Search Results (226)

This paper addresses the cooperative formation control problem for a heterogeneous unmanned system composed of Unmanned Surface Vehicles (USVs) and Autonomous Underwater Vehicles (AUVs) under coexisting constraints of actuator faults, time-varying communication topology, and communication delay. First, a unified dynamic model is established under the Euler–Lagrange framework. Building on this, a novel distributed adaptive fault-tolerant control (DAFTC) framework is proposed. This framework integrates a Dynamic Event-Triggered Mechanism (DETM) to address communication bandwidth limitations, alongside an adaptive fault-tolerant strategy to enhance system robustness. The novelty lies in the cohesive integration of DETM for communication efficiency and adaptive laws for online fault compensation (both loss of effectiveness and bias), while rigorously handling communication delays via Lyapunov–Krasovskii analysis. It is proven via Lyapunov stability analysis that the proposed control protocol ensures all signals in the closed-loop system remain semi-globally uniformly ultimately bounded, with the formation tracking error converging to an adjustable compact set. Simulation results demonstrate the framework’s effectiveness. Compared to periodic communication (0.1 s interval), the proposed DETM reduces the communication load by over 99.6%. Even when subjected to a 25% effectiveness fault and a 5 Nm bias fault, the root-mean-square (RMS) tracking error is maintained below 0.15 m, validating the system’s high performance and robustness. Full article

Accurately predicting the long-term behavior of complex dynamical systems is a central challenge for safety-critical applications like autonomous navigation. Mechanistic models are often brittle, relying on difficult-to-measure parameters, while standard deep learning models are black boxes that fail to generalize, producing physically inconsistent predictions. Here, we introduce a physics-informed framework that learns the continuous-time dynamics of an Autonomous Underwater Vehicle (AUV) by discovering its underlying energy landscape. We embed the structure of Port-Hamiltonian mechanics into a neural ordinary differential equation (NODE) architecture, learning not to imitate trajectories but rather to identify the system’s Hamiltonian and its constituent physical matrices from observational data. Geometric consistency is enforced by representing rotational dynamics on the SE(3) manifold, preventing numerical error accumulation. Experimental validation reveals a stark performance divide. While a state-of-the-art black-box model matches our accuracy in simple, interpolative maneuvers, its predictions fail catastrophically under complex controls. Quantitatively, our physics-informed model maintained a mean 10 s position error of a mere 3.3 cm, whereas the black-box model’s error diverged to 5.4 m—an over 160-fold performance gap. This work establishes that the key to robust, generalizable models lies not in bigger data or deeper networks but in the principled integration of physical laws, providing a clear path to overcoming the brittleness of black-box models in critical engineering simulations. Full article

Autonomous underwater vehicles (AUVs) play a critical role in underwater remote sensing and monitoring applications. This paper addresses the problem of navigating multiple AUVs to perform sweep video sensing of unknown underwater regions over uneven seafloors, where visibility is limited by the conical field of view (FoV) of the onboard cameras and by occlusions caused by terrain. Coverage is formulated as a feasibility objective of achieving a prescribed target fraction while respecting vehicle kinematics, actuation limits, terrain clearance, and inter-vehicle spacing constraints. We propose an online, occlusion-aware trajectory planning algorithm that integrates frontier-based goal selection, safe viewing depth estimation with clearance constraints, and model predictive control (MPC) for trajectory tracking. The algorithm adaptively guides a team of AUVs to preserve line of sight (LoS) visibility, maintain safe separation, and ensure sufficient clearance while progressively expanding coverage. The approach is validated through MATLAB simulations on randomly generated 2.5D seafloor surfaces with varying elevation characteristics. Benchmarking against classical lawnmower baselines demonstrates the effectiveness of the proposed method in achieving occlusion-aware coverage in scenarios where fixed-pattern strategies are insufficient. Full article

