Unmanned Underwater Vehicles Research Articles

An underwater docking system based on UUV and recovery mother ship: design and experiment

The research focuses on the autonomous docking of unmanned underwater vehicles (UUV) to a recovery mother ship. Firstly, a novel fork-pole docking station was designed, which could operate independently or be transported onboard a recovery mother ship. Subsequently, a docking guidance scheme combining long-range acoustic guidance and short-range opti-acoustic guidance was proposed. Acoustic guidance employed ultra-short baseline sonar and acoustic beacons to measure relative position information, while optical guidance utilized camera and L-shaped light arrays to obtain relative pose information. Then, an opti-acoustic complementary recovery control strategy was developed to address the instability of acoustic signals. Finally, experimental tests were conducted in a pool to evaluate the performance of the proposed recovery docking system in both static and dynamic docking scenarios, which demonstrated that the docking system was capable of achieving fully autonomous recovery of UUV. Furthermore, the experimental data analysis shows that the presented recovery control strategy is effective and feasible.

A GNSS-aided DVL calibration method based on quaternion estimation for underwater vehicles

Doppler velocity logger (DVL) error parameters can significantly influence the navigation accuracy of DVL/strapdown inertial navigation system (SINS) integration for unmanned underwater vehicles (UUV). To improve the navigation accuracy of UUV, this study proposes a two-stage DVL calibration method aided by global navigation satellite system (GNSS) measurements. First, utilizing the velocity of GNSS/SINS integrated navigation, the scale factor error of DVL is calculated by the moduli of velocities in UUV body frame and DVL instrument frame. Then, using the measurements throughout the calibration process, the calibration problem of the installation angle is converted to a nonlinear constraint optimization problem by describing the angle as a unit quaternion. Moreover, an easy-to-implement quaternion estimation algorithm is chosen to solve the problem and obtain the optimal quaternion. Simulation and sea trial indicate that the proposed method can rapidly and accurately estimate the DVL error parameters in different scenarios, and the position accuracy of the DVL/SINS system is improved.

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), have the potential to complete maritime tasks that are unachievable for manned vehicles, lower the risk of man power, raise the power required to carry out military missions, and reap huge economic benefits. The aim of this review is to identify past and current trends in UMV development and present insights into future UMV development. The review discusses the potential benefits of UMVs, including completing maritime tasks that are unachievable for manned vehicles, lowering the risk of human intervention, and increasing power for military missions and economic benefits. However, the development of UMVs has been relatively tardy compared to that of UVs used on the ground and in the air due to adverse environments for UMV operation. This review highlights the challenges in developing UMVs, particularly in adverse environments, and the need for continued advancements in communication and networking technologies, navigation and sound exploration technologies, and multivehicle mission planning technologies to improve UMV cooperation and intelligence. Furthermore, the review identifies the importance of incorporating AI and machine learning technologies in UMVs to enhance their autonomy and ability to perform complex tasks. Overall, this review provides insights into the current state and future directions for UMV development.

An Underwater Target Tracking Algorithm Based on Extended Kalman Filter

The technology of ocean monitoring is more advanced while the continuous development of industrial Internet. Unmanned underwater vehicle (UUV) is one of major ways for underwater environment monitoring, which makes high-precision positioning, and tracking of it is one of the key problems and needs to be solved urgently. An underwater acoustic positioning and tracking algorithm based on multiple beacons is proposed to reduce the positioning error of underwater acoustic positioning system caused by uncertain sound speed. The system consists of multiple GPS intelligent buoys floated on the sea surface and acoustic signal generator installed on the UUV. The effective sound speeds between the UUV and different buoys are considered to be unequal and estimated as the state parameters, together with the kinematic parameters of the UUV. Based on the kinematic equations of the UUV, the tracking model is obtained under the framework of the extended Kalman filter. Simulation results show that the proposed algorithm can correct the sound speed and improve the stability and accuracy of underwater acoustic positioning system.

