Search Results (661)

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

Underwater wireless sensor networks (UWSNs) have been applied in lots of fields. However, coverage holes are usually caused by complex underwater environment. Coverage holes seriously affect UWSNs’ performance and quality of service; thus, their recovery is crucial for 3D UWSNs. Although most of the current research recovery algorithms demand hole detection, the number of additional mobile nodes is too large, the communication and computing costs are high, and the coverage and energy balance are poor. Therefore, these methods are not suitable for UWSN hole repairing. In order to enhance the performance of hole recovery, a coverage hole recovery method for 3D UWSNs in complex underwater environments based on virtual force guidance and energy balance is proposed. The proposed method closely combines the node energy and considers complex environmental factors. A series of multi-dimensional virtual force models are established based on energy between nodes, area boundaries, zero-energy holes, low-energy coverage holes, underwater terrain, and obstacle forces. Then, a coverage hole recovery method for 3D UWSNs based on virtual force guidance and energy balance (CHRVE) is proposed. In this method, the direction and step size of mobile repairing node movement is guided by distributed computation of virtual forces, and the nodes are driven towards the target location by means of AUV or other carrier devices. The optimal position to improve coverage rate and node force balance is obtained. Simulation experiments show good adaptability and robustness to complex underwater terrain and different environments. The algorithm does not require precise coverage hole boundary detection. Furthermore, it balances network energy distribution significantly. Therefore, this method reduces the frequency of coverage hole emergence and network maintenance costs. Full article

To improve the path-following performance of autonomous underwater vehicles (AUVs) under ocean currents, a control method based on line-of-sight with fuzzy controller (FLOS) guidance and the fuzzy sliding mode controller (FSMC) is proposed. This method considers multiple factors affecting guidance and adaptively determines the optimal heading angle through the fuzzy controller to enhance guidance capability. Additionally, a novel FSMC based on Lyapunov stability theory is designed to suppress the influence of model uncertainty and external disturbances on the control system. Simulations and experiments of the proposed control method demonstrate that it can maintain precise tracking under disturbances, improving path-following performance metrics by more than 15%. Full article

In a complex and dynamic battlefield environment, enabling autonomous underwater vehicles (AUVs) to reach dynamic targets in the shortest possible time using global autonomous planning is a key issue affecting the completion of search tasks. In this study, ahierarchicalAUV task planning method that uses a combination of hierarchical programming and a snake optimization algorithm is proposed for two typical cases where the platform can provide initial target information. This method decomposes the search task problem into a three-level programming problem, with the outer task planning goal of achieving the shortest encounter time between AUV and dynamic targets; the goal of task planning in the middle layer is to achieve the shortest actual navigation time for AUVs under different operating conditions; and the internal task planning is responsible for considering the comprehensive trajectory optimization under navigation constraints such as threat zone, path length, and path smoothness. The snake optimization algorithm was used for solving each layer of task planning. The feasibility of the proposed method was verified through simulation experiments of AUV search tasks under two types of initial target information conditions. The simulation results show that this method can achieve task planning for AUV searching for dynamic targets under various constraint conditions, optimize the encounter time between AUV and dynamic targets, and have strong engineering practical value. It has certain reference significance for task planning problems similar to underwater unmanned equipment. Full article

Autonomous underwater vehicles have seen widespread adoption across industrial, scientific, and defence applications. They are typically utilized to perform oceanic mapping, surveillance, and inspection-type missions. Hovering AUVs, used for inspection applications, are over-actuated vehicles incorporating multiple thrusters to enable multiple degrees of freedom control at a low velocity. These vehicles, however, are extremely energy-limited, owing to their restrictive structural design that prohibits large batteries. This necessitates careful hydrodynamic design to best utilize this limited energy storage. Of particular importance are the hydrodynamic propulsion efficiencies of these vehicles. Whilst the external structure of AUV platforms is relatively well-defined and hydrodynamically optimized, one area has seen limited focus and optimization. This is the immediate surroundings of the propulsion geometry and housing. In this body of work, we propose an adaptation to the traditional through-body tunnel thruster geometry of an over-actuated AUV platform. The modification is the inclusion of a retractable internal thruster cover. Subsequently, a comparison is provided between a clean-hull AUV configuration, one with open through-body thrusters, and one fitted with the designed cover geometry. A comprehensive computational fluid dynamics analysis is then converged and assessed using the Reynolds-Averaged Navier–Stokes equations. The drag and local flow fields are determined, where the covers are found to reduce the drag coefficient and total drag of the AUV by 9.51%, primarily due to a reduction of 9.91% in the pressure drag. These findings highlight the increased operational efficiency of the cover geometry and support the adoption of such covers for energy-constrained AUVs. Full article

Underwater unmanned vehicles are complementary with human presence and manned vehicles for deeper and more complex environments. An autonomous underwater vechicle (AUV) has automation and long-range capacity compared to a cable-guided remotely operated vehicle (ROV). Navigation of AUVs is challenging due to the high absorption of radio-frequency signals underwater and the absence of a global navigation satellite system (GNSS). As a result, most navigation algorithms rely on inertial and acoustic signals; precise localization is then costly in addition to being independent from acoustic data communication. The purpose of this paper is to propose and analyze the performance of a novel low-cost simultaneous communication and localization algorithm. The considered scenario consists of an AUV that acoustically sends sensor or status data to a single fixed beacon. By estimating the Doppler shift and the range from this data exchange, the algorithm can provide a location estimate of the AUV. Using a robust state estimator, we analyze the algorithm over a survey path used for AUV mission planning both in numerical simulations and at-sea experiments. Full article

