Search Results (674)

In the context of multi-autonomous underwater vehicle (multi-AUV) operations, the target assignment is addressed as a multi-objective allocation (MOA) problem. The selection of strategy for multi-AUV target allocation is dependent on the current non-cooperative environment. This paper establishes a multi-AUV allocation situation advantage evaluation system to assess and quantify the non-cooperative environment. Based on this framework, a multi-AUV target allocation model using a bi-matrix game theory is developed, where multi-AUV target allocation strategies are considered as part of the strategic framework within the game. The payoff matrix is constructed based on factors including the situational context of multi-AUV operations, effectiveness, and AUV operational integrity. The Nash equilibrium derived from the game analysis serves as the optimal solution for resource distribution in multi-AUV non-cooperative scenarios. To address the challenge of finding the Nash equilibrium in bi-matrix games, this paper introduces a repulsion process quantum particle swarm optimization (RPQPSO) algorithm. This method not only resolves the complexities of Nash equilibrium computation but also overcomes the limitations of traditional optimization methods that often converge to local optima. A simulation experiment of multi-AUV operations is designed to validate the multi-AUV target allocation model, demonstrating that the RPQPSO algorithm performs effectively and is applicable to multi-AUV task scenarios. Full article

This paper examines the control challenges faced by underactuated Autonomous Underwater Vehicles (AUVs) under ocean current disturbances. It proposes a Backstepping Integral Sliding Mode Control (BISMC) strategy to enhance their adaptability and robustness. The BISMC strategy integrates the system decomposition capability of the backstepping control method with the rapid response and robustness advantages of the Sliding Mode Control method, enabling the design of a heading controller and a double closed-loop depth controller. By introducing an integral component, the strategy eliminates steady-state errors caused by ocean currents, accelerating system convergence and improving accuracy. Furthermore, a saturation function is employed to mitigate output chattering issues. Simulation results demonstrate that the BISMC controller significantly enhances the control precision and anti-disturbance capabilities of AUVs under low-frequency ocean current disturbances, showcasing exceptional adaptive and self-disturbance rejection performance. Full article

In complex marine environments, autonomous underwater vehicles (AUVs) rely on robust navigation and positioning. Traditional algorithms face challenges from sensor outliers and non-Gaussian noise, leading to significant prediction errors and filter divergence. Outliers in sensor observations also impact positioning accuracy. The original unscented Kalman filter (UKF) based on the minimum mean square error (MMSE) criterion suffers from performance degradation under these conditions. This paper enhances the minimum error entropy unscented Kalman filter algorithm using variational Bayesian (VB) methods and mixed entropy functions. By implementing minimum error entropy (MEE) and mixed kernel functions in the UKF, the algorithm’s robustness under complex underwater conditions is improved. The VB method adaptively fits the measurement noise covariance, enhancing adaptability to marine environments. Simulations and sea trials validate the proposed algorithm’s performance, showing significant improvements in navigation accuracy and root mean square error (RMSE). In environments with complex noise, our algorithm improves the overall navigation accuracy by at least 10% over other existing algorithms. This demonstrates the high accuracy and robustness of the algorithm. Full article

Autonomous Underwater Vehicles (AUVs) rely on integrated navigation systems and corresponding filtering algorithms to ensure mission success and the spatiotemporal accuracy of sampled data. Among these, the ensemble Kalman filter (EnKF) combines Monte Carlo methods with the Kalman filter, which is particularly suited for nonlinear systems. This study proposes an enhanced adaptive EnKF algorithm to improve the smoothness and accuracy of the filtering process. Instead of the conventional Gaussian distribution, this algorithm employs a Laplace distribution to construct the system state vector and observation vector ensembles, enhancing stability against non-Gaussian noise. Additionally, the algorithm dynamically adjusts the number of vector members in the ensemble using adaptive mechanisms by specifying thresholds during filtering to adapt the requirements of real-world observational settings. Using field trial data from DVL, GPS, and electronic compass measurements, we optimize the algorithm’s parameter settings and evaluate the overall performance of the algorithm. Results indicate that the proposed adaptive EnKF achieves superior accuracy and smoothness performance. Compared to the conventional EnKF and EKF, it not only reduces the average positioning error by 30% and 44%, respectively, but also significantly improves the filtering smoothness and stability, highlighting its advantages for AUV navigation. Full article

