Search Results (887)

Autonomous Underwater Vehicles (AUVs) equipped with vision systems face unique challenges in real-time environmental perception due to harsh underwater conditions and computational constraints. This paper presents a novel cloud–edge framework for real-time vision–language analysis in underwater drones using the Qwen2.5-VL model. Our system employs a uniform frame sampling mechanism that balances temporal resolution with processing capabilities, achieving near real-time analysis at 1 fps from 23 fps input streams. We construct a comprehensive data flow model encompassing image enhancement, communication latency, cloud-side inference, and semantic result return, which is supported by a theoretical latency framework and sustainable processing rate analysis. Simulation-based experimental results across three challenging underwater scenarios—pipeline inspection, coral reef monitoring, and wreck investigation—demonstrate consistent scene comprehension with end-to-end latencies near 1 s. The Qwen2.5-VL model successfully generates natural language summaries capturing spatial structure, biological content, and habitat conditions, even under turbidity and occlusion. Our results show that vision–language models (VLMs) can provide rich semantic understanding of underwater scenes despite challenging conditions, enabling AUVs to perform complex monitoring tasks with natural language scene descriptions. This work contributes to advancing AI-powered perception systems for the growing autonomous underwater drone market, supporting applications in environmental monitoring, offshore infrastructure inspection, and marine ecosystem assessment. Full article

This study proposes a system identification (SI) technique based on the constrained recursive least squares (CRLS) method to model the dynamics of the P-SUROII. By simplifying the dynamic model in consideration of the inherent characteristics of underwater vehicles and minimizing the number of parameters to be estimated, the proposed approach aims to improve estimation accuracy. In addition, a simplified thruster input model was applied to quantify the actual thruster output and improve the reliability of the input data. To satisfy the persistent excitation (PE) condition during the estimation process, experiments incorporating various motion modes were designed, and free-running and S-shaped maneuvering tests were additionally conducted to validate the model’s generalization capability and prediction performance. The coefficients estimated using the CRLS method, which is robust to noise and bias, were evaluated using quantitative similarity metrics such as root mean squared error (RMSE) and mean absolute error (MAE), confirming their validity. The proposed method effectively captures the actual dynamics of the underwater vehicle and is expected to serve as a key enabling technology for the future development of high-performance control systems and autonomous operation systems. Full article

In this work, a predefined-time formation output constraint control method is proposed for underactuated AUVs with input saturation. First, a coordinate transformation method is utilized to convert the underactuated AUV system into a fully actuated system form. A universal time-varying asymmetric barrier function is constructed to convert the system to an unconstrained form and construct the formation tracking error. Then, a predefined-time formation output constraint control law is designed based on the active disturbance rejection control framework and predefined-time control method, which can achieve the control objective without relying on the precise mathematical model of the system. In addition, to address the input saturation issue, a novel predefined-time auxiliary dynamic system (ADS) is proposed. The proposed method with ADS can ensure that the multi-AUV system with input saturation can complete the formation output constraint tracking control task within a predefined time. Finally, a simulation is designed to verify the effectiveness of the proposed method. Full article

In this paper, a connecting joint capable of underwater autonomous docking and separation is proposed, which can be used for a reconfigurable articulated underwater robot (RAU robot). The structural design, optimization, and experimental validation of the connecting joint are presented in detail. First, the concept of the RAU robot is introduced, along with its different operational modes and the application scenarios. Second, the specific structural design and basic functions of the connecting joint are described. Third, a dynamic model of the docking process between different vehicles is established and simulated by kinematic simulation software. Through discretely sampling the parameter space, the optimal parameter combination is obtained. Finally, a prototype of the connecting joint is fabricated and functional tests are conducted. The impact forces on the docking rods before and after optimization are compared. The results show that the designed connecting joint can fulfill the functional requirements for autonomous docking of the underwater robot, and the maximum impact force is reduced by 27.08% compared to the one before optimization. Full article