Challenges and alternatives for unmanned underwater vehicular research in the Amazon basin: Towards a more sustainable management of water resources and the environment

AbstractThe Amazon basin is one of the largest hydrographic systems in the world, possessing a great diversity of natural resources in need of more sustainable water and environmental management. However, as much of what lies beneath the surface of Amazonian waters is still unexplored, underwater exploration technologies are seen as a means of research and monitoring. The present work therefore aims to answer two research questions: What are the current technologies that could be implemented for unmanned underwater research in the Amazon waters? What are the main limitations and existing alternatives for using unmanned underwater vehicles in the rivers of this region to provide more sustainable water and environment management? Our results demonstrate that remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) are possible options in the short and long terms, respectively. The main challenges in implementing these technologies are related to the variations in hydraulic geometry, current velocities and turbidity of the rivers, as well as the preservation of the region's biodiversity. This research can be taken as a starting point for planners and decision makers seeking more sustainable underwater and environmental exploration of the Amazon river system.

Dynamic step selection algorithm for piecewise linear approximation of complex control trajectories

The paper proposes a method for replacing a complex function used as a target for automatic control of unmanned vehicles with a simplified piecewise linear function, which requires much less computing resources. A numerical method has been developed for adaptive selection of a variable step for approximating a nonlinear one-dimensional function, the analytical expression of which is not given, by a piecewise linear function. The article shows that selection of approximation step (grid) is an important task for minimizing the calculations required number, if the computing device on board the unmanned vehicle must be miniature. The developed algorithm includes the calculation of successive intervals lengths that eventually cover the entire domain of the function with a predetermined approximation accuracy. The determination coefficient is used as a measure of accuracy. Numerical experiments are presented for calculating the linear-piecewise function parameters for the approximating a target trajectory problem with complex configuration for unmanned aerial underwater vehicle automatic control. The proposed method efficiency is compared with methods with a constant step, as well as with methods for calculating the dynamic step in various approaches. The advantage of the developed method from the point of view of computational costs is proved with the same accuracy, the limitations of the applicability of the method are determined.

An underwater explorer remotely operated vehicle: Unraveling the secrets of the ocean

In recent years, underwater robots have been an essential focus of marine science and technology applications. Whether it is the application of military tasks or general civil affairs, underwater robots have played a significant role. For example, unmanned underwater vehicles (UUVs) can work in the sea for a long time, have high maneuverability, and perform various underwater tasks. There are many subcategories of UUVs, such as autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), and autonomous and remotely operated vehicles (ARVs) (Kong et al., 2020).

Detecting moving targets in active sonar echograph of harbor environment using robust high-order flux tensor.

An important goal of an active sonar system is to detect and track underwater intruders such as frogmen, unmanned underwater vehicles, etc. Unfortunately, the intruders appear visually as a small fluctuating "blob" against the high-level fluctuating background caused by multipath propagation and reverberation in the harbor environment, making it difficult to be distinguished. Classical motion features well developed in computer vision cannot cope with an underwater environment. Thus, this paper presents a robust high-order flux tensor (RHO-FT) to characterize the small underwater moving targets against high-level fluctuating background. According to the dynamic behavior of active clutter from real-world harbor environment, we first classify it into two main types: (1) dynamic clutter but with relatively consistent spatial-temporal variation in a certain neighborhood; (2) sparkle clutter presenting completely random flashing. Then starting from the classical flux tensor, we develop a statistical high-order computation to handle the former followed by a spatial-temporal connected component to suppress the latter to achieve higher robustness. Experiments on a set of real-world harbor datasets demonstrate the effectiveness of our RHO-FT.

Compensation of UUV magnetic field interference based on ridge estimation

The ocean magnetic anomaly detection based on unmanned underwater vehicles has great significance in military field, such as anti-submarine, anti-mine, and ship magnetic property evaluation. But it is easy for magnetometer fixed on vehicles to be affected by the magnetic interference of vehicles ferromagnetic equipment and electronic equipment. The magnetic interferential field seriously affects the measurement accuracy of the magnetometer. In this paper, ridge estimation method is proposed to estimate magnetic field interference coefficient and compensate magnetic field. A simulation is conducted to verify the validity of the method. And an experiment is designed and implemented on a vehicle. This method can reduce the measurement error of the magnetometer from 11000.0nT to 155.6nT and compensate 98.55% of the magnetic field interference. Both of simulation and experimental results show that ridge estimation method can effectively overcome the influence of complex collinearity of data and better compensate the interference magnetic field.