Deep reinforcement learning models have the advantage of being able to control nonlinear systems in an end-to-end manner. However, reinforcement learning controllers trained in simulation environments often perform poorly with real robots and are unable to cope with situations where the dynamics of the controlled object change. In this paper, we propose a DRL control algorithm that combines progressive networks and context as a depth tracking controller for AUVs. Firstly, an embedding network that maps interaction history sequence data onto latent variables is connected to the input of the policy network, and the context generated by the network gives the DRL agent the ability to adapt to the environment online. Then, the model can be rapidly adapted to a new dynamic environment, which was represented by the presence of generalized force disturbances and changes in the mass of the AUV, through a two-stage training mechanism based on progressive neural networks. The results showed that the proposed algorithm was able to improve the robustness of the controller to environmental disturbances and achieve fast adaptation when there were differences in the dynamics. Full article

This study presents the development of a conceptual model for an autonomous underwater vehicle (AUV) information and control system (ICS) tailored for the mineral and raw materials complex (MRMC). To address the challenges of underwater mineral exploration, such as harsh conditions, high costs, and personnel risks, a comprehensive model was designed. This model was built using correlation analysis and expert evaluations to identify critical parameters affecting AUV efficiency and reliability. Key elements, including pressure resistance, communication stability, energy efficiency, and maneuverability, were prioritized. The results indicate that enhancing these elements can significantly improve AUV performance in deep-sea environments. The proposed model optimizes the ICS, providing a foundation for designing advanced AUVs capable of efficiently executing complex underwater tasks. By integrating these innovations, the model aims to boost operational productivity, ensure safety, and open new avenues for mineral resource exploration. This study’s findings highlight the importance of focusing on critical AUV parameters for developing effective and reliable solutions, thus addressing the pressing needs of the MRMC while promoting sustainable resource management. Full article

Visual recognition and localization of underwater optical beacons are critical for AUV docking, but traditional beacons are limited by fixed directionality and light attenuation in water. To extend the range of optical docking, this study designs a novel omnidirectional rotating optical beacon that provides 360-degree light coverage over 45 m, improving beacon detection probability through synchronized scanning. Addressing the challenges of light centroid detection, we introduce a parallel deep learning detection algorithm based on an improved YOLOv8-pose model. Initially, an underwater optical beacon dataset encompassing various light patterns was constructed. Subsequently, the network was optimized by incorporating a small detection head, implementing dynamic convolution and receptive-field attention convolution for single-stage multi-scale localization. A post-processing method based on keypoint joint IoU matching was proposed to filter redundant detections. The algorithm achieved 93.9% AP at 36.5 FPS, with at least a 5.8% increase in detection accuracy over existing methods. Moreover, a light-source-based measurement method was developed to accurately detect the beacon’s orientation. Experimental results indicate that this scheme can achieve high-precision omnidirectional guidance with azimuth and pose estimation errors of -4.54° and 3.09°, respectively, providing a reliable solution for long-range and large-scale optical docking. Full article

Communication with the underwater vehicles during their tasks is one of the most important issues. The need for real-time data transfer raises the necessity of developing communication systems. Conventional underwater communication systems, such as acoustic systems, cannot satisfy applications that need a high transmission data rate. In this study, we investigate the radio frequency communication system in seawater, which is crucial for real-time data transfer with underwater vehicles. The experiments were in a water tank full of seawater and a real environment in the ocean. Three types of antennae were used: loop antenna, wire antenna, and helical antenna. An Autonomous Underwater Vehicle (AUV) is used as a transmitter to measure the transmission rate as a function of distance. The helical antenna showed better performance regarding the coverage area. Furthermore, the AUV could move freely within the helical and capture live video streaming successfully. This investigation underscores the potential of radio frequency communication systems for enhancing underwater vehicle operations, offering promising avenues for future research and practical implementation. Full article