Aiming at the cooperative path-planning problem of multiple autonomous underwater vehicles in underwater three-dimensional terrain and dynamic ocean current environments, a hybrid algorithm based on the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) and Dynamic Window (DWA) is proposed. The traditional particle swarm optimization algorithm is prone to falling into local optimization in high-dimensional and complex marine environments. It is difficult to meet multiple constraint conditions, the particle distribution is uneven, and the adaptability to dynamic environments is poor. In response to these problems, a hybrid initialization method based on Chebyshev chaotic mapping, pre-iterative elimination, and boundary particle injection (CPB) is proposed, and the particle swarm optimization algorithm is improved by combining dynamic parameter adjustment and a hybrid perturbation mechanism. On this basis, the Dynamic Window Method (DWA) is introduced as the local path optimization module to achieve real-time avoidance of dynamic obstacles and rolling path correction, thereby constructing a globally and locally coupled hybrid path-planning framework. Finally, cubic spline interpolation is used to smooth the planned path. Considering factors such as path length, smoothness, deflection Angle, and ocean current kinetic energy loss, the dynamic penalty function is adopted to optimize the multi-AUV cooperative collision avoidance and terrain constraints. The simulation results show that the proposed algorithm can effectively plan the dynamic safe path planning of multiple AUVs. By comparing it with other algorithms, the efficiency and security of the proposed algorithm are verified, meeting the navigation requirements in the current environment. Experiments show that the IMOPSO–DWA hybrid algorithm reduces the path length by 15.5%, the threat penalty by 8.3%, and the total fitness by 3.2% compared with the traditional PSO algorithm. Full article

AUVs offer the potential for in situ MP detection at constant, pre-set depths in marine environments. By carrying onboard MP detectors, AUVs can serve as alternatives to traditional methods of sample collection, processing, and analysis, while also addressing the inefficiencies and complexities associated with conventional detection procedures. This study conducts a comprehensive review of existing and potential MP detection methods that can be integrated with AUVs for in situ detection. In particular, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review analyzes selected studies on MP detection using AUVs. It finds that real-time, in situ MP detection via AUVs or multi-AUV systems remains underdeveloped. Key challenges include deep-sea communication, sensor integration, and underwater durability. The review highlights the current advances, research gaps, and future directions for AUV-based MP detection technologies. Full article

Autonomous Underwater Vehicles (AUVs) play a critical role in ocean exploration. However, due to the inherent limitations of most sensors in underwater environments, achieving accurate navigation and localization in complex underwater scenarios remains a significant challenge. While vision-based Simultaneous Localization and Mapping (SLAM) provides a cost-effective alternative for AUV navigation, existing methods are primarily designed for terrestrial applications and struggle to address underwater-specific issues, such as poor illumination, dynamic interference, and sparse features. To tackle these challenges, we propose RAEM-SLAM, a robust adaptive end-to-end monocular SLAM framework for AUVs in underwater environments. Specifically, we propose a Physics-guided Underwater Adaptive Augmentation (PUAA) method that dynamically converts terrestrial scene datasets into physically realistic pseudo-underwater images for the augmentation training of RAEM-SLAM, improving the system’s generalization and adaptability in complex underwater scenes. We also introduce a Residual Semantic–Spatial Attention Module (RSSA), which utilizes a dual-branch attention mechanism to effectively fuse semantic and spatial information. This design enables adaptive enhancement of key feature regions and suppression of noise interference, resulting in more discriminative feature representations. Furthermore, we incorporate a Local–Global Perception Block (LGP), which integrates multi-scale local details with global contextual dependencies to significantly improve AUV pose estimation accuracy in dynamic underwater scenes. Experimental results on real-world underwater datasets demonstrate that RAEM-SLAM outperforms state-of-the-art SLAM approaches in enabling precise and robust navigation for AUVs. Full article

