Efficiently salvaging shipwrecks is of the utmost importance for safeguarding shipping safety and preserving the marine ecosystem. However, traditional methods find it difficult to salvage shipwrecks in deep water. This article presents a novel salvage technology that involves multiple hydraulic claws for directly catching and lifting a 2500-ton shipwreck at 600 m depth. To ensure lifting stability, a semi-active heave compensation (SAHC) system was employed for each lifter to mitigate the effects of sea waves. However, the response delays arising from the hydraulic, control, and filtering systems resist the heave compensation performance. Predicting the barge motion to mitigate measuring and filtering delays and achieve leading compensation is necessary for the salvage. Therefore, a multivariate long short-term memory (LSTM) based neural network was trained to forecast the barge’s heave and pitch motions, exhibiting satisfactory results for the next 5 s. According to the results of numerical simulations, the proposed LSTM-based motion predictive SAHC system demonstrates remarkable effectiveness in compensating for shipwreck motion.

The deep-sea autonomous underwater vehicle (AUV) is equipment of vital importance for ocean exploration, monitoring, and surveying. With a variable buoyancy system (VBS), AUV can achieve rising, diving, and hovering in the water column. This paper proposes a deep-sea AUV with an oil bladder type hydraulic VBS, which controls the oil flow rate with a proportional valve. However, the implementation of accurate depth control for AUV faces various challenges due to the varying water density with depth, the non-linear feature of the hydraulic system, and the disturbance from sea flows and currents. To tackle these problems, a third-order linear active disturbance rejection controller (LADRC) and its fuzzy adaptive version were designed and implemented in MATLAB/Simulink based on the state-space function of the proposed AUV system. Compared with the conventional PID controller, the simulation results indicate that the proposed LADRC controller shows strong robustness to disturbance, with other advantages including smaller steady-state error, overshoot, settling time, and response time. Moreover, the proposed fuzzy LADRC controller could further decrease the overshoot caused by the increasing target distance. The results prove that the designed depth controllers can meet the control requirements of the proposed deep-sea AUV.

Underwater wireless optical communication (UWOC) for autonomous underwater vehicles is severely compromised by the coupling of oceanic optical turbulence and platform motion. Traditional static statistical models fail to capture the temporal evolution of these stochastic processes, hindering effective real-time beam tracking. This paper proposes a unified dynamic framework and a hybrid intelligent control strategy to address beam misalignment in turbulent environments. First, a physically motivated stochastic differential equation (SDE) model is derived from the Radiative Transfer Equation via diffusion approximation. Validated by an inverse Fokker–Planck approach, this model accurately reconstructs drift fields for diverse channel conditions, serving as a dynamic generator for time-varying fading. Second, to maintain robust link alignment, a hybrid Fuzzy-Reinforcement Learning control strategy is developed. This approach integrates the interpretability of fuzzy logic with the adaptive optimization of Q-learning, incorporating a supervisor mechanism to handle deep fading events. Numerical simulations and hardware-in-the-loop (HIL) experiments demonstrate the system’s efficacy. The proposed controller achieves a median alignment error of 3.64 mm and reduces transient errors by over 80% compared to classical PID controllers during signal recovery. These results confirm that the proposed framework significantly enhances link stability and tracking robustness for AUVs in complex random media.

Robotic 3D manipulation of small targets, such as laser ablation of urban-tree fruit balls, requires locally reliable depth. Mainstream RGB-D depth-refinement methods usually optimize image-wide metrics, which can leave task-relevant regions under-resolved and limit their direct use in precision robotic operation. This paper presents residual-gated RGB-D depth refinement (RG-RGD), a task-triggered local depth-refinement framework that reallocates computation toward task-relevant 3D geometry after a candidate target has been selected. The method contains three coupled designs: a self-play benefit-driven foveation mechanism that focuses network capacity on regions where refinement reduces geometric residuals; residual prediction with Bayesian measurement fusion that anchors predictions to available raw observations; and inertial measurement unit (IMU)-conditioned self-supervised training that improves inter-frame view consistency. Evaluated on the Visual Odometry with Inertial and Depth (VOID) benchmark, RG-RGD obtains competitive standard depth-completion metrics, including a mean absolute error (MAE) of 24.95 mm and an inverse mean absolute error (iMAE) of 10.85. On self-collected London plane fruit-ball sequences, the method reduces region-of-interest geometric error more strongly than full-image error. A qualitative demonstrative laser-ablation use case illustrates how refined local geometry can drive physical branch filtering, cutting-point selection, and gimbal-based execution. The results support task-triggered local depth refinement as a practical perception component for robotic manipulation.