As a significant subset of unmanned underwater vehicles (UUVs), autonomous underwater vehicles (AUVs) possess the capability to autonomously execute tasks. Characterized by its flexibility, cost-effectiveness, extensive operational range, and robust environmental adaptability, AUV has emerged as the primary technological apparatus for deep-sea exploration and research. In this paper, we present the design of a 10,000 m class AUV equipped with capabilities such as fixed-depth navigation, regional autonomous cruising, full-depth video recording, and temperature and salinity profiling. Initially, we outline the comprehensive design of the AUV, detailing its structural configuration, system components, functional module arrangement, and operational principles. Subsequently, we compute the hydrodynamic parameters using a spatial kinematics model. Finally, the AUV designed in this paper is tested for its functions and performance, such as fixed-depth sailing, maximum speed, and maximum diving depth, and its reliability and practicability are verified. Full article

Underwater object tracking holds considerable significance in the field of ocean engineering. Additionally, it serves as a crucial component in the operations of autonomous underwater vehicles (AUVs), particularly during tasks associated with capturing marine organisms. However, the attenuation and scattering of light result in shortcomings such as poor contrast in underwater images. Additionally, the motion deformation of marine organisms poses a significant challenge. Therefore, existing tracking algorithms face difficulty in direct application to underwater object tracking. To overcome this challenge, we propose a novel tracking architecture for the marine organism capturing of AUVs called ULOTrack. ULOTrack is based on a performance discrimination and re-detection framework and constitutes three modules: (1) an object tracker, which can extract multi-feature information of the underwater target; (2) a multi-layer tracking performance discriminator, which serves the purpose of evaluating the stability of the current tracking state, thereby reducing potential model drift; and (3) lightweight detection, which can predict the candidate boxes to relocate the lost tracked underwater object. We conduct comprehensive experiments to validate the efficacy of the designed modules. Finally, the results of the experimentation demonstrate that ULOTrack significantly outperforms existing approaches. In the future, we aim to carefully scrutinize and select more suitable features to enhance tracking accuracy and speed. Full article

In view of the poor adaptability and uneven coverage of static underwater wireless sensor networks (UWSNs) to environmental changes and the need for dynamic monitoring, a three-dimensional coverage method involving a dynamic UWSNs for marine ranching, based on an improved sparrow search algorithm (ISSA), is proposed. Firstly, the reverse learning strategy was introduced to generate the reverse sparrow individuals and fuse with the initial population, and the individual sparrows with high fitness were selected to improve the search range. Secondly, Levy flight was introduced to optimize the location update of the producer, which effectively expanded the local search capability of the algorithm. Finally, the Cauchy mutation perturbation mechanism was introduced into the scrounger location to update the optimal solution, which enhanced the ability of the algorithm to obtain the global optimal solution. When deploying UWSNs nodes, an autonomous underwater vehicle (AUV) was used as a mobile node to assist the deployment. In the case of underwater obstacles, the coverage hole in the UWSNs was covered by an AUV at specific times. The experimental results show that compared with other algorithms, the ISSA has a shorter mobile path and achieves a higher coverage rate, with lower node energy consumption. Full article