Recent advances in vision–language models (VLMs) have transformed the field of robotics. Researchers are combining the reasoning capabilities of large language models (LLMs) with the visual information processing capabilities of VLMs in various domains. However, most efforts have focused on terrestrial robots and are limited in their applicability to volatile environments such as ocean surfaces and underwater environments, where real-time judgment is required. We propose a system integrating the cognition, decision making, path planning, and control of autonomous marine surface vehicles in the ROS2–Gazebo simulation environment using a multimodal vision–LLM system with zero-shot prompting for real-time adaptability. In 30 experiments, adding the path plan mode feature increased the success rate from 23% to 73%. The average distance increased from 39 m to 45 m, and the time required to complete the task increased from 483 s to 672 s. These results demonstrate the trade-off between improved reliability and reduced efficiency. Experiments were conducted to verify the effectiveness of the proposed system and evaluate its performance with and without adding a path-planning step. The final algorithm with the path-planning sub-process yields a higher success rate, and better average path length and time. We achieve real-time environmental adaptability and performance improvement through prompt engineering and the addition of a path-planning sub-process in a limited structure, where the LLM state is initialized with every application programming interface call (zero-shot prompting). Additionally, the developed system is independent of the vision–LLM archetype, making it scalable and adaptable to future models. Full article

This paper investigates the collaborative obstacle avoidance control of multiple autonomous underwater vehicles (AUVs) in underwater environments with communication delays and intermittent connectivity. Firstly, a novel time-delay piecewise differential inequality incorporating an exponential decay term is established, which systematically integrates state delay, intermittent control strategies, and event-triggered mechanisms. Secondly, the traditional request-response mechanism is replaced by a broadcast communication protocol, significantly reducing the requirement for continuous inter-AUV communication and enhancing overall communication efficiency. Thirdly, the obstacle avoidance problem of multiple AUVs is addressed through the implementation of a nominal-optimized controller. Obstacle avoidance safety conditions between unmanned vehicles and obstacles are derived by employing a zeroing control barrier function (CBF). Based on these safety conditions and input constraints, a quadratic programming (QP) framework is formulated to dynamically optimize control signals in real time, thereby ensuring the safe operation of the multi-AUV system. Finally, the efficacy of the proposed control method is validated through comprehensive simulation results, demonstrating its robustness and practical performance. Full article

In this study, a multi-objective shape optimization framework was established for photovoltaic-powered underwater vehicles (PUVs) to systematically investigate multidisciplinary coupled design methodologies. Specifically, a global sensitivity analysis was conducted to identify four critical design parameters with 24 h energy consumption and cabin volume serving as dual optimization objectives. An integrated automated optimization workflow was constructed by incorporating parametric modeling, computational fluid dynamics (CFD) simulations, and dynamic surrogate models. Additionally, a new phased hybrid adaptive lower confidence bound (PHA-LCB) infill criterion was designed under the consideration of error-driven mechanisms, improvement feedback loops, and iterative attenuation factors to develop high-precision dynamic surrogate models. Coupled with the NSGA-II multi-objective genetic algorithm, this framework generated Pareto-optimal front solutions possessing significant engineering value. Furthermore, an optimal design configuration was ultimately determined through multi-criteria decision analysis. Compared to the initial form, it generates an additional 1148.12 Wh of electrical energy within 24 h, with an 22.36% increase in sailing range and a 2.77% improvement in cabin volume capacity. The proposed closed-loop “modeling–simulation–optimization” framework realized multi-objective optimization of PUV shape parameters, providing methodological paradigms and technical foundations for the engineering design of next-generation autonomous underwater vehicles. Full article

Precise underwater object detectors can provide Autonomous Underwater Vehicles (AUVs) with good situational awareness in underwater environments, supporting a wide range of unmanned exploration missions. However, the quality of optical imaging is often insufficient to support high detector accuracy due to poor lighting and the complexity of underwater environments. Therefore, this paper develops an efficient and precise object detector that maintains high recognition accuracy on degraded underwater images. We design a Cross Spatial Global Perceptual Attention (CSGPA) mechanism to achieve accurate recognition of target and background information. We then construct an Efficient Multi-Scale Weighting Feature Pyramid Network (EMWFPN) to eliminate computational redundancy and increase the model’s feature-representation ability. The proposed Occlusion-Robust Wavelet Network (ORWNet) enables the model to handle fine-grained frequency-domain information, enhancing robustness to occluded objects. Finally, EMASlideloss is introduced to alleviate sample-distribution imbalance in underwater datasets. Our architecture achieves 81.8% and 83.8% mAP on the DUO and UW6C datasets, respectively, with only 7.2 GFLOPs, outperforming baseline models and balancing detection precision with computational efficiency. Full article

