In this paper, the formation obstacle avoidance problem of autonomous underwater vehicles (AUVs) under the disturbances of ocean currents is studied. A variable formation reconfiguration and obstacle avoidance control scheme based on affine transform and the improved artificial potential field (AT-IAPF) is designed, which enable AUVs to avoid both static and dynamic obstacles under external interference, and maintain the desired time-varying formation. Because of the robustness and strong effectiveness of the time-varying control of AT and the obstacle avoidance control law of IAPF. The AT-IAPF algorithm improves the multi-AUV systems’ environmental adaptability and obstacle avoidance performance. Using the Lyapunov function’s stability constraint guarantees stability of a multi-AUV system. A series of simulation results based on MATLAB verify that AUVs can effectively avoid obstacles with different formation shapes. Obstacle avoidance experiments on bionic robotic fish demonstrate the proposed method’s feasibility. Note to Practitioners —This paper was motivated by the problem of formation reconfiguration and obstacle avoidance for AUVs. Still, it also applies to unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The existing formation control methods usually solve the problems of formation acquisition and time-invariant maneuvering, and rarely consider the problem of formation obstacle avoidance. This paper presents a new formation obstacle avoidance method using affine transformation (AT) and improved artificial potential field (IAPF) techniques. We use the IAPF method to plan a possible path for the formation in the obstacle environment. At the same time, the appropriate formation shape is selected according to the obstacle information to better adapt to the environment. The preliminary experiments of two bionic robot fish in near-surface positions show that this method is feasible. During the experiment, UWB is used for positioning, and a Zigbee module is used to communicate and transmit data. But it still needs to solve the problem of underwater communication, and it has yet to be tested on multiple bionic robot fish. In future studies, we will conduct multiple actual AUV formation obstacle avoidance experiments or do 3D formation control experiments underwater.

Encircling and capturing foreign unmanned underwater vehicles (UUVs) by multiple defensive UUVs has recently attracted increasing attention. Previous studies using a self-organized map neural network for target allocation suffer from team imbalance and heavy communication burden problems. In addition, the cooperation of defensive UUVs in a team is insufficient. Aiming at these problems, this article forms balanced encircling teams with the estimated travel time acquired in local communication through distributed auction. The quantum particle swarm optimization algorithm is used to optimize the initial encircling circle for fast encirclement, and then dynamically shrink the encircling circle to improve cooperation. Finally, the encirclement control of the defensive UUV is achieved by a fast marching model predictive velocity planner and a proportional–integral controller. In real multi-island waters, this article adopts the proposed algorithm to simulate the encircling of foreign UUVs, which have the same speed as that of defensive UUVs. The simulation results show that the algorithm has advantages in shortening the chasing distance and improving the encircling efficiency. A pool validation experiment is also conducted to verify the effectiveness of the algorithm.

This paper addresses the challenge of cooperatively escorting a moving underwater target, such as a human-occupied vehicle (HOV), using a multi-AUV formation in complex ocean environments. We propose a comprehensive framework, RG-SAPF scheme, that integrates a rigidity graph (RG)-based reconfigurable formation control strategy with a safe artificial potential field (SAPF) motion planning method. The RG-based controller enables the AUVs to form and dynamically reconfigure a 3D escort formation around the target using only relative position information, ensuring the target remains within the formation’s convex hull. Meanwhile, the SAPF algorithm, enhanced with an adaptive Widrow–Hoff rule, enables real-time and collision-free path planning in obstacle-rich environments. Simulation and experimental results demonstrate that the proposed method effectively maintains formation integrity, supports flexible obstacle avoidance, and provides continuous target escort under dynamic conditions, validating its potential for practical underwater escort applications.