Air Traffic Management (ATM) has become complicated mainly due to the increase and variety of input information from Communication, Navigation, and Surveillance (CNS) systems as well as the proliferation of Unmanned Aerial Vehicles (UAVs) requiring Unmanned Aerial System Traffic Management (UTM). In response to the UTM challenge, a decision support system (DSS) has been developed to help ATM personnel and aircraft pilots cope with their heavy workloads and challenging airspace situations. The DSS provides airspace situational awareness (ASA) driven by knowledge representation and reasoning from an Avionics Analytics Ontology (AAO), which is an Artificial Intelligence (AI) database that augments humans’ mental processes by means of implementing AI cognition. Ontologies for avionics have also been of interest to the Federal Aviation Administration (FAA) Next Generation Air Transportation System (NextGen) and the Single European Sky ATM Research (SESAR) project, but they have yet to be received by practitioners and industry. This paper presents a decision-making computer tool to support ATM personnel and aviators in deciding on airspace situations. It details the AAO and the analytical AI foundations that support such an ontology. An application example and experimental test results from a UAV AAO (U-AAO) framework prototype are also presented. The AAO-based DSS can provide ASA from outdoor park-testing trials based on downscaled application scenarios that replicate takeoffs where drones play the role of different aircraft, i.e., where a drone represents an airplane that takes off and other drones represent AUVs flying around during the airplane’s takeoff. The resulting ASA is the output of an AI cognitive process, the inputs of which are the aircraft localization based on Automatic Dependent Surveillance–Broadcast (ADS-B) and the classification of airplanes and UAVs (both represented by drones), the proximity between aircraft, and the knowledge of potential hazards from airspace situations involving the aircraft. The ASA outcomes are shown to augment the human ability to make decisions. Full article

Underwater vision data facilitate a variety of underwater operations, including underwater ecosystem monitoring, topographical mapping, mariculture, and marine resource exploration. Conventional laser-based underwater imaging systems with complex system architecture rely on high-cost laser systems with high power, and software-based methods can not enrich the physical information captured by cameras. In this manuscript, a low-cost modulated laser-based imaging system is proposed with a spot in the shape of a square ring to eliminate the overlap between the illumination light path and the imaging path, which could reduce the negative effect of backscatter on the imaging process and enhance imaging quality. The imaging system is able to achieve underwater imaging at long distance (e.g., 10 m) with turbidity in the range of 2.49 to 7.82 NTUs, and the adjustable divergence angle of the laser tubes enables the flexibility of the proposed system to image on the basis of application requirements, such as the overall view or partial detail information of targets. Compared with a conventional underwater imaging camera (DS-2XC6244F, Hikvision, Hangzhou, China), the developed system could provide better imaging performance regarding visual effects and quantitative evaluation (e.g., UCIQUE and IE). Through integration with the CycleGAN-based method, the imaging results can be further improved, with the UCIQUE increased by 0.4. The proposed low-cost imaging system with a compact system structure and low consumption of energy could be equipped with platforms, such as underwater robots and AUVs, to facilitate real-world underwater applications. Full article

In the marine environment, the motion characteristics of Autonomous Underwater Vehicles (AUVs) are influenced by unknown factors such as time-varying ocean currents, thereby amplifying the complexity involved in the design of path-following controllers. In this study, a backstepping sliding mode control method based on a current observer and nonlinear disturbance observer (NDO) has been developed, addressing the 3D path-following issue for AUVs operating in the ocean environment. Accounting for uncertainties like variable ocean currents, this research establishes the AUV’s kinematics and dynamics models and formulates the tracking error within the Frenet–Serret coordinate system. The kinematic controller is designed through the line-of-sight method and the backstepping method, and the dynamic controller is developed using the nonlinear disturbance observer and the integral sliding mode control method. Furthermore, an ocean current observer is developed for the real-time estimation of current velocities, thereby mitigating the effects of ocean currents on navigational performance. Theoretical analysis confirms the system’s asymptotic stability, while numerical simulation attests to the proposed method’s efficacy and robustness in 3D path following. Full article

This paper addresses the challenge of docking an Autonomous Underwater Vehicle (AUV) under realistic conditions. Traditional model-based controllers are often constrained by the complexity and variability of the ocean environment. To overcome these limitations, we propose a Deep Reinforcement Learning (DRL) approach to manage the homing and docking maneuver. First, we define the proposed docking task in terms of its observations, actions, and reward function, aiming to bridge the gap between theoretical DRL research and docking algorithms tested on real vehicles. Additionally, we introduce a novel observation space that combines raw noisy observations with filtered data obtained using an Extended Kalman Filter (EKF). We demonstrate the effectiveness of this approach through simulations with various DRL algorithms, showing that the proposed observations can produce stable policies in fewer learning steps, outperforming not only traditional control methods but also policies obtained by the same DRL algorithms in noise-free environments. Full article