This study proposed a vision-based underwater vertical docking guidance and positioning method to address docking control challenges for human-operated vehicles (HOVs) and unmanned underwater vehicles (UUVs) under complex underwater visual conditions. A cascaded detection and positioning strategy incorporating fused active and passive markers enabled real-time detection of the relative position and pose between the UUV and docking station (DS). A novel deep learning-based network model, YOLO-D, was developed to detect docking markers in real time. YOLO-D employed the Adaptive Kernel Convolution Module (AKConv) to dynamically adjust the sample shapes and sizes and optimize the target feature detection across various scales and regions. It integrated the Context Aggregation Network (CONTAINER) to enhance small-target detection and overall image accuracy, while the bidirectional feature pyramid network (BiFPN) facilitated effective cross-scale feature fusion, improving detection precision for multi-scale and fuzzy targets. In addition, an underwater docking positioning algorithm leveraging multiple markers was implemented. Tests on an underwater docking markers dataset demonstrated that YOLO-D achieved a detection accuracy of mAP@0.5 to 94.5%, surpassing the baseline YOLOv11n with improvements of 1.5% in precision, 5% in recall, and 4.2% in mAP@0.5. Pool experiments verified the feasibility of the method, achieving a 90% success rate for single-attempt docking and recovery. The proposed approach offered an accurate and efficient solution for underwater docking guidance and target detection, which is of great significance for improving the safety of docking.

Path planning is crucial for autonomous underwater vehicles (AUVs) and plays a vital role in ocean engineering. To improve the search efficiency and accuracy, this study proposed a three-dimensional path-planning method for AUVs based on the interval multi-objective secretary bird optimization algorithm (IMOSBOA). This method addressed path-planning challenges under imprecise current predictions and uncertain hazard source locations. First, the marine environment was modeled in three dimensions using the interval theory. Second, the danger levels and navigation times were set as the optimization objectives to construct a three-dimensional path-planning mathematical model. Finally, IMOSBOA was proposed and applied to solve the optimization problem. To verify the optimization performance of the new algorithm, its planning results were compared with those of the other algorithms. The simulation results demonstrated that the robustness and search capability of the proposed algorithm surpass those of comparative algorithms.

This study proposes a receding horizon optimization-based docking control method to address the autonomy and safety challenge of underwater docking between manned submersibles and unmanned vehicles, facilitating the integration of docking trajectory generation and tracking control. A novel approach for optimizing and generating reference trajectory is proposed to construct a docking corridor that satisfies safe collision-free and visual guidance effective regions. It generates dynamically feasible and continuously smooth docking trajectories by rolling optimization. Subsequently, a docking trajectory tracking control method based on nonlinear model predictive control (NMPC) is designed, which is specifically tailored to address thruster saturation and system state constraints while ensuring the feasibility and stability of the control system. The control performance and robustness of underwater docking were validated through simulation experiments. The optimized trajectory generated is continuous, smooth, and complies with the docking constraints. The control system demonstrates superior tracking accuracy than backstepping control, even under conditions where the model has a 40% error and bounded disturbances from currents are present. The research findings presented in this study contribute significantly to enhancing safety and efficiency in deep-sea development.

This study proposed an event-triggered quantized model predictive control (ETQMPC) method for the dynamic docking of unmanned underwater vehicles (UUVs) and human-occupied vehicles (HOVs). The proposed strategy employed a non-periodic control approach that initiated the non-linear model predictive control (NMPC) optimization and state sampling based on tracking errors and deviations from the predicted optimal state, thereby enhancing computing performance and system efficiency without compromising the control quality. To further conserve communication resources and improve information transfer efficiency, a quantitative feedback mechanism was employed for sampling and state quantification. The simulation experiments were performed to verify the effectiveness of the method, demonstrating excellent docking trajectory tracking performance, robustness against bounded current interference, and significant reductions in computational and communication burdens. The experimental results demonstrated that the method outperformed in the docking trajectory tracking control performance significantly improved the computational and communication performance, and comprehensively improved the system efficiency.

The control of underwater docking is critical in deep-sea operations, particularly during terminal movements, where dynamic recovery control is essential owing to its significant impact on operational efficiency and safety. A novel dynamic docking model predictive control (DDMPC) method was proposed, which incorporated a fuzzy-based dynamic weight adjustment mechanism to enable multi-objective control of position tracking, velocity tracking, and obstacle avoidance under multi-dimensional constraints. This method with adaptive weights addressed the challenges associated with dynamic docking control for both manned and unmanned underwater vehicles more effectively. In addition, a nonlinear docking disturbance observer (DDO) was designed to mitigate the effects of external currents and docking disturbances, thereby improving system robustness. The effectiveness of the proposed method was validated through simulation experiments that focused on tracking control and obstacle avoidance under the influence of external currents and docking interference. The simulation results indicated that the tracking performance of the proposed approach surpassed that of the conventional model predictive control (MPC) and backstepping control (BSC) methods. The proposed controller demonstrated high accuracy in position and velocity trajectory tracking, while ensuring safe target recovery and robustness against docking disturbance forces. These results confirm the efficacy and robustness of the proposed method in enhancing the performance and safety of underwater docking operations.