In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments.

This paper contributes to an anti-windup control scheme for path-following of underactuated autonomous underwater vehicles (AUVs) subject to constrained inputs. First, a disturbance observer is employed to estimate the model uncertainties and external disturbances on the nominal vehicle model, which are referred to as mismatched lumped disturbances. On the basis of that, a three-time scale singular perturbation control law is proposed, taking advantage of the time scale separation caused by the different rates of various variables. Furthermore, a novel disturbance observer-based anti-windup modification is developed to handle possible input saturation. And stability for the singularly perturbed system subject to actuator saturation is also established, using a method called “manifold reconfiguration” in a geometric view. This results in a relatively simple constrained controller and reduces implementation complexity. Finally, simulation and experimental results are presented to substantiate the effectiveness of the proposed method for path-following of underactuated AUVs exposed to unknown disturbances and input constraints.

A direct numerical simulation method based on Reynolds Average Navier–Stokes (RANS) equations is used to carry out numerical prediction studies of submarine emergency rising in this paper. Firstly, a numerical simulation of the nonpropelled model without rudder manipulating is accomplished as the basis of this study. The numerical prediction results are in good agreement with the experimental data, which proves the feasibility and accuracy of the direct numerical simulation method. Meanwhile, both model tests and numerical simulation results reveal the strong coupling effect between roll and yaw motions during the underwater ascending process. Based on the above observation and analysis, another two numerical simulations, whose grids are identical with the non-manipulation simulation, are conducted respectively under the condition of rudder steering, i.e., course keeping simulation and self-propulsion simulation. An optimized S surface controller based on conditional determination is designed to manipulate the rudders. As a result, the yaw angle of the latter two simulations is limited within the range of 0.2° and 0.8° respectively, proving the effectiveness of the S surface controller. Correspondingly, the maximum roll angle is reduced by 96% and 70% respectively, which demonstrates that the roll stability is significantly enhanced by improving the course keeping ability of the model. Moreover, it is also proven from the perspective of reverse verification that, the excessive yaw deviation is the root cause of emergency rising roll instability for the situation of incidence angle lower than 30°.

The hydrodynamic interaction between an AUV (Autonomous Underwater Vehicle) and a recovery device, such as a real-scale submarine, is a crucial factor affecting the safe recovery of the AUV. This paper employs the CFD (Computational Fluid Dynamics) method to investigate the hydrodynamic interaction of the AUV and the submarine during the recovery process. Both the AUV and the submarine are considered to be relatively stationary. The results indicate that the submarine has a significant impact on the AUV during the recovery process, with sailing speed and relative positions identified as key influential factors. Due to the influence of the submarine, it can be difficult for the AUV to approach the submarine and be recovered safely. This study provides valuable insights into the hydrodynamic interaction between the AUV and the recovery device, and offers guidance for future submarine recovery operations involving AUVs. By considering the influence of the submarine’s position and motion, as well as other relevant factors, it may be possible to improve the stability, safety, and efficiency of AUV recovery operations.

Underwater dynamic targets often display significant blurriness in their forward-looking sonar imagery, accompanied by sparse feature representation. This phenomenon presents several challenges, including disturbances in the trajectories of underwater targets and alterations in target identification throughout the tracking process, thereby complicating the continuous monitoring of moving targets. This research proposes a new framework for underwater acoustic data interpolation and underwater object tracking. Considering the character of underwater acoustic images, a Swin Transformer is integrated in the architecture of the YOLOv5 network; then, an improved Deep Simple Online and Real-time Tracking method is developed. By enlarging the bounding box output generated by the detector and subsequently integrating it into the tracker, the sensing horizon of the tracker is broadened. This strategy enables the extraction of noise features surrounding the target, thereby augmenting the target’s characteristics and improving the stability of the tracking process. The experimental results demonstrate that the proposed method effectively reduces the frequency of changes in target identification numbers, minimizes the occurrence of trajectory interruptions, and decreases the overall percentage of trajectory interruptions. Additionally, it significantly enhances tracking stability, particularly in scenarios involving intersecting target paths and encounters.