Search Results (38)

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

Camera-centric perception has matured into a cornerstone of modern autonomy, from self-driving cars and factory cobots to underwater and planetary exploration. This review synthesizes more than a decade of progress in vision-based robotic navigation through an engineering lens, charting the full pipeline from sensing to deployment. We first examine the expanding sensor palette—monocular and multi-camera rigs, stereo and RGB-D devices, LiDAR–camera hybrids, event cameras, and infrared systems—highlighting the complementary operating envelopes and the rise of learning-based depth inference. The advances in visual localization and mapping are then analyzed, contrasting sparse and dense SLAM approaches, as well as monocular, stereo, and visual–inertial formulations. Additional topics include loop closure, semantic mapping, and LiDAR–visual–inertial fusion, which enables drift-free operation in dynamic environments. Building on these foundations, we review the navigation and control strategies, spanning classical planning, reinforcement and imitation learning, hybrid topological–metric memories, and emerging visual language guidance. Application case studies—autonomous driving, industrial manipulation, autonomous underwater vehicles, planetary rovers, aerial drones, and humanoids—demonstrate how tailored sensor suites and algorithms meet domain-specific constraints. Finally, the future research trajectories are distilled: generative AI for synthetic training data and scene completion; high-density 3D perception with solid-state LiDAR and neural implicit representations; event-based vision for ultra-fast control; and human-centric autonomy in next-generation robots. By providing a unified taxonomy, a comparative analysis, and engineering guidelines, this review aims to inform researchers and practitioners designing robust, scalable, vision-driven robotic systems. Full article

Autonomous Underwater Vehicles (AUVs) operating in dynamic, constrained underwater environments demand sophisticated navigation and detection fusion capabilities that traditional methods often fail to provide. This paper introduces a novel hybrid framework that synergistically fuses a Multithreaded Energy-Optimized Batch Informed Trees (MEO-BIT*) algorithm with Deep Q-Networks (DQN) to achieve robust AUV autonomy. The MEO-BIT* component delivers efficient global path planning through (1) a multithreaded batch sampling mechanism for rapid state-space exploration, (2) heuristic-driven search accelerated by KD-tree spatial indexing for optimized path discovery, and (3) an energy-aware cost function balancing path length and steering effort for enhanced endurance. Critically, the DQN component facilitates dynamic obstacle detection and adaptive local navigation, enabling the AUV to adjust its trajectory intelligently in real time. This integrated approach leverages the strengths of both algorithms. The global path intelligence of MEO-BIT* is dynamically informed and refined by the DQN’s learned perception. This allows the DQN to make effective decisions to avoid moving obstacles. Experimental validation in a simulated Achao waterway (Chile) demonstrates the MEO-BIT* + DQN system’s superiority, achieving a 46% reduction in collision rates (directly reflecting improved detection and avoidance fusion), a 15.7% improvement in path smoothness, and a 78.9% faster execution time compared to conventional RRT* and BIT* methods. This work presents a robust solution that effectively fuses two key components: the computational efficiency of MEO-BIT* and the adaptive capabilities of DQN. This fusion significantly advances the integration of navigation with dynamic obstacle detection. Ultimately, it enhances AUV operational performance and autonomy in complex maritime scenarios. Full article

Underwater wireless communications face significant challenges due to high attenuation, turbulence, and water turbidity. Traditional methods like acoustic and radio frequency (RF) communication suffer from low data rates (<100 kbps), high latency (>1 s), and limited transmission distances (<10 km).Visible Light Communication (VLC) emerges as a promising alternative, offering high-speed data transmission (up to 5 Gbps), low latency (<1 ms), and immunity to electromagnetic interference. This paper provides an in-depth review of underwater VLC, covering fundamental principles, environmental factors (scattering, absorption), and dynamic water properties. We analyze modulation techniques, including adaptive and hybrid schemes (QAM-OFDM achieving 4.92 Gbps over 1.5 m), and demonstrate their superiority over conventional methods. Practical applications—underwater exploration, autonomous vehicle control, and environmental monitoring—are discussed alongside security challenges. Key findings highlight UVLC’s ability to overcome traditional limitations, with experimental results showing 500 Mbps over 150 m using PAM4 modulation. Future research directions include integrating quantum communication and Reconfigurable Intelligent Surfaces (RISs) to further enhance performance, with simulations projecting 40% improved spectral efficiency in turbulent conditions. Full article