This study presents the design and evaluation of AquaSignalNet, a deep learning-based system for recognizing underwater visual commands to enable the autonomous navigation of a Remotely Operated Vehicle (ROV). The system is built on a ResNet50 V2 architecture and trained with a custom dataset, UVSRD, comprising 33,800 labeled images across 12 gesture classes, including directional commands, speed values, and vertical motion instructions. The model was deployed on a Raspberry Pi 4 integrated with a TIVA C microcontroller for real-time motor control, a PID-based depth control loop, and an MPU9250 sensor for orientation tracking. Experiments were conducted in a controlled pool environment using printed signal cards to define two autonomous trajectories. In the first trajectory, the system achieved 90% success, correctly interpreting a mixed sequence of turns, ascents, and speed changes. In the second, more complex trajectory, involving a rectangular inspection loop and multi-layer navigation, the system achieved 85% success, with failures mainly due to misclassification resulting from lighting variability near the water surface. Unlike conventional approaches that rely on QR codes or artificial markers, AquaSignalNet employs markerless visual cues, offering a flexible alternative for underwater inspection, exploration, and logistical operations. The results demonstrate the system’s viability for real-time gesture-based control. Full article

This paper addresses the trajectory tracking control problem of Autonomous Underwater Vehicles (AUVs). A comprehensive mathematical model is first established based on Newtonian mechanics, incorporating both kinematic and dynamic equations. By reasonably neglecting the minor influence of roll motion, a five-degree-of-freedom (5-DOF) underactuated AUV model is derived. Considering the strong nonlinearities, high coupling, and time-varying hydrodynamic parameters typical of underwater environments, a fuzzy adaptive PID controller is proposed. This controller combines the adaptability of fuzzy logic with the structural simplicity and reliability of PID control, making it well-suited to the demanding requirements of AUV motion control. Extensive simulation experiments are conducted to evaluate the controller’s performance under various operating conditions. The results show that the fuzzy adaptive PID controller significantly outperforms conventional PID and standalone fuzzy logic controllers in terms of convergence speed and oscillation suppression. Furthermore, a theoretical stability analysis is provided to ensure that the proposed control system remains stable under time-varying fuzzy gain scheduling, confirming its effectiveness and potential for practical application in underwater vehicle control. Full article

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

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

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

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

Inspection, maintenance, and repair (IMR) operations on underwater infrastructure remain costly and time-intensive because fully teleoperated remote operated vehicle s(ROVs) lack the range and dexterity necessary for precise cooperative underwater manipulation, and the alternative of using professional divers is ruled out due to the risk involved. This work presents and experimentally validates an autonomous, dual-I-AUV (Intervention–Autonomous Underwater Vehicle) system capable of assembling rigid pipeline segments through coordinated actions in a confined underwater workspace. The first I-AUV is a Girona 500 (4-DoF vehicle motion, pitch and roll stable) fitted with multiple payload cameras and a 6-DoF Reach Bravo 7 arm, giving the vehicle 10 total DoF. The second I-AUV is a BlueROV2 Heavy equipped with a Reach Alpha 5 arm, likewise yielding 10 DoF. The workflow comprises (i) detection and grasping of a coupler pipe section, (ii) synchronized teleoperation to an assembly start pose, and (iii) assembly using a kinematic controller that exploits the Girona 500’s full 10 DoF, while the BlueROV2 holds position and orientation to stabilize the workspace. Validation took place in a 12 m × 8 m × 5 m water tank. Results show that the paired I-AUVs can autonomously perform precision pipeline assembly in real water conditions, representing a significant step toward fully automated subsea construction and maintenance. Full article

