The underwater hyperspectral imager enables the detection and identification of targets on the seafloor by collecting high-resolution spectral images. The distance between the hyperspectral imager and the targets cannot be consistent in real operation by factors such as motion and fluctuating terrain, resulting in unfocused images and negative effects on the identification. In this paper, we developed a novel integrated underwater hyperspectral imaging system for deep sea surveys and proposed an autofocus strategy based on liquid lens focusing transfer. The calibration tests provided a clear focus result for hyperspectral transects and a global spectral resolution of less than 7 nm in spectral range from 400 to 800 nm. The prototype was used to obtain spectrum and image information of manganese nodules and four other rocks in a laboratory environment. The classification of the five kinds of minerals was successfully realized by using a support vector machine. We tested the UHI prototype in the deep sea and observed a Psychropotidae specimen on the sediment from the in situ hyperspectral images. The results show that the prototype developed here can accurately and stably obtain hyperspectral data and has potential applications for in situ deep-sea exploration.

The quantitative evaluations of mineral resources and delineation of promising areas in survey regions for future mining have attracted many researchers’ interest. Cobalt-Rich manganese crusts (Mn-crusts), as one of the three significant strategic submarine mineral resources, lack effective and low-cost detection devices for surveying since the challenging distribution requires a high vertical and horizontal resolution. To solve this problem, we have built an engineering prototype parametric acoustic probe named PPPAAP19. With the echo data acquired by the probe, the interpretation of the accurate thickness information and the seabed classification using the deep learning network-based method are realized. We introduce the acoustic dataset of the minerals collected from two sea trials. Firstly, the preprocessing method and data augment strategy used to form the dataset are described. Afterward, the performances of several baseline approaches are assessed on the dataset, and the experimental results show that they all achieve high accuracy for binary classification. We find that the end-to-end approach for binary classification based on a 1D Convolution Neural Network has a comprehensive advantage. Such a demonstration validates the possibility of binary classification for recognizing the ferromanganese crust only in an acoustic manner, which may significantly contribute to the efficiency of the survey.

In view of the shortage of structural defect monitoring methods for deep submersibles, numerical simulation and experimental research on underwater SAW propagation based on interdigital transducers are carried out in this paper. PVDF interdigital transducer (PVDF-IDT) has shown considerable potential in the application of structural health monitoring because of its micro size, soft material characteristics, and the characteristics of long-term bonding on the surface of the tested structure. In order to realize the application of IDT on submersible or underwater structures, it is necessary to understand the influence of underwater environment on IDTs with different structures. The underwater attenuation of IDT with 2–5 mm wavelength and the underwater attenuation of Lamb (A0 mode) wave on a 4 mm thick titanium alloy plate is obtained through COMSOL software simulation. The experimental verification shows that the simulation results match with the actual situation, which proves that COMSOL software can accurately calculate the acoustic attenuation of surface waves at the solid–liquid interface. At the same time, the underwater attenuation of IDT with different structures is very different, providing important design parameters for the underwater interdigital transducer. In this paper, it is found that the Lamb wave has significant advantages over the Rayleigh wave in the health monitoring of underwater thin plate structures.

Deep-sea biological detection is essential for deep-sea resource research and conservation. However, due to the poor image quality and insufficient image samples in the complex deep-sea imaging environment, resulting in poor detection results. Furthermore, most existing detection models accomplish high precision at the expense of increased complexity, and leading cannot be well deployed in the deep-sea environment. To alleviate these problems, a detection method for deep-sea organisms based on lightweight YOLOv5n is proposed. First, a lightweight YOLOv5n is created. The proposed image enhancement method based on global and local contrast fusion (GLCF) is introduced into the input layer of YOLOv5n to address the problem of color deviation and low contrast in the image. At the same time, a Bottleneck based on the Ghost module and simAM (GS-Bottleneck) is developed to achieve a lightweight model while ensuring sure detection performance. Second, a transfer learning strategy combined with knowledge distillation (TLKD) is designed, which can reduce the dependence of the model on the amount of data and improve the generalization ability to enhance detection accuracy. Experimental results on the deep-sea biological dataset show that the proposed method achieves good detection accuracy and speed, outperforming existing methods.

With advances in underwater exploration and intelligent ocean technologies, wheeled underwater mobile robots are increasingly used for seabed surveying, engineering, and environmental monitoring. However, complex terrains centered on seabed soft slopes—characterized by wheel slippage due to soil deformability and force imbalance arising from slope variations—pose challenges to the accuracy and robustness of trajectory tracking control systems. Model predictive control (MPC), known for predictive optimization and constraint handling, is commonly used in such tasks. Yet, its performance relies on manually tuned parameters and lacks adaptability to dynamic changes. This study introduces a hybrid grey wolf-particle swarm optimization (GPSO) algorithm, combining the exploratory ability of a grey wolf optimizer with the rapid convergence of particle swarm optimization. The GPSO algorithm adaptively tunes MPC parameters online to improve control. A kinematic model of a four-wheeled differential-drive robot is developed, and an MPC controller using error-state linearization is implemented. GPSO integrates hierarchical leadership and chaotic disturbance strategies to enhance global search and local convergence. Simulation experiments on circular and double-lane-change trajectories show that GPSO-MPC outperforms standard MPC and PSO-MPC in tracking accuracy, heading stability, and control smoothness. The results confirm the improved adaptability and robustness of the proposed method, supporting its effectiveness in dynamic underwater environments.

Manned underwater robot is an important platform to carry various sensors and working tools to finish the in situ measurement and sampling in the deep sea. Due to its limited loading capacity, the device it will be carried, usually, requires to be able to dive to its working depth with a small volume and weight. According to the application requirements of deep-sea sediment temperature gradient in situ measurement observed, a detector system that can obtain data in situ is designed, which mainly includes a titanium alloy electronic cabin and a temperature probe. A comprehensive design analysis method is used to compare and analyze the ultimate static pressure, stability limit load, bending moment load, and transient temperature field on the underwater operation conditions. Optimal dimensions and filled media in the probe are decided. Finally, the pressure resistance test is finished in the lab, and scientific application is conducted on Jiaolong’s 127th dive in the Northwest Indian Ocean, which successfully sampling the temperature gradient data of deep-sea sediments in the Daxi hydrothermal area. The method introduced in this article can effectively improve the safety and reliability of deep-sea structure and greatly reduce costs throughout the design cycle.