To better study the biology and ecology of hadal trenches for marine scientists, the Hadal Science and Technology Research Center (HAST) of Shanghai Ocean University proposed to construct a movable laboratory that includes a mothership, several full-ocean-depth (FOD) submersibles, and FOD landers to obtain samples in the hadal trenches. Among these vehicles, the project of an FOD autonomous and remotely-operated vehicle (ARV) named “Dream Chaser” was started in July 2018. The ARV could work in both remotely-operated and autonomous-operated modes, and serves large-range underwater observation, on-site sampling, surveying, mapping, etc. This paper proposed a novel three-body design of the FOD ARV. A detailed illustration of the whole system design method is provided. Numerical simulations and experimental tests for various sub-systems and disciplines have been conducted, such as resistance analysis using the computational fluid mechanics method and structural strength analysis for FOD hydrostatic pressure using the finite element method and pressure chamber tests. In addition, components tests and the entire system tests have been performed on land, underwater, and in the pressure chamber in the laboratory of HAST, and the results are discussed. Extensive experiments of two critical components, i.e., the thrusters and ballast-abandoning system, have been conducted and further analyzed in this paper. Finally, the procedures and results of lake trials, South China Sea trials and the first phase of Mariana Trench sea trials of the ARV in 2020 are also introduced. This paper provides a design method for the novel three-body FOD ARV. More importantly, the lessons learned from the FOD pressure test, lake tests, and sea trials, no matter the success or failure, will guide future endeavors and the application of ARV Dream Chaser and underwater vehicles of this kind.

To address the phenomenon of color shift and low contrast in underwater images caused by wavelength- and distance-related attenuation and scattering when light propagates in water, we propose a method based on an attention mechanism and adversarial autoencoder for enhancing underwater images. Firstly, the pixel and channel attention mechanisms are utilized to extract rich discriminative image information from multiple color spaces. Secondly, the above image information and the original image reverse medium transmittance map are feature-fused by a feature fusion module to enhance the network response to the image quality degradation region. Finally, the encoder learning is guided by the adversarial mechanism of the adversarial autoencoder, and the hidden space of the autoencoder is continuously approached to the hidden space of the pre-trained model. The results of the experimental images acquired from the Beihai Bay area of China on the HYSY-163 platform show that the average value of the Natural Image Quality Evaluator is reduced by 27.8%, the average value of the Underwater Color Image Quality Evaluation is improved by 28.8%, and the average values of the Structural Similarity and Peak Signal-to-Noise Ratio are improved by 35.7% and 42.8%, respectively, compared with the unprocessed real underwater images, and the enhanced underwater images have improved clarity and more realistic colors. In summary, our network can effectively improve the visibility of underwater target objects, especially the quality of images of submarine pipelines and marine organisms, and is expected to be applied in the future with underwater robots for pile legs of offshore wellhead platforms and large ship bottom sea life cleaning.

Addressing challenges in underwater manipulator operations, including unknown environments, hydrodynamic disturbances, and dynamic force tracking, this study proposes a novel adaptive variable impedance control (AVIC) framework. Firstly, the framework employs a sliding mode controller (SMC) for end-effector position control, augmented by a nonlinear disturbance observer for real-time compensation of system uncertainties and underwater disturbances. Secondly, an AVIC strategy is developed, integrating parameter identification and force-tracking error feedback to adjust impedance parameters dynamically. Unlike conventional constant impedance control, which requires precise knowledge of the environment and exhibits limited adaptability to dynamic forces, the proposed method utilizes force-position coupling analysis to optimize impedance parameters and trajectory compensation concurrently. The position correction term, derived from the force-tracking error, dynamically modifies the reference trajectory via the SMC, achieving synergistic optimization of position tracking and dynamic contact force control. Lyapunov stability analysis proves the stability of the closed-loop system. Comparative simulations demonstrate a significant performance improvement of the proposed control law. Compared to PID position control, it reduces the settling time by 64.18% and decreases position error by 94.44%. When evaluated against constant impedance control and variable damping impedance control, the proposed method reduces force overshoot by 83.68% and 79.88%, respectively, and shortens force settling time by 57.62% and 48.35%, respectively. Moreover, it exhibits enhanced robustness to variations in environmental parameters. Simulation experiments confirm the control law’s superior transient performance and minimize steady-state error in tracking dynamic contact forces.

Remotely operated vehicles (ROVs) face challenges in achieving optimal trajectory tracking performance during underwater movement due to external disturbances and parameter uncertainties. To address this issue, this paper proposes a position and attitude control strategy for underwater robots based on a reinforcement learning active disturbance rejection controller. The linear active disturbance rejection controller has achieved satisfactory results in the field of underwater robot control. However, fixed-parameter controllers cannot achieve optimal control performance for the controlled object. Therefore, further exploration of the adaptive capability of control parameters based on the linear active disturbance rejection controller was conducted. The deep deterministic policy gradient (DDPG) algorithm was used to optimize the linear extended state observer (LESO). This strategy employs deep neural networks to adjust the LESO parameters online based on measured states, allowing for more accurate estimation of model uncertainties and environmental disturbances, and compensating the total disturbance into the control input online, resulting in better disturbance estimation and control performance. Simulation results show that the proposed control scheme, compared to PID and fixed parameter LADRC, as well as the double closed-loop sliding mode control method based on nonlinear observers (NESO-DSMC), significantly improves the disturbance estimation accuracy of the linear active disturbance rejection controller, leading to higher control precision and stronger robustness, thus demonstrating the effectiveness of the proposed control strategy.

Under the background of the information age, the folk environment, production mode and aesthetic value of grass cloth embroidery have undergone great changes. grass cloth embroidery is facing a new choice or withering away. The article systematically analyzes and summarizes the current situation of grass cloth embroidery from the external environment and internal relations. Through the analysis of the protection, inheritance, innovation and regeneration of grass cloth embroidery art, finally, the Development strategies of grass cloth embroidery regeneration under the background of the information age is proposed from three aspects: digital protection, informatization inheritance, and technological innovation.