maneuvering modeling is proposed in this paper. Based on the recursive least-squares method, the proposed algorithm combines multi-innovation and nonlinear innovation techniques that focus on the innovation’s processing. In addition, its convergence capability is theoretically demonstrated. The identification algorithm shows good accuracy and convergence that is verified by a simulation experiment on a container ship. Furthermore, another simulation experiment using the full mission ship handling simulator demonstrates its generalization capability.

Ocean-going vessels usually sail at a constant speed in open sea. Rule 8 of the International Regulations for Preventing Collisions at Sea (COLREGs) recommends alteration of course as the single most effective way to avoid collisions. This makes the study of constant speed collision avoidance (CSCA) critical for the development of automatic navigation systems for ocean-going vessels. This paper presents a 4-DOF MMG ship motion mathematical model that can capture the maneuvering characteristics of a ship using collision avoidance (CA). Drawing upon a velocity obstacle (VO) method, a velocity-derived heading obstacle algorithm (VHO) that meets COLREGs (Rule 8, 13, 14 and 15) constraints is proposed. The algorithm does not control the speed of the ship, but rather guides its course to prevent collisions. To aid determination of when CA action should be employed, a new function is also defined. CSCA simulations for scenarios involving both two ships and multiple ships were designed and carried out. In simulations, all ships could avoid other ships, keeping a distance of more than 1 nautical mile from each other, which demonstrates that the VHO algorithm ensures that ships can always keep a safe distance from other ships.