The plumes generated during the collection of polymetallic nodules in the deep sea may have a significant impact on the marine ecosystem. Therefore, this article reviews the progress in deep-sea mining and monitoring technologies related to plumes. It is suggested that specific areas of environmental interest (APEIs) and positive altitude characteristic regions (such as seamounts and hills) in the process of polymetallic nodule collection can serve as refuges for benthic organisms and provide a biological basis for the recovery of biodiversity in mining areas. Water-supported vessels, pipeline lifting systems, and hydraulic collection methods are the least disruptive and most promising methods. By sorting out the deep-sea mining process, plumes can be roughly classified into seabed disturbance plumes and tailing plumes. The best way to address plume formation is at the source when developing environmentally friendly mining vehicles. The evaluation of plumes is crucial for the sustainable development of the environment and seabed resources. However, the mechanism is not clear at present. Therefore, laboratory simulation and in situ monitoring need to be coordinated, and attention should be paid to the impact on benthic marine organisms as much as possible during original operations. Plume research in the deep-sea mining process will also provide favorable support for the possible future development of seabed resources.

As core structural components for deep-sea oil and gas exploitation, deep-sea risers are continuously subjected to wind, wave, and current loads, which readily induce vortex-induced vibration (VIV) and further trigger structural fatigue damage. Furthermore, the progressive exploitation of deepwater and ultra-deepwater oil and gas resources has exacerbated the complexity and risk of riser VIV, rendering it a critical engineering problem that urgently requires effective solutions. This paper presents a comprehensive review of numerical studies on deep-sea riser VIV, systematically elaborating the fundamental principles, research advances, and application scenarios of three mainstream numerical approaches: semi-empirical models, computational fluid dynamics (CFD) models, and computational structural dynamics (CSD) models. The respective accuracy advantages and inherent limitations of each numerical method are thoroughly analyzed. Additionally, this review focuses on key research hotspots and challenging issues, including VIV responses of flexible risers, dynamic fluid–structure boundary coupling, internal–external flow coupling effects, wake interference of multi-riser systems, efficient VIV prediction, and vibration suppression optimization. The current technical bottlenecks in existing research are clarified. This study aims to provide a systematic theoretical framework and methodological reference for subsequent numerical investigations and engineering applications of riser VIV, and offer technical support for the optimal structural design and safety risk prevention of deep-sea riser systems.

To address the issue of increasing navigation errors in low-cost autonomous underwater vehicles (AUVs) operating without assisted positioning underwater, this paper proposes a Virtual GPS Model (VGPSM) based on deep sequence learning. This model is integrated with an Extended Kalman Filter (EKF) to provide a high-precision navigation solution for AUVs. The VGPSM leverages the time-series characteristics of data from sensors such as the Attitude and Heading Reference System (AHRS) and the Doppler Velocity Log (DVL) while the AUV is on the surface. It learns the relationship between these sensor data and GPS data by utilizing a hybrid model of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM), which are well-suited for processing and predicting time-series data. This approach constructs a virtual GPS model that generates virtual GPS displacements updated at the same frequency as the real GPS data. When the AUV navigates underwater, the virtual GPS displacements generated using the VGPSM in real-time are used as measurements to assist the EKF in state estimation, thereby enhancing the accuracy and robustness of underwater navigation. The effectiveness of the proposed method is validated through a series of experiments under various conditions. The experimental results demonstrate that the proposed method significantly reduces cumulative errors, with navigation accuracy improvements ranging from 29.2% to 69.56% compared to the standard EKF, indicating strong adaptability and robustness.

When autonomous underwater vehicles (AUVs) perform underwater tasks, the absence of GPS position assistance can lead to a decrease in the accuracy of traditional navigation systems, such as the extended Kalman filter (EKF), due to the accumulation of errors. To enhance the navigation accuracy of AUVs in the absence of position assistance, this paper proposes an innovative navigation method that integrates a position correction model and a velocity model. Specifically, a velocity model is developed using a dynamic model and the Optimal Pruning Extreme Learning Machine (OP-ELM) method. This velocity model is trained online to provide velocity outputs during the intervals when the Doppler Velocity Log (DVL) is not updating, ensuring more consistent and reliable velocity estimation. Additionally, a position correction model (PCM) is constructed, based on a hybrid gated recurrent neural network (HGRNN). This model is specifically designed to correct the AUV’s navigation position when GPS data are unavailable underwater. The HGRNN utilizes historical navigation data and patterns learned during training to predict and adjust the AUV’s estimated position, thereby reducing the drift caused by the lack of real-time position updates. Experimental results demonstrate that the proposed VM-PCM-EKF algorithm can significantly improve the positioning accuracy of the navigation system, with a maximum accuracy improvement of 87.2% compared to conventional EKF algorithms. This method not only improves the reliability and accuracy of AUV missions but also opens up new possibilities for more complex and extended underwater operations.

Ship trajectory tracking controllers based on deep reinforcement learning (DRL) are widely applied in various fields such as autonomous driving and robotics due to their strong adaptive learning capabilities and optimization decision-making ability. However, ship trajectory control faces challenges such as long training cycles and poor convergence performance. These issues are primarily caused by the unreasonable design of algorithm models and reward functions, which limit the performance optimization and energy efficiency improvements in real-world navigation. In this paper, we propose a ship trajectory tracking control algorithm based on deep reinforcement learning. The proposed algorithm introduces maximum entropy theory and experience replay techniques. Additionally, it enhances the reward function module by adding reward terms and fitting weight designs. A three-dimensional simulation environment is constructed to validate the proposed method. The results demonstrate that the controller designed in this study outperforms traditional DRL controllers in terms of fast convergence, convergence stability, and final reward values. The controller meets the requirements for tracking conventional trajectories and shows stable and efficient performance in both wide-area water search experiments and river channel traversal experiments. These experimental results provide valuable insights for future research directions.