The Internet of Underwater Things (IoUT) holds significant promise for developing a smart ocean. In recent years, there has been swift progress in data collection methods using autonomous underwater vehicles (AUVs) within underwater acoustic sensor networks (UASNs). One of the key challenges in the IoUT is improving both the energy consumption (EC) of underwater vehicles and the value of information (VoI) necessary for completing missions while gathering sensing data. In this paper, a hybrid optimization technique is proposed based on boundary tour modified ant colony optimization (BTMACO). The proposed optimization algorithm was developed to solve the challenging problem of determining the optimal path of an AUV visiting all sensor nodes with minimum energy consumption. The optimization algorithm specifies the best order in which to visit all the sensor nodes, while it also works to adjust the AUV’s information-gathering locations according to the permissible data transmission range. Compared with the related works in the literature, the proposed method showed better performance, and it can find the best route through which to collect sensor information with minimum power consumption and a 6.9% better VoI. Full article

To address the issue of increasing navigation errors in low-cost autonomous underwater vehicles (AUVs) operating without assisted positioning underwater, this paper proposes a Virtual GPS Model (VGPSM) based on deep sequence learning. This model is integrated with an Extended Kalman Filter (EKF) to provide a high-precision navigation solution for AUVs. The VGPSM leverages the time-series characteristics of data from sensors such as the Attitude and Heading Reference System (AHRS) and the Doppler Velocity Log (DVL) while the AUV is on the surface. It learns the relationship between these sensor data and GPS data by utilizing a hybrid model of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM), which are well-suited for processing and predicting time-series data. This approach constructs a virtual GPS model that generates virtual GPS displacements updated at the same frequency as the real GPS data. When the AUV navigates underwater, the virtual GPS displacements generated using the VGPSM in real-time are used as measurements to assist the EKF in state estimation, thereby enhancing the accuracy and robustness of underwater navigation. The effectiveness of the proposed method is validated through a series of experiments under various conditions. The experimental results demonstrate that the proposed method significantly reduces cumulative errors, with navigation accuracy improvements ranging from 29.2% to 69.56% compared to the standard EKF, indicating strong adaptability and robustness. Full article

When autonomous underwater vehicles (AUVs) perform underwater tasks, the absence of GPS position assistance can lead to a decrease in the accuracy of traditional navigation systems, such as the extended Kalman filter (EKF), due to the accumulation of errors. To enhance the navigation accuracy of AUVs in the absence of position assistance, this paper proposes an innovative navigation method that integrates a position correction model and a velocity model. Specifically, a velocity model is developed using a dynamic model and the Optimal Pruning Extreme Learning Machine (OP-ELM) method. This velocity model is trained online to provide velocity outputs during the intervals when the Doppler Velocity Log (DVL) is not updating, ensuring more consistent and reliable velocity estimation. Additionally, a position correction model (PCM) is constructed, based on a hybrid gated recurrent neural network (HGRNN). This model is specifically designed to correct the AUV’s navigation position when GPS data are unavailable underwater. The HGRNN utilizes historical navigation data and patterns learned during training to predict and adjust the AUV’s estimated position, thereby reducing the drift caused by the lack of real-time position updates. Experimental results demonstrate that the proposed VM-PCM-EKF algorithm can significantly improve the positioning accuracy of the navigation system, with a maximum accuracy improvement of 87.2% compared to conventional EKF algorithms. This method not only improves the reliability and accuracy of AUV missions but also opens up new possibilities for more complex and extended underwater operations. Full article

This article introduces a new design pattern that provides an optimal solution for the systematic development of AUV controllers. In this study, a hybrid control model is designed on the basis of the OOSEM (Object-Oriented Systems Engineering Method), combined with MDA (Model-Driven Architecture) concepts, real-time UML/SysML (Unified Modeling Language/Systems Modeling Language), and the UKF (unscented Kalman filter) algorithm. This hybrid model enables the implementation of the control elements of autonomous underwater vehicles (AUVs), which are considered HDSs (hybrid dynamic systems), and it can be adapted for reuse for most standard AUV platforms. To achieve this goal, a dynamic AUV model is integrated with the specializations of the OOSEM/MDA, in which an analysis model is clarified via a use-case model definition and then combined with HA (hybrid automata) to precisely define the control requirements. Next, the designed model is tailored via real-time UML/SysML to obtain the core control blocks, which describe the behaviors and structures of the control parts in detail. This design model is then transformed into an implementation model with the assistance of round-trip engineering to conveniently realize a controller for AUVs. Based on this new model, a feasible AUV controller for low-cost, turtle-shaped AUVs is implemented, and it is utilized to perform planar trajectory tracking. Full article