Due to the singularity of Euler angles and the ambiguity of quaternions, to further expand the attitude reachable range of underactuated AUVs in the vertical plane, $SO\left(3\right)$ is used to represent the attitude change of underactuated AUVs. The transverse function of the attitude on $SO\left(3\right)$ is designed, and the exponential mapping method is used to construct the attitude kinematic controller of underactuated AUVs. Considering the changes in the model and ocean current during motion, interval type II fuzzy systems (IT2-FLSs) are used to estimate these changes. The backstepping method and the small gain theorem are adopted to design dynamic controllers to ensure the stability and robustness of the system. A novel saturation auxiliary system is designed to compensate for the influence of actuator saturation characteristics. Finally, the simulation results verify the effectiveness of the proposed controller and ensure the practical stabilization of the underactuated AUV attitude. Full article

To address the issues of high attenuation, weak reception signal, and channel blockage in the current electric field communication of underwater robots, research on autonomous underwater vehicle (AUV) multi-node networking communication based on underwater electric field Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) channel was conducted. This article, first through simulation, finds that the Optimized Link State Routing (OLSR) protocol has a smaller routing packet delay time and higher reliability compared to the Ad Hoc On-Demand Distance Vector (AODV) protocol on underwater electric field CSMA/CA channels. Then, a 2FSK underwater electric field communication system was established, and dynamic communication experiments were carried out between two AUV nodes. The experimental results showed that within a range of 0 to 3.5 m, this system can achieve underwater dynamic electric field communication with a bit error rate of 0 to 0.628%. Finally, to avoid channel blockage during underwater AUV multi-node communication, this article proposes a dynamic backoff method for AUV multi-node communication based on CSMA/CA. This system can achieve dynamic multi-node communication of underwater electric fields with an error rate ranging from 0 to 0.96%. The research results have engineering application prospects for underwater cluster operations. Full article

A new autonomous underwater vehicle (AUV) has high maneuverability near the bottom and a direction turnaround ability, called the autonomous underwater helicopter (AUH). This paper numerically investigates the hydrodynamic performance of the AUH. A Reynolds-Averaged Navier–Stokes (RANS) equation, a computational fluid dynamics (CFD) technique, is applied to analyze the AUH’s behavior. Investigations of the AUH’s hydrodynamic characteristics become more obvious with a service speed in the range of 0.4–1.2 m/s. For the same velocity condition, the resistance of the AUH increases, and the irregular eddy at the rear of the AUH expands with changes in the angles of attack and the length/height ratio. Essential design characteristics including pressure, velocity distribution, and velocity streamlines are shown and analyzed. These insights can be used as a guideline to reduce drag force and optimize the AUH profile for future designs. It has great potential for improving the AUH’s control algorithms. Full article

For dealing with the energy consumption in multi-agent systems (MASs), an event-triggered (ET) methodology is promising, which relies on the activation of communication devices only when communication of data is needed. This paper explores the leaderless consensus for nonlinear MASs using an adaptive ET approach via an output feedback methodology. This adaptive ET scheme is preferred as it can adapt to the environment through setting a communication threshold. The proposed approach renders the observed states of agents by use of nonlinear observers in an output feedback control dilemma, making it more practical. Simple Luenberger observers are developed to avoid the problem of always measuring agents’ states. The strategy of adaptive ET-based control is employed to minimize resource use and information transmission. Design conditions for the observer-based adaptive ET consensus control of nonlinear MASs have been derived via a Lyapunov function, containing state estimation error, consensus error, adaptation term, and nonlinearity bounds. In contrast to the existing methods, the present approach applies a more practical output feedback schema, uses adaptive ET proficiency, and deals with nonlinear agents. An example of a formation of autonomous underwater vehicles achieving the basic consensus realization between displacement and velocity is included to illustrate the viability of the resultant approach. Full article

Autonomous Underwater Vehicles (AUVs) are widely used for hydrological monitoring, underwater exploration, and geological surveys. However, AUVs face limitations in underwater navigation due to the high costs associated with Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL), hindering the development of low-cost vehicles. Micro Electro Mechanical System Inertial Measurement Units (MEMS IMUs) are widely used in industry due to their low cost and can output acceleration and angular velocity, making them suitable as an Attitude Heading Reference System (AHRS) for low-cost vehicles. However, poorly calibrated MEMS IMUs provide an inaccurate angular velocity, leading to rapid drift in orientation. In underwater environments where AUVs cannot use GPS for position correction, this drift can have severe consequences. To address this issue, this paper proposes Underwater Gyros Denoising Net (UGDN), a method based on dilated convolutions and LSTM that learns and extracts the spatiotemporal features of IMU sequences to dynamically compensate for the gyroscope’s angular velocity measurements, reducing attitude and heading errors. In the experimental section of this paper, we deployed this method on a dataset collected from field trials and achieved significant results. The experimental results show that the accuracy of MEMS IMU data denoised by UGDN approaches that of fiber-optic SINS, and when integrated with DVL, it can serve as a low-cost underwater navigation solution. Full article

In this paper, we investigate the formation isolation regulation issue regarding multiple Autonomous Underwater Vehicles (AUVs) characterized by a “leader–follower” framework. Considering the cooperative–competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer is designed based on backstepping to mitigate these disturbances, and an event-triggered control scheme is designed to realize the two-part consensus control within the multi-AUV system. Through rigorous theoretical analysis, it is shown that the system achieves asymptotic steadiness and is free from Zeno behavior under the proposed event-triggered control scheme. Finally, numerical simulations confirm the efficiency of the regulation strategy in achieving formation separation within the multi-AUV, where the trajectory tracking errors of individual AUVs gather in a compact vicinity close to the source, and the structure convergence is achieved, with the absence of Zeno behavior also demonstrated. Full article