Accurately modeling the system dynamics of autonomous underwater vehicles (AUVs) is imperative to facilitating the implementation of intelligent control. In this research, we introduce a physics-informed neural network (PINN) method to model the dynamics of AUVs by integrating dynamical equations with deep neural networks. This integration leverages the nonlinear expressive power of deep neural networks, alongside the robust foundation of physical prior knowledge, resulting in an AUV model proficient in long-term motion forecasting. The experimental results indicate that this method is capable of effectively extracting AUV system dynamics from datasets, exhibiting strong generalization capabilities and achieving robust long-term motion prediction. Furthermore, a model predictive control method is proposed, using the learned PINN as the predictive model to accurately track the closed-loop trajectory. This research offers novel perspectives on the dynamics modeling of AUVs and has the potential to be applied in other relevant research endeavors.

In challenging tasks such as large-scale resource detection, deep-sea exploration, prolonged cruising, extensive topographical mapping, and operations within intricate current regions, AUV swarm technologies play a pivotal role. A core technical challenge within this realm is the precise determination of relative positions among AUVs within the cluster. Given the complexity of underwater environments, this study introduces an integrated and high-precision underwater cluster positioning method, incorporating advanced image restoration algorithms and enhanced underwater visual markers. Utilizing the Hydro-Optical Image Restoration Model (HOIRM) developed in this research, image clarity in underwater settings is significantly improved, thereby expanding the attenuation coefficient range for marker identification and enhancing it by at least 20%. Compared to other markers, the novel underwater visual marker designed in this research elevates positioning accuracy by 1.5 times under optimal water conditions and twice as much under adverse conditions. By synthesizing the aforementioned techniques, this study has successfully developed a comprehensive underwater visual positioning algorithm, amalgamating image restoration, feature detection, geometric code value analysis, and pose resolution. The efficacy of the method has been validated through real-world underwater swarm experiments, providing crucial navigational and operational assurance for AUV clusters.

Water–air cross-domain vehicles (CDVs) are capable of both flight and underwater navigation, showing broad prospects in marine science, such as underwater observation, disaster response, and rescue operations. It is crucial to investigate the dynamic performance of CDVs hovering above water surfaces to enhance safety and stability. In this study, the performance of a CDV’s ducted propeller hovering at various heights above a water surface was analyzed via computational fluid dynamic (CFD) simulations using the lattice Boltzmann method (LBM) and thrust tests. The results indicate that the air–water mixture formed by the wake of the propeller impacting the water surface is sucked in by the duct, causing the propeller to enter an unstable vortex ring state. At the same rotation speed in the air, the thrust of the propeller system decreases and the required power increases. With an increase in the height of the propeller above the water surface, the thrust and power return to normal. Furthermore, a numerical model was proposed to express the correlation among thrust, propeller rotation speed, and distance from the water surface. This study establishes a foundation for the dynamic modeling of CDVs and can be utilized by other related studies.

Due to the limited and fixed field of view of the onboard camera, the guiding beacons gradually drift out of sight as the AUV approaches the docking station, resulting in unreliable positioning and intermittent data. This paper proposes an underwater autonomous docking visual localization method based on a cage-type dual-layer guiding light array. To address the gradual loss of beacon visibility during AUV approach, a rationally designed localization scheme employing a cage-type, dual-layer guiding light array is presented. A dual-layer light array localization algorithm is introduced to accommodate varying beacon appearances at different docking stages by dynamically distinguishing between front and rear guiding light arrays. Following layer-wise separation of guiding lights, a robust tag-matching framework is constructed for each layer. Particle swarm optimization (PSO) is employed for high-precision initial tag matching, and a filtering strategy based on distance and angular ratio consistency eliminates unreliable matches. Under extreme conditions with three missing lights or two spurious beacons, the method achieves 90.3% and 99.6% matching success rates, respectively. After applying filtering strategy, error correction using backtracking extended Kalman filter (BTEKF) brings matching success rate to 99.9%. Simulations and underwater experiments demonstrate stable and robust tag matching across all docking phases, with average detection time of 0.112 s, even when handling dual-layer arrays. The proposed method achieves continuous visual guidance-based docking for autonomous AUV recovery.

Sound source localization provides an absorbing capability for unmanned aerial vehicles (UAVs) in scenarios such as search and rescue operations. The shape fusion between the sound array and UAVs forms a special conformal property that is drawing more and more attention. However, the inevitable shadow effect caused by shape fusion seriously degrades the degrees of freedom (DOF) of the array. In this paper, a signal reconstruction-based direction of arrival (DOA) estimation method is proposed to address this limitation. Firstly, we establish a restricted signal model for the conformal cylindrical array (CCA), and then based on frequency domain energy detection, the elements are divided into receiving restricted elements and receiving normal elements. Secondly, according to the position vector of receiving restricted elements, the approximate range of the DOA is roughly estimated to reduce the complexity. Meanwhile, the signals of receiving restricted elements are reconstructed on the basis of receiving normal elements to eliminate the shadow effect and increase the DOF. Lastly, in the estimated approximate range of the DOA, the precise DOA is estimated by peak search. We also derive the 2-D Cramer-Rao lower bound (CRLB) for the CCA. Simulations show that the proposed SR-MUSIC-RS method can achieve satisfactory performance with lower complexity, and the RMSE is close to that of general signal model with normal elements.

This paper proposes a method for passive detection of autonomous underwater vehicle (AUV) wakes using a cilium-inspired wake sensor (CIWS), which can be used for the detection and tracking of AUVs. First, the characteristics of the CIWS and its working principle for detecting underwater flow fields are introduced. Then, a flow velocity sensor is used to measure the flow velocities of the “TS MINI” AUV’s wake at different positions, and a velocity field model of the “TS MINI” AUV’s wake is established. Finally, the wake field of the “TS MINI” AUV was measured at various positions using the CIWS. By analyzing the data, the characteristic frequency of the AUV’s propeller is identified, which is correlated with the AUV’s rotation speed and the number of blades. Through further analysis, a mapping model is established between the spectral amplitude of the characteristic frequency at different positions and the corresponding wake velocity. By substituting this mapping model into the AUV’s wake velocity field model, the possible position range of the sensor relative to the AUV propeller can be estimated. The research provides a technical foundation for underwater detection and tracking missions based on wake detection.