As a key method of ocean exploration, the positioning accuracy of autonomous underwater vehicles (AUVs) directly influences the success of subsequent missions. This study aims to develop a novel method to address the low accuracy in visual simultaneous localization and mapping (SLAM) within underwater environments, enhancing its application in the navigation and localization of AUVs. We propose an underwater multisensor fusion SLAM system based on image enhancement. First, we integrate hybrid attention mechanisms with generative adversarial networks to address the blurring and low contrast in underwater images, thereby increasing the number of feature points. Next, we develop an underwater feature-matching algorithm based on a local matcher to solve the feature tracking problem caused by grayscale changes in the enhanced image. Finally, we tightly couple the Doppler velocity log (DVL) with the SLAM algorithm to better adapt to underwater environments. The experiments demonstrate that, compared to other algorithms, our proposed method achieves reductions in both mean absolute error (MAE) and standard deviation (STD) by up to 68.18% and 44.44%, respectively, when all algorithms are operating normally. Additionally, the MAE and STD of our algorithm are 0.84 m and 0.48 m, respectively, when other algorithms fail to operate properly. Full article

Accurate underwater target detection is crucial for the operation of autonomous underwater vehicles (AUVs), enhancing their environmental awareness and target search and rescue capabilities. Current deep learning-based detection models are typically large, requiring substantial storage and computational resources. However, the limited space on AUVs poses significant challenges for deploying these models on the embedded processors. Therefore, research on model compression is of great practical importance, aiming to reduce model parameters and computational load without significantly sacrificing accuracy. To address the challenge of deploying large detection models, this paper introduces an automated pruning method based on dependency graphs and successfully implements efficient pruning on the YOLOv7 model. To mitigate the accuracy degradation caused by extensive pruning, we design a hybrid distillation method that combines output-based and feature-based distillation techniques, thereby improving the detection accuracy of the pruned model. Finally, we deploy the compressed model on an embedded processor within an AUV to evaluate its performance. Multiple experiments confirm the effectiveness of our proposed method in practical applications. Full article

The expansion of underwater scenarios and missions highlights the crucial need for autonomous underwater vehicles (AUVs) to make informed decisions. Therefore, developing an efficient decision-making framework is vital to enhance productivity in executing complex tasks within tight time constraints. This paper delves into task planning and reconstruction within the AUV control decision system to enable intelligent completion of intricate underwater tasks. Behavior trees (BTs) offer a structured approach to organizing the switching structure of a hybrid dynamical system (HDS), originally introduced in the computer game programming community. In this research, an intelligent search algorithm, MCTS-QPSO (Monte Carlo tree search and quantum particle swarm optimization), is proposed to bolster the AUV’s capacity in planning complex task decision control systems. This algorithm tackles the issue of the time-consuming manual design of control systems by effectively integrating BTs. By assessing a predefined set of subtasks and actions in tandem with the complex task scenario, a reward function is formulated for MCTS to pinpoint the optimal subtree set. The QPSO algorithm is then leveraged for subtree integration, treating it as an optimal path search problem from the root node to the leaf node. This process optimizes the search subtree, thereby enhancing the robustness and security of the control architecture. To expedite search speed and algorithm convergence, this paper recommends reducing the search space by pre-grouping conditions and states within the behavior tree. The efficacy and superiority of the proposed algorithm are validated through security and timeliness evaluations of the BT, along with comparisons with other algorithms for automatic AUV decision control behavior tree design. Ultimately, the effectiveness and superiority of the proposed algorithm are corroborated through simulations on a multi-AUV complex task platform, showcasing its practical applicability and efficiency in real-world underwater scenarios. Full article