Underwater vehicles are widely used in underwater salvage and underwater photography. However, the processing of underwater images has always been a significant challenge. Due to low light conditions in underwater environments, images are often affected by color casts, low visibility and missing edge details. These issues seriously affect the accuracy of underwater object detection by underwater vehicles. To address these problems, an underwater low-light image enhancement method based on image fusion and color balance is proposed in this paper. First, color compensation and white balance algorithms are employed to restore the natural appearance of the images. The texture characteristics of these white-balanced images are then enhanced using unsharp masking (USM) technology. Subsequently, a dual channel dehazing is applied, the image visibility is improved and the blocking artifacts common in traditional dark channel dehazing is avoided. Finally, through multi-scale fusion, the sharpened and dehazed image are combined to obtain the final enhanced image. In quantitative analysis, PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity index), UIQM (Underwater Image Quality Measurement) and UCIQE (Underwater Color Image Quality Evaluation) were 28.62, 0.8753, 0.8831 and 0.5928, respectively. The results show that the images generated by this enhancement technique have higher visibility compared with other methods. It also produces images with more details while preserving edge information.

Underwater navigation remains constrained by technological limitations, driving the exploration of alternative approaches such as polarized light-based systems. This review systematically examines advances in polarized navigation from three perspectives. First, the principles of atmospheric polarization navigation are analyzed, with their operational mechanisms, advantages, and inherent constraints dissected. Second, innovations in bionic polarization multi-sensor fusion positioning are consolidated, highlighting progress beyond conventional heading-direction extraction. Third, emerging underwater polarization navigation techniques are critically evaluated, revealing that current methods predominantly adapt atmospheric frameworks enhanced by advanced filtering to mitigate underwater interference. A comprehensive synthesis of underwater polarization modeling methodologies is provided, categorizing physical, data-driven, and hybrid approaches. Through rigorous analysis of studies, three persistent barriers are identified: (1) inadequate polarization pattern modeling under dynamic cross-media conditions; (2) insufficient robustness against turbidity-induced noise; (3) immature integration of polarization vision with sonar/IMU (Inertial Measurement Unit) sensing. Targeted research directions are proposed, including adaptive deep learning models, multi-spectral polarization sensing, and bio-inspired sensor fusion architectures. These insights establish a roadmap for developing reliable underwater navigation systems that transcend current technological boundaries.

Heterogeneous UAV–UGV formations offer significant potential for autonomous missions; however, achieving stable three-dimensional (3-D) formation control with collision-free navigation in dense obstacle environments remains challenging due to heterogeneous dynamics, unknown external disturbances, and strict real-time computational constraints. This paper proposes a distributed rigid graph-based adaptive safe artificial potential field (RG–ASAPF) framework that tightly integrates formation control and motion planning for heterogeneous multi-UAV–UGV systems. The proposed framework addresses three key challenges: (i) maintaining continuous formation integrity during obstacle avoidance, thereby avoiding the conventional disassembly-recovery process; (ii) enabling vertical coordination between UAV and UGV layers with prescribed inter-layer spacing; and (iii) supporting dynamic formation reconfiguration through time-varying scaling to safely navigate narrow passages. A backstepping-based adaptive formation controller is developed using rigid graph theory to enforce distance consensus under Euler-Lagrange dynamics, explicitly accounting for underactuated UAVs and nonholonomic UGVs. An adaptive safe artificial potential field with Widrow–Hoff-based online repulsion tuning generates smooth, collision-free trajectories while respecting formation footprint constraints. Rigorous Lyapunov analysis establishes exponential convergence of formation errors and global uniform ultimate boundedness (GUUB) of tracking errors in the presence of bounded disturbances. Furthermore, a safety-certified fallback mechanism with corridor-aware rerouting enhances robustness when clearance constraints are violated. Extensive simulations involving sparse and dense obstacle environments, formation scaling maneuvers, dynamic obstacles, and formation maneuvers featuring more agents demonstrate that the proposed framework outperforms model predictive control, particle swarm optimization, and conventional artificial potential field methods in terms of computational efficiency, trajectory smoothness, formation stability, and energy consumption. These results highlight the effectiveness of the proposed approach for heterogeneous multi-domain robotic applications such as disaster response, surveillance, and cooperative logistics.