Positioning of Unmanned Underwater Vehicle Based on Autonomous Tracking Buoy.

This paper presents a novel method for the dynamic positioning of an unmanned underwater vehicle (UUV) with unknown trajectories based on an autonomous tracking buoy (PUVV-ATB) that indirectly positions the UUV using ultra-short baseline measurements. The method employs a spatial location geometric model and divides the positioning process into four steps, including data preprocessing to detect geometric errors and apply mean filtering, direction capture, position tracking, and position synchronization. To achieve these steps, a new adaptive tracking control algorithm is proposed that does not require trajectory prediction and is applied to the last three steps. The algorithm is deployed to the buoy for tracking simulation and sea trial experiments, and the results are compared with those of a model predictive control algorithm. The autonomous tracking buoy based on the adaptive tracking control algorithm runs more stably and can better complete the precise tracking task for the UUV with a positioning error of less than 10 cm. This method breaks the premise of trajectory prediction based on traditional tracking control algorithms, providing a new direction for further research on UUV localization. Furthermore, the conclusion of this paper has important reference value for other research and application fields related to UUV.

Hydrodynamic interactions within fish schools

Fish schooling behavior may spawn unmanned underwater vehicle schooling.

Experimental Study on Combustion and Heat Transfer Characteristics of Li/SF6 based on Unmanned Underwater Vehicle Combustor

Abstract The Li/SF6 combustion reaction can provide power for unmanned underwater vehicle (UUV) due to the advantages of high heat release and no gaseous combustion products. Exploring the combustion and heat transfer characteristics of Li/SF6 can greatly improve the performance of the UUV combustor. In this paper, based on the operation requirements of UUV combustor, the C300 combustion reactor is designed, the Li/SF6 combustion experimental system is established, and the Li/SF6 combustion and heat transfer experiments are performed. The experimental results show that the overall combustion process lasts about 1.55 h, and the flame temperature is higher than 1768 °C. The temperature in the gas phase decreased gradually along the radial direction and increased along the axis. Eight quasi-equilibrium states are detected, and the maximum combustion power is 12.73 kW. As the pressure increases, the combustion power will increase accordingly. Under the maximum power, the heat transfer efficiency of the C300 combustion reactor can be calculated to be η=85.93% by using water as the heat transfer medium. The heat transfer quantity of the combustion furnace Qf is 2.07 times that of the combustion chamber Qc, and the heat generated by combustion is concentrated in the lower part of the combustion reactor. In addition, a thermal resistance model is proposed to optimize the heat transfer process. Finally, the upper fuel is fully combusted, while the bottom fuel is poorly combusted due to the phenomenon of insufficient melting and combustion product covering.

UxV-Based Deep-Learning-Integrated Automated and Secure Garbage Management Scheme Using Blockchain

This paper presents a deep learning (DL) model integrated automated and secure garbage management scheme using unmanned any vehicle (UxV) to minimize the human effort in terms of the traditional garbage management system. Different kinds of UxV (unmanned aerial vehicles, automated guided vehicles, unmanned surface vehicles, unmanned underwater vehicles, etc.) are utilized to establish an automated garbage management scheme to collect and place the garbage both from the ground and sea surfaces. However, due to the limited battery capacity and inadequate resources of different UxV, a lightweight DL model is developed to detect the garbage successfully with a higher accuracy rate. The proposed lightweight DL model uses two activation functions named MISH and rectified linear unit to enhance the feature extraction and detect the garbage. Moreover, a multi-access edge computing (MEC) server is allocated in the proposed scheme to improve the quality of service (QoS) (i.e., reduce latency and improve security). Furthermore, a blockchain-based secure hazardous garbage (e.g., infectious, toxic, or radioactive materials) tracking technique is concluded in this scheme to identify the individual and reduce the potential harm to the environment. Experimental results demonstrate that the UxV can successfully detect the garbage using the proposed lightweight DL model within a minimum time frame and the obtained accuracy is higher than the other existing DL models. Besides, QoS has been investigated to verify the efficacy of the proposed scheme. Finally, a private blockchain network is established to demonstrate the performance of the proposed hazardous garbage tracking technique.