This article introduces a control architecture designed for the development of Hybrid Remotely Operated Underwater Vehicles. The term ”Hybrid” characterizes Remotely Operated systems capable of autonomously executing specific operations. The presented architecture maintains teleoperation capabilities while enabling two fully autonomous applications. The approach emphasizes the implementation of reactive navigation by exclusively utilizing data from a Mechanical Scanned Imaging Sonar for control decisions. This mandates the control system to solely react to data derived from the vehicle’s environment, without considering other positioning information or state estimation. The study involves transforming a small-scale commercial Remotely Operated Underwater Vehicle into a hybrid system without structural modifications, and details the development of an intermediate Operational Control Layer responsible for sensor data processing and task execution control. Two practical applications, inspired by tasks common in natural or open-water aquaculture farms, are explored: one for conducting transects, facilitating monitoring and maintenance operations, and another for navigating toward an object for inspection purposes. Experimental results validate the feasibility and effectiveness of the authors’ hypotheses. This approach expands the potential applications of underwater vehicles and facilitates the development of Hybrid Remotely Operated Underwater Vehicles, enabling the execution of autonomous reactive tasks. Full article

Robust control systems are a necessity for autonomous underwater vehicle (AUV) systems due to the challenges they face during operation. Many AUV control-design methods have been developed for different actuator configurations, with robustness against model parameter uncertainties, environmental disturbances, and system faults. Actuator faults can reduce the physical capabilities of a system, which can be compensated for through control re-allocation. However, the increased control allocation to the remaining actuators may cause actuator saturation and reduce controller performance. In this work, we present a depth-pitch model-based nonlinear control law that directly considers actuator saturation, and a fault-tolerant control allocation method for a hybrid AUV actuator configuration. Two types of actuator faults are considered for an underwater vehicle with a hybrid actuator configuration. The proposed controller is implemented in a simulated system, and its trajectory tracking performance is compared with a baseline system without fault or saturation tolerance. To determine the utility of the proposed saturation and fault tolerance control methods, the tracking performance in these simulations is quantified in terms of the settling time, post-fault peak values, and root mean square of the depth and pitch errors. Full article

This study introduces a hybrid control structure called Improved Interfered Fluid Dynamic System Nonlinear Model Predictive Control (IIFDS-NMPC) for the path planning and trajectory tracking of autonomous underwater vehicles (AUVs). The system consists of two layers; the upper layer utilizes the Improved Interfered Fluid Dynamic System (IIFDS) for path planning, while the lower layer employs Nonlinear Model Predictive Control (NMPC) for trajectory tracking. Extensive simulation experiments are conducted to determine optimal parameters for both static and dynamic obstacle scenarios. Additionally, real-world testing is performed using the BlueRov2 platform, incorporating multiple dynamic and static obstacles. The proposed approach achieves real-time control at a frequency of 100 Hz and exhibits impressive path tracking accuracy, with a root mean square (RMS) of 0.02 m. This research provides a valuable framework for navigation and control in practical applications. Full article

Efficiently predicting high-resolution and accurate flow fields through networked autonomous marine vehicles (AMVs) is crucial for diverse applications. Nonetheless, a research gap exists in the seamless integration of data-driven flow modeling, real-time data assimilation from flow sensing, and the optimization of AMVs’ sensing strategies, culminating in a closed-loop dynamic data-driven application system (DDDAS). This article presents a novel DDDAS that systematically integrates flow modeling, data assimilation, and adaptive flow sensing using networked AMVs. It features a hybrid data-driven flow model, uniting a neural network for trend prediction and a Gaussian process model for residual fitting. The neural network architecture is designed using knowledge extracted from historic flow data through tidal harmonic analysis, enhancing its capability in flow prediction. The Kriged ensemble transform Kalman filter is introduced to assimilate spatially correlated flow-sensing data from AMVs, enabling effective model learning and accurate spatiotemporal flow prediction, while forming the basis for optimizing AMVs’ flow-sensing paths. A receding horizon strategy is proposed to implement non-myopic optimal path planning, and a distributed strategy of implementing Monte Carlo tree search is proposed to solve the resulting large-scale tree searching-based optimization problem. Computer simulations, employing underwater gliders as sensing networks, demonstrate the effectiveness of the proposed DDDAS in predicting depth-averaged flow in nearshore ocean environments. Full article