The bottom of the ocean is rich in mineral resources, and deep-sea mining has been a research hotspot in recent years. As a key part of deep-sea mining operation, polymetallic nodule collection technology has been researched in many countries around the world. The distribution of deep-sea polymetallic nodule mining areas and the characteristics of nodules are summarized, which provides a reference for the study of collection technology and the optimization of pick-up device structure. In order to further establish a deep-sea mining collection technology system, the current development status of polymetallic nodule mechanical, mechanical–hydraulic composite and hydraulic collection technologies are summarized, and the analysis shows that hydraulic collection technology has a more promising commercialization prospect. For the hydraulic collection technology, the research progress of suck-up-based collection technology, Coandă-effect-based collection technology, double-row hydraulic collection sluicing technology and other collection technologies are summarized from three aspects: collecting principle, device structure parameter optimization, and sea trial situation, and the key technical problems of hydraulic ore collection are put forward. Through the comparative analysis of the pick-up efficiency, energy consumption, environmental disturbance and other performances of different devices, it is found that the Coandă-effect-based hydraulic collection technology has better comprehensive performance. A structural design evaluation indicator for the collection head of hydraulic collection technology is proposed, and the prospect of further research on hydraulic collection technology is put forward.

Prediction of the spatial configuration of the umbilical cable during deep-sea mining in-situ tests is of great significance because dynamic change may cause the umbilical cable to touch the ground or overturn the mining vehicle. In the present paper, a real-time prediction method for the dynamic spatial configuration of the umbilical cable during the deep-sea mining process is proposed. At first, the environmental information, position and motion of the vessel–umbilical cable–mining system were collected by sensors arranged at different locations. Then, the data were converted and transformed to the local vessel coordinate system. After that, the commercial software OrcaFlex was employed to conduct real-time simulation, in which the spatial configuration of the umbilical cable was predicted by the lumped mass method. Furthermore, the proposed real-time simulation method was employed in a sea trial test of deep-sea mining in an area with a water depth of 1100 m. Comparing the prediction results with the trajectory of the USBL beacon obtained from the monitoring data, the maximum distance of some specific points was close to 5 m, and most of them were less than 3 m. Meanwhile, it could also give the dynamic responses of the deep-sea mining system. For example, the maximum top tension of the umbilical cable was less than 15 kN, which could be used to evaluate the health condition of the system. During the sea trial test, the proposed method played an important role in ensuring the safety of the umbilical cable during wide-range movement of the mining vehicle. With characteristics of good real-time performance, accurate prediction, high reliability and stability, the proposed method could enhance the confidence of engineers for on-site operation as a powerful digital tool for visualization of the subsea working state.

Dense polymetallic nodule fields are found in different areas of the Pacific and Indian Oceans. However, limited knowledge exists about microbial diversity, processes and functions in deep-sea polymetallic nodule sediments. This study investigated microbial diversity, composition and function in sediments from various locations and depths in a western Pacific polymetallic nodule province. Sediment cores were collected, DNA extracted, and the V3–V4 regions of the 16S rRNA gene were sequenced using Illumina MiSeq. The test results show that the abundance and diversity of microbial communities in sediments from different sites vary significantly. The dominant microbial communities at the family level in the three sediment samples were all mainly Marinobacteraceae and Alcanivoracaceae. Sediment samples from core 1 had similar microbial structures and microbial community functions. Surface sediment had higher species richness, diversity and evenness than the middle layer. The dominant phylum at different depths was consistent. There was significant spatial heterogeneity in the microbial community within sediments from polymetallic nodule regions. This study expands on our knowledge of the spatial and vertical distribution of microbial community diversity beneath polymetallic nodules in deep-sea settings.

A multivector direct model predictive control (DMPC) scheme is proposed for the dual three-phase permanent magnet synchronous machine (DTP-PMSM) drive system to achieve closed-loop control for both fundamental current tracking and harmonic current minimization. The proposed multivector DMPC scheme employs four active voltage vectors, including two large vectors and two basic vectors for implicit modulation. Moreover, the control optimization problem is formulated as a four-dimensional quadratic programming problem, which is suitable for real-time implementation. The proposed multivector DMPC scheme enables fast and accurate tracking of the fundamental current as well as effective suppression of harmonic currents in both the fundamental and harmonic subspaces. In addition, a Kalman filter observer is incorporated to enhance robustness against model uncertainties and disturbances. Experimental results on a DTP-PMSM test bench verify that the proposed multivector DMPC scheme effectively reduces torque ripple, improves current quality, and enhances both steady-state and transient performance of the system.

To enhance the autonomous operation capability of deep-sea mining vehicle formations, this study addresses the issues of slow convergence in formation path planning and insufficient obstacle avoidance flexibility under complex environments by investigating a global path planning and local obstacle avoidance strategy based on an improved RRT algorithm*. Through dynamic elliptical sampling, adaptive goal-biased sampling, safe distance detection, and path smoothing optimization, the efficiency and passability of path planning are improved. For the obstacle avoidance of formation members, a priority determination model incorporating local obstacle avoidance, formation contraction, and transformation is designed, and methods such as Gaussian distribution fan-shaped sampling and trajectory backtracking are proposed to optimize the local planning effect. Simulation results show that this method can effectively improve the path planning quality and obstacle avoidance performance of mining vehicle formations in complex environments. Specifically, when in a longitudinal formation, the maximum inter-vehicle error is approximately 15.1%, and the average error is controlled within 3.5%; when in a triangular formation, the maximum inter-vehicle error is approximately 20%, and the average error is controlled within 4.2%, indicating promising application prospects.

The umbilical cable is an important component of the deep-sea mining system, serving as the sole connection between the surface support vessel and the seabed mining system. The harsh marine environment poses significant challenges to umbilical cable safety. Methods based on traditional time-domain simulation are time-consuming and it is hard for them to meet the needs of real-time prediction. In this paper, a novel forecasting method is proposed, PFLM-PSML, which integrates the theory of potential flow (PF), the lumped mass method (LM), and a parameterised supervised machine learning method (PSML) to forecast the safety of umbilical cables. Six environmental and system parameters—wave height, wave direction, current velocity, current direction, cable length, and the relative position between vehicle and vessel—are used as model inputs, while outputs include cable top tension, curvature, and mining vehicle overturning moments. The model employs Latin hypercube sampling and an active learning approach with hybrid kernel functions to efficiently map input–output relationships. Validation through numerical simulations and a 6000 m deep-sea trial confirms that the proposed method achieves high accuracy and a computational speed thousands of times faster than traditional approaches, enabling real-time mechanical state prediction. Parametric analyses reveal that increases in wave height, current velocity, and water depth lead to higher cable tension and vehicle overturning moments. The PFLM-PSML framework demonstrates strong potential for real-time safety assessment and control of deep-sea mining systems under complex ocean conditions.