VERIFICATION OF THE WAVE FACTORIZATION METHOD FOR CALCULATION OF DISTRIBUTED SYSTEMS DURING TOWING IN A FLOW IN ACCELERATION MODE

For purposes of asymmetric confrontation with the Russian Navy, distributed systems (DS; cable, tethered, etc.) for unmanned underwater vehicles need further development and improvement. To date, specifics and methods of designing such DSs are insufficiently covered in scientific and technical literature. Distributed systems include towed systems of constant or variable length in a flow of liquid, underwater communication cables, pillars of offshore oil platforms, etc. This definition includes mechanical objects with one of the linear dimensions at least 10 – 20 times larger than the other two. The main limitations for application of the finite difference method (FDM) for numerical modeling of wave propagation and reflection in DS are the peculiarities of the defining quasi-linear equations. They are related to the need of simultaneous calculation of variables responsible for fast and slow wave processes. The term "singularly perturbed system of equations" is used for such systems of equations. These perturbations are the result of a significant difference in propagation velocities of longitudinal, configurational, bending, and torsional waves in DS at the physical level, etc. In this regard, it is necessary to apply the special step-by-step methods of regularization and filtering of numerical results. This imposes certain limitations on the possibility of simulating real processes and on the accuracy of obtained results, and forces the use of the implicit difference schemes and high-frequency filtering. The method of parallelization of the finite-difference operator and the program code according to the wave type is considered. The idea of parallelization by waves is based on the physical feature of propagation of different types of waves in distributed systems – a difference of 10 – 100 or more times between the propagation velocities of the longitudinal, transverse (configurational), bending and torsional waves in DS. The increase in productivity of the MPI version of a programming code when performing calculations on the SMP system is on average at least 30 – 100%, depending on the required accuracy of calculations and parallelization options. The version of the parallelized code, which uses the wave factorization method, is relevant when solving tasks of DS control, operational numerical analysis of transient modes of motion, etc., where performance is critically necessary. The comparative assessment of the accuracy of the experimental data, the original (non-parallelized) algorithm and the parallelized algorithm was carried out using the example of the numerical solution of the problem of the towing vessel’s movement in the acceleration mode when towing the DS.

An INS/DVL integrated navigation filtering method against complex underwater environment

In this paper, a filtering method against complex underwater environment is proposed for the inertial navigation system/doppler velocity log (INS/DVL) of the unmanned underwater vehicle (UUV). The method has two stages. The first stage is the pre-cruise. In this stage, the martingale distance is constructed. And according to chi-square test, the two thresholds can be obtained, which are used to determine the complexity of the environment. Then, these thresholds are used in cruise of UUV. During cruise, the martingale distance is similarly constructed. The environment is judged in real time according to chi-square test. When the martingale distance exceeds the first threshold, the environment is considered to be of high complexity. The robust method based on covariance matching is used. When the martingale distance lies between the first and second thresholds, the UUV is considered to be in a low complex environment, and the adaptive method is used. Otherwise, the conventional nonlinear filter is performed. Finally, the results of field experiment show that the method can avoid the over compensation of parameters in stable case from conventional methods, and comprehensively solve the problem of degradation of accuracy due to different environment.

DDQN path planning for unmanned aerial underwater vehicle (UAUV) in underwater acoustic sensor network

DDQN path planning for unmanned aerial underwater vehicle (UAUV) in underwater acoustic sensor network

A novel adaptive nonlinear Kalman filter scheme for DVL-aided SINS alignment in underwater vehicles

The rapid and accurate initial alignment of a strapdown inertial navigation system (SINS) aided by a Doppler velocity logger (DVL) still poses a significant challenge in unmanned underwater vehicle navigation. In this study, a novel adaptive nonlinear filtering alignment scheme is developed for a SINS with a large misalignment angle. In previous studies, the frame-inconsistency problem of the nonlinear SINS error model was neglected. Thus, a modified nonlinear error model of a SINS is derived here rigorously as the process model of the nonlinear Kalman filter. In addition, the noise amplification of applying Sage-Husa adaptive Kalman filter (SHAKF) in alignment is analyzed, and a novel arctangent fading memorial factor is introduced to dynamically adjust the weight of current measurements. Simulations and field experiments indicate that the proposed algorithm is efficient in underwater DVL/SINS in-motion alignment. The alignment accuracy and convergence rate of the modified model are significantly improved compared to those of the traditional model. The arctangent fading memorial SHAKF can reliably estimate the measurement noise and improve the performance of alignment.