This paper investigates weak fault feature extraction in AUV thrusters under multi-input signal conditions. Conventional methods often rely on insufficient input signals, leading to a non-monotonic mapping between fault features and fault severity. This, in turn, makes accurate fault severity identification infeasible. To overcome this limitation, this paper increases the number of input signals by utilizing all available measurable signals. To address the problems arising from the expanded signal set, a signal denoising method that combines Feature Mode Decomposition and wavelet denoising is proposed. Furthermore, a signal enhancement technique that integrates energy operators and the Modified Bayes method. Additionally, distinct technical approaches for noise reduction and enhancement are specifically designed for different input signals. Unlike conventional methods that extract features directly from raw input signals, for fault feature extraction and fusion, this study transforms the signals into the time, frequency, and time–frequency domains, extracting diverse fault features across these domains. A sensitivity factor selection method is then employed to identify the sensitive features. These selected features are subsequently fused using Dempster–Shafer evidence theory to construct the final fault feature. Finally, fault severity identification is carried out using the classical grey relational analysis. Pool experiments using the “Beaver II” AUV prototype validate the effectiveness of the proposed method. Full article

The rapid advancement of underwater wireless communication technologies is critical to unlocking the full potential of marine resource exploration and environmental monitoring. This paper reviews recent progress in three primary modalities: underwater acoustic communication, radio frequency (RF) communication, and underwater optical wireless communication (UWOC), each designed to address specific challenges posed by complex underwater environments. Acoustic communication, while effective for long-range transmission, is constrained by ambient noise and high latency; recent innovations in noise reduction and data rate enhancement have notably improved its reliability. RF communication offers high-speed, short-range capabilities in shallow waters, but still faces challenges in hardware miniaturization and accurate channel modeling. UWOC has emerged as a promising solution, enabling multi-gigabit data rates over medium distances through advanced modulation techniques and turbulence mitigation. Additionally, bio-inspired approaches such as electric field communication provide energy-efficient and robust alternatives under turbid conditions. This paper further examines the practical integration of these technologies in underwater platforms, including autonomous underwater vehicles (AUVs), highlighting trade-offs between energy efficiency, system complexity, and communication performance. By synthesizing recent advancements, this review outlines the advantages and limitations of current underwater communication methods and their real-world applications, offering insights to guide the future development of underwater communication systems for robotic and vehicular platforms. Full article

Wireless power transfer (WPT) systems for autonomous underwater vehicles (AUVs) are gaining traction in marine exploration due to their operational convenience, safety, and flexibility. Nevertheless, disturbances from ocean currents and marine organisms frequently induce rotational, axial, and air-gap misalignments, significantly degrading the output power stability. To mitigate this issue, this paper proposes a novel reconfigurable WPT system utilizing coaxial antipodal dual DD (CAD-DD) coils, which strategically switches between a detuned S-LCC topology and a detuned S-S topology at a fixed operating frequency. By characterizing the output power versus the coupling coefficient (P-k) profiles under both reconfiguration modes, a parameter design methodology is developed to ensure stable power delivery across wide coupling variations. Experimental validation using a 1.2 kW AUV charging prototype demonstrates remarkable tolerance to misalignment: ±30° rotation, ±120 mm axial displacement, and 20–50 mm air-gap variation. Within this range, the output power fluctuation is confined to within 5%, while the system efficiency exceeds 85% consistently, peaking at 91.56%. Full article

The deformation of the array shape during the turning process of an autonomous underwater vehicle (AUV)-towed line array sonar can significantly degrade its remote sensing performance. In this paper, a method for circular arc array modeling and dynamic deformation estimation is proposed. By treating the array shape as a hyperparameter, an adaptive central angle (shape) marginal likelihood maximization (ASMLM) algorithm is derived to jointly estimate the array shape and the directions of arrival (DOAs) of sources. The high-resolution ASMLM algorithm is used to improve the DOA estimation performance, effectively suppress left–right ambiguity and significantly reduce computational complexity, making it suitable for AUV platforms with limited computational resources. Experimental results from sea trials in the South China Sea are used to validate the superior performance of the proposed method over existing methods. Full article