Unmanned platforms such as unmanned aerial vehicles, unmanned ground vehicles, and autonomous underwater vehicles often face challenges of data, device, and model heterogeneity when performing collaborative data processing tasks. Existing research does not simultaneously address issues from these three aspects. To address this issue, this study designs an unmanned platform cluster architecture inspired by the cloud-edge-end model. This architecture integrates federated learning for privacy protection, leverages the advantages of distributed model training, and utilizes edge computing’s near-source data processing capabilities. Additionally, this paper proposes a federated edge intelligence method (DSIA-FEI), which comprises two key components. Based on traditional federated learning, a data sharing mechanism is introduced, in which data is extracted from edge-side platforms and placed into a data sharing platform to form a public dataset. At the beginning of model training, random sampling is conducted from the public dataset and distributed to each unmanned platform, so as to mitigate the impact of data distribution heterogeneity and class imbalance during collaborative data processing in unmanned platforms. Moreover, an intelligent model aggregation strategy based on similarity measurement and loss gradient is developed. This strategy maps heterogeneous model parameters to a unified space via hierarchical parameter alignment, and evaluates the similarity between local and global models of edge devices in real-time, along with the loss gradient, to select the optimal model for global aggregation, reducing the influence of device and model heterogeneity on cooperative learning of unmanned platform swarms. This study carried out extensive validation on multiple datasets, and the experimental results showed that the accuracy of the DSIA-FEI proposed in this paper reaches 0.91, 0.91, 0.88, and 0.87 on the FEMNIST, FEAIR, EuroSAT, and RSSCN7 datasets, respectively, which is more than 10% higher than the baseline method. In addition, the number of communication rounds is reduced by more than 40%, which is better than the existing mainstream methods, and the effectiveness of the proposed method is verified. Full article

Autonomous underwater vehicles (AUVs) have obtained extensive application in the exploitation of marine resources. Terrain-aided navigation (TAN), as an accurate and reliable autonomous navigation method, is commonly used for AUV navigation. However, its accuracy degrades significantly in self-similar terrain features or measurement uncertainties. To overcome these challenges, we propose a novel terrain-aided navigation framework integrating an Improved Marine Predators Algorithm with Depth-First Search optimization (DFS-IMPA-TAN). This framework maintains positioning precision in partially self-similar terrains through two synergistic mechanisms: (1) IMPA-driven optimization based on the hunger-inspired adaptive exploitation to determine optimal trajectory transformations, cascaded with Kalman filtering for navigation state correction; (2) a Robust Tree (RT) hypothesis manager that maintains potential trajectory candidates in graph-structured memory, employing Depth-First Search for ambiguity resolution in feature matching. Experimental validation through simulations and in-vehicle testing demonstrates the framework’s distinctive advantages: (1) consistent terrain association in partially self-similar topographies; (2) inherent error resilience against ambiguous feature measurements; and (3) long-term navigation stability. In all experimental groups, the root mean squared error of the framework remained around 60 m. Under adverse conditions, its navigation accuracy improved by over 30% compared to other traditional batch processing TAN methods. Comparative analysis confirms superior performance over conventional methods under challenging conditions, establishing DFS-IMPA-TAN as a robust navigation solution for AUVs in complex underwater environments. Full article

Wireless power transfer systems require not only strong coupling capabilities but also stable output under various misalignment conditions. This paper proposes a hybrid magnetic coupler for autonomous underwater vehicles (AUVs), featuring two identical arc-shaped rectangular transmitting coils and a combination of an arc-shaped rectangular receiving coil and two anti-series connected solenoid coils. The arc-shaped rectangular receiving coil captures the magnetic flux generated by the transmitting coil, which is directed toward the center, while the solenoid coils capture the axial magnetic flux generated by the transmitting coil. The parameters of the proposed magnetic coupler have been optimized to enhance the coupling coefficient and improve the system’s tolerance to misalignments. To verify the feasibility of the proposed magnetic coupler, a 300 W prototype with LCC-S compensation topology is built. Within a 360° rotational misalignment range, the system’s output power maintains around 300 W, with a stable power transmission efficiency of over 92.14%. When axial misalignment of 40 mm occurs, the minimum output power is 282.8 W, and the minimum power transmission efficiency is 91.6%. Full article