Demonstration of Spiral Wavefront Navigation on an Unmanned Underwater Vehicle

Experiments carried out at NUWC's Seneca Lake facility in the summer of 2020 demonstrate the efficacy of underwater navigation via a single spiral wavefront (SWF) acoustic beacon. An SWF beacon is a transducer array that has the capability of generating a spiral acoustic signal whose phase varies linearly with angular aspect along with a reference signal with constant phase. An unmanned underwater vehicle (UUV) operating in Seneca Lake under various channel conditions receives a regular signal from a fixed position single SWF beacon. The UUV's aspect relative to the beacon and its radial velocity can be determined by comparing the phase of the spiral signal to that of the reference signal. The SWF navigation solutions are compared to the UUVs inertial/GPS navigation. Advances in signal processing for the spiral wavefront navigation in multipath environments are detailed. An example of multipath performance from experimental data is shown.

Autonomous Underwater Environment Perceiving and Modeling: An Experimental Campaign With FeelHippo AUV for Forward Looking Sonar-Based Automatic Target Recognition and Data Association

Seabed inspection is one of the most sought-after applications for autonomous underwater vehicles (AUVs). Acoustical sensors, such as side-scan sonars and forward-looking sonars (FLSs), are commonly favored over optical cameras to carry out such a task. Indeed, sonars are not influenced by illumination conditions and can provide high-range data. However, due to the lack of features and low resolution, acoustical images are often hard to interpret with conventional automatic techniques, forcing human operators to analyze thousands of collected images to identify the so-called objects of potential interest (OPIs). In this article, we report the development of an automatic target recognition (ATR) methodology to identify and localize OPIs in FLS imagery. Such detections have been then exploited to realize a virtual world model with the probabilistic multiple hypothesis anchoring data association and model tracking algorithm. Distinct models of convolutional neural networks have been trained with a data set acquired in May 2019 at the Naval Support and Experimentation Centre (Centro di Supporto e Sperimentazione Navale—CSSN) basin in La Spezia, Italy. The ATR strategy has been successfully validated offline with the data gathered in October 2019 in the same site where the seabed targets were replaced and relocated. As regards the world modeling technique, it has been preliminarily tested on a simulated scenario built upon unmanned underwater vehicle Simulator. Finally, both the ATR and world modeling systems were on-field tested in October 2020 at the CSSN basin in a multivehicle architecture by employing an acoustical channel between FeelHippo AUV and an autonomous moving buoy.

Autonomous Heading Planning and Control Method of Unmanned Underwater Vehicles for Tunnel Detection

To address the challenge of unmanned underwater vehicle (UUV) autonomous navigation in long-distance underwater tunnel detection tasks and improve the control performance of its heading control system, a method of autonomous heading planning and control based on sonar-ranging feedback control was proposed. This method combines UUV’s autonomous heading planning technology with the heading proportion-integral-derivative (PID) control algorithm, optimizing the acquisition method of controller input data, to impart specific adaptive characteristics to the controller. Using the ranging principle of ultrasonic spontaneous self-collection, it is possible to obtain the yaw direction and angle of the vehicle relative to the target heading in the tunnel and continuously adjust the control law to change the heading as the vehicle’s heading status changes during navigation. The effectiveness of the autonomous heading planning and control method is verified through pool experiments. The analysis and experimental results show that the proposed heading planning method achieves good control effect in UUV’s underwater tunnel detection heading control, and exhibits obvious advantages in long-distance closed tunnel environments. UUV can adaptively adjust the heading according to the tunnel environment and has a fast response and strong applicability in planning and controlling the heading.