Pollution caused by ship emissions has drawn attention from various countries. Because of the high density of ships in ports, channels, and anchorages and their proximity to the densely populated areas, ship emissions will considerably impact these areas. Herein, a Chinese seagoing ship is selected and a platform is established for monitoring the ship emissions to obtain detailed characteristics of the ship’s nearshore emissions. The ship navigation and pollution emission data are obtained under six complete operating conditions, i.e., berthing, manoeuvring in port, acceleration in a channel, cruising, deceleration before anchoring, and anchoring. This study analyzes the concentrations of the main emission gases (O₂, NO_X, SO₂, CO₂, and CO) and the average emission factors (EFs) of the pollution gases (NO_X, SO₂, CO₂, and CO) based on the engine power under different operating conditions. Results show that the change in O₂ concentration reflects the load associated with the main engine of the ship. The NO_X, SO₂, and CO₂ emission concentrations are the highest during cruising, whereas the peak CO emission concentration is observed during anchoring. The average EFs of NO_X and SO₂ based on the power of the main engine are the highest during cruising, and those of CO₂ and CO are the highest after anchoring. The ship EFs are different during acceleration and deceleration. By comparing the EFs along the coast of China and the global EFs commonly used to perform the emission inventory calculations in China, the NO_X EFs under different operating conditions is observed to be generally lower than the global EFs under the corresponding operating conditions. Furthermore, the SO₂ EF is considerably affected by the sulfur content in the fuel oil and the operating conditions of the ship. The average CO₂ EFs are higher than the global EFs commonly used during cruising, and the CO EFs are higher than the global EFs under all the conditions. Our results help to supplement the EFs for this type of ship under different operating conditions, resolve the lack of emission data under anchoring conditions, and provide data support to conduct nearshore environmental monitoring and assessment.

Path planning is crucial in the automatic navigation of USVs (unmanned underwater vehicles), which directly affects the operational efficiency and safety of USVs. In this paper, we propose a path-planning algorithm based on DDPG (Deep Deterministic Policy Gradient) and make a detailed comparison with the traditional A-Star algorithm and the recent Actor–Critical algorithm. Through a series of simulation experiments, it can be observed that the optimal path for USVs found by the DDPG-based path planning algorithm is faster and more accurate than that found by the other two methods. The experimental results show that the DDPG algorithm has a significant advantage in processing time and better performance in terms of path quality and safety. These results provide a strong reference for future research on automatic navigation for USVs and demonstrate the potential of DDPG-based path planning for USVs.

This study examines the course-keeping control of directionally unstable large oil tankers involving a pole in the right half plane. Treated as an unstable plant in control engineering, tankers are theoretically and experimentally investigated during the controller design process. First, the unstable plant is mirror-mapped to its corresponding stable minimum phase plant using the mirror-mapping technique, which enables an easy controller design. Then, a linear proportional-differential and a first-order filter controller is designed based on the closed-loop gain shaping algorithm, which requires only one controller parameter to be properly selected based on the system’s characteristics. Numerical simulation results confirmed that the designed controller can successfully stabilise an unstable plant subjected to external wind and wave disturbances. The controller designed with the proposed method is suitable for course-keeping control of directionally unstable large tankers. The controller design method is simple with an uncomplicated structure that can easily be implemented in engineering endeavours. Moreover, the rudder motion is small and soft.

Arctic shipping lanes are gradually opening, creating an urgent demand for unmanned surface vehicles (USVs) capable of safe and efficient navigation in drifting-ice environments. However, dense, highly dynamic sea ice poses significant challenges for existing obstacle-avoidance approaches. This study proposes a dynamically weighted hybrid obstacle avoidance algorithm integrating an improved VO module and an enhanced APF module. The optimized VO method refines the velocity sampling strategy and incorporates DCPA/TCPA-based risk screening to eliminate high-risk candidate velocities. The improved APF method introduces adaptive parameter regulation, virtual-target-based local minimum escape, and historical-velocity-driven oscillation suppression. Furthermore, a real-time dynamic weighting mechanism is designed to balance the contributions of the VO and APF modules according to the instantaneous environmental risk level. Extensive simulation experiments demonstrate that the proposed algorithm achieves reliable collision avoidance performance, high navigation efficiency, and strong environmental adaptability for USVs operating in dynamic Arctic drifting-ice environments.