Effective communication between multiple autonomous underwater vehicles (AUVs) is necessary for formation control. As the most reliable underwater communication method, acoustic communication still has many constraints compared with radio communication, which affects the effectiveness of formation control. Therefore, this paper proposes a formation control scheme for multiple AUVs under communication delay, packet discreteness and dropout. Firstly, the communication delay is estimated based on the kernel density estimation method. To solve the problem of packet discreteness and dropout, the curve fitting method is used to predict the states of the AUV. Secondly, a follower controller is designed based on the leader–follower approach using input–output feedback linearization, which is proven to be stable with Lyapunov stability theory. Then, some simulation results are presented to demonstrate the stability and accuracy of the formation control in different communication environments. Finally, the field tests on the lake show that the scheme introduced in this paper is valid and practical.

This paper proposes a centralized MTT method based on a state-of-the-art multi-sensor labeled multi-Bernoulli (LMB) filter in underwater multi-static networks with autonomous underwater vehicles (AUVs). The LMB filter can accurately extract the number of targets and trajectories from measurements affected by noise, missed detections, false alarms and port–starboard ambiguity. However, its complexity increases as the number of sensors increases. In addition, due to the time-varying underwater environment, AUV detection probabilities are time-varying, and their mismatches often lead to poor MTT performance. Consequently, we detail a robust multi-sensor LMB filter that estimates detection probabilities and multi-target states simultaneously in real time. Moreover, we derive an effective approximate form of the multi-sensor LMB filter using Kullback–Leibler divergence and develop an efficient belief propagation (BP) implementation of the multi-sensor LMB filter. Our method scales linearly with the number of AUVs, providing good scalability and low computational complexity. The proposed method demonstrates superior performance in underwater multi-AUV network MTT simulations.

Accurate biological detection is crucial for autonomous navigation of underwater robots, yet severely challenged by optical degradation and scale variation in marine environments. While image enhancement and domain adaptation methods offer some mitigation, they often operate as disjointed preprocessing steps, potentially introducing artifacts and compromising downstream detection performance. Furthermore, existing architectures struggle to balance accuracy, computational efficiency, and robustness across the extreme scale variability of marine organisms in challenging underwater conditions. To overcome these limitations, we propose UW-YOLO-Bio, a novel framework built upon the YOLOv8 architecture. Our approach integrates three dedicated modules: (1) The Global Context 3D Perception Module (GCPM), which captures long-range dependencies to mitigate occlusion and noise without the quadratic cost of self-attention; (2) The Channel-Aggregation Efficient Downsampling Block (CAEDB), which preserves critical information from low-contrast targets during spatial reduction; (3) The Regional Context Feature Pyramid Network (RCFPN), which optimizes multi-scale fusion with contextual awareness for small marine organisms. Extensive evaluations on DUO, RUOD, and URPC datasets demonstrate state-of-the-art performance, achieving an average improvement in mAP50 of up to 2.0% across benchmarks while simultaneously reducing model parameters by 8.3%. Notably, it maintains a real-time inference speed of 61.8 FPS, rendering it highly suitable for deployment on resource-constrained autonomous underwater vehicles (AUVs).

This paper addresses the aperture limitation problem faced by array-equipped underwater gliders (UGs) in direction-of-arrival (DOA) estimation. A passive synthetic aperture (PSA) method for DOA estimation using a single hydrophone mounted on a UG is proposed. This method uses the motion of the UG to synthesize a linear array whose elements are positioned to acquire the target signal, thereby increasing the array aperture. The dead-reckoning method is used to determine the underwater trajectory of the UG, and the UG’s trajectory was corrected by the UG motion parameters, from which the array shape was adjusted accordingly and the position of the array elements was corrected. Additionally, array distortion caused by movement offsets due to ocean currents underwent linearization, reducing computational complexity. To validate the proposed method, a sea trial was conducted in the South China Sea using the Haiyi 1000 UG equipped with a hydrophone, and its effectiveness was demonstrated through the processing of the collected data. The performance of DOA estimation prior to and following UG trajectory correction was compared to evaluate the impact of ocean currents on target DOA estimation accuracy.

In this paper, an adaptive control scheme with disturbance estimation is proposed for the station keeping and trajectory tracking of an underwater vehicle-manipulator system (UVMS) in a task space. The control of UVMSs encounters time-varying and lumped disturbances introduced by the motion of the manipulator and currents. The proposed control scheme includes an observer to identify disturbances without introducing additional sensors. Using state-of-the-art UVMS control technologies, it alleviates the restriction of the feedforward control applied to the UVMS. In addition, the proposed control scheme is not designed based on the worst case. In other words, the robustness of the controller is not achieved at the price of performance, and the performance of the controller can be recovered in the absence of disturbance. Meanwhile, the controller can also realize quick responses to attenuating disturbance and closed-loop stability when encountering an unexpected disturbance. The effectiveness of the proposed control scheme was demonstrated by a series of spatial trajectory tracking experiments under various disturbances. A limitation of this study is that the proposed control scheme is specialized for the underwater manipulation of UVMSs.