Unmanned Underwater Vehicles Research Articles

Design a Computer Controlled Unmanned Underwater Vehicle Thruster Using Smart Closed Loop Controller

The first step of designing an Unmanned Underwater Vehicle (UUV) depends mainly on the selection of the thrusters as UUVs require many thrusters to be installed onboard for smooth operation. The widely used thrusters in UUVs are has relatively large dimensions and they just spin in the water without reporting any kind of important information such as the status of the thruster and the RPM of the motor. This project aims to design and implement a new computer-controlled thrusting system from end-to-end that fits various UUVs with more advantages including smaller size thruster, cheaper in price, and with more additional smart capabilities that reports thruster’s instantaneous RPM, power consumption, proper operation, malfunctions, damages, and water stream blockage.

A-Star (A*) with Map Processing for the Global Path Planning of Autonomous Underwater and Surface Vehicles Operating in Large Areas

The global path planning system is one of the basic systems ensuring the autonomous operation of unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs) in a complex aquatic environment. The A* path planning algorithm is one of the most well-known algorithms used to obtain an almost optimal path, avoiding obstacles even in a complex environment containing objects with specific shapes and non-uniform arrangements. The main disadvantage of this algorithm is the computational cost of path calculation. This article presents a new approach based on the image processing of the map before determining the path using A*. The results of numerical research based on a large-sized map expressing the port area confirm the proposed method’s effectiveness, which reduces the calculation time by over 500 times with a slight increase in the path length compared to the basic version of the A* algorithm. Based on the obtained results, the proposed approach also increases the path’s safety by designating narrow and risky areas as closed to vehicle movement. For this reason, the method seems suitable for use in global path planning for autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) operating in large areas.

A Multi-Task Network: Improving Unmanned Underwater Vehicle Self-Noise Separation via Sound Event Recognition

The performance of an Unmanned Underwater Vehicle (UUV) is significantly influenced by the magnitude of self-generated noise, making it a crucial factor in advancing acoustic load technologies. Effective noise management, through the identification and separation of various self-noise types, is essential for enhancing a UUV’s reception capabilities. This paper concentrates on the development of UUV self-noise separation techniques, with a particular emphasis on feature extraction and separation in multi-task learning environments. We introduce an enhancement module designed to leverage noise categorization for improved network efficiency. Furthermore, we propose a neural network-based multi-task framework for the identification and separation of self-noise, the efficacy of which is substantiated by experimental trials conducted in a lake setting. The results demonstrate that our network outperforms the Conv-tasnet baseline, achieving a 0.99 dB increase in Signal-to-Interference-plus-Noise Ratio (SINR) and a 0.05 enhancement in the recognized energy ratio.

Collision-free trajectory tracking strategy of a UUV via finite-time extended state observer-based sliding mode predictive control

This paper focuses on a trajectory tracking problem for unmanned underwater vehicles (UUVs) subject to current disturbances and static obstacles. A double-loop framework is established. The kinematic governor employs model predictive control(MPC), which takes into account the UUV’s kinematic characteristics as well as the conditions for obstacle avoidance when calculating the control command and provides an optimal velocity command input for the dynamic controller. Then, the dynamic controller is developed based on a fast finite-time extended state observer (FESO) and a fast adaptive integral terminal sliding mode controller (FAITSMC). The construction of the fast FESO integrates UUV’s dynamics model, a proportional integral velocity variable, and a fractional order term of the output observation error, which can identify model uncertainties and external disturbances in finite time. By means of adaptive uncertainty compensation, the FAITSMC enables the error between actual speed and speed reference to converge to a minimum quickly. By applying Lyapunov stability theory and the finite-time analysis technique, sufficiency criteria are established to guide and keep the UUV on a reference trajectory via an inner-outer loop control structure. Finally, simulation examples in different scenarios are presented to verify the feasibility and effectiveness of the derived theoretical results.

Evolutionary Optimization for Unmanned Underwater Vehicle Navigation

Unmanned Underwater Vehicles (UUVs) play a vital role in various underwater exploration and surveillance tasks. However, the effective navigation of UUVs in complex underwater environments poses significant challenges due to factors such as limited communication, dynamic currents, and other difficulties. Evolutionary optimization techniques have developed as promising tools for enhancing UUV navigation abilities. This review provides a brief overview of the application of evolutionary optimization algorithms, including genetic algorithms, evolutionary approaches, and particle swarm optimization, in the context of UUV navigation. The fundamental principles of these algorithms and their applications in path planning, path optimization, localization, obstacle avoidance, and mission planning for UUVs are discussed in brief. Through an analysis of existing literature and case studies, the use of evolutionary optimization in improving the navigation efficiency, accuracy, and robustness of UUVs is highlighted. Additionally, current challenges were identified, and future research directions to advance the integration of evolutionary optimization techniques in UUV navigation systems are also discussed. Overall, this aims to provide insights into the potential of evolutionary optimization for addressing the navigation challenges faced by unmanned underwater vehicles and promoting advancements in underwater exploration and surveillance technologies.

Numerical simulation of scattering of TWIPS by underwater air bubble

Abstract The interaction of water with the subsurface of sea-ice leads to the generation of air bubbles. This heterogeneity results in additional challenges for unmanned underwater vehicles (UUV) conducting active detection in the upper water column of the Arctic Ocean. Under excitation by a high-amplitude acoustic source, asymmetric oscillations of the bubble are generated due to the high compressibility of gas, which further leads to acoustic scattering showing nonlinear characteristics. The corresponding signals may interfere with or even mask the echoes from potential targets, which are often linear scatterers, ultimately impairing the UUV’s active sensing capabilities under the sea-ice. Twin Inverted Pulse Sonar (TWIPS) is an underwater detection technique consisting of sending two pulse signals of equal amplitude but with opposing phases, benefiting from the difference in the responses of the bubble and target and the resulting echo signals for the eventual distinguishing. In this study, a numerical modeling of underwater bubbles for the acoustic study is developed by making use of both multiphysics and moving mesh features of Comsol. Numerical simulations were conducted to investigate the nonlinear feature of acoustic scattering from air bubbles. These results are then compared with analytical results for verification. Monochromatic signals and TWIPS at different frequencies are used as excitation signals, and the response of spherical air bubbles and the resulting scattered wave field are calculated, compared, and analyzed.

Path-Following Control of Unmanned Underwater Vehicle Based on an Improved TD3 Deep Reinforcement Learning

Path-Following Control of Unmanned Underwater Vehicle Based on an Improved TD3 Deep Reinforcement Learning

Collaborative task decision-making of multi-UUV in dynamic environments based on deep reinforcement learning

ABSTRACT The problem of collaborative task decision-making for unmanned underwater vehicles (UUVs) in an unknown dynamic ocean environment is investigated. In view of the unknown marine environment, a partially observable Markov decision process is designed to achieve partially observable path planning for multi-UUV. Aiming at the problems of long planning time, large amount of data, and insufficient multi-task decision-making capability in the process of multi-UUV underwater collaborative operation, a multi-UUV collaborative task decision-making model based on the multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm for the dynamic environment is constructed. The implementation of the centralised training with distributed execution (CT-DE) training framework enables each UUV to possess autonomous decision-making capability and ensures task safety under weak communication conditions. The pre-training phase shows that the MATD3 algorithm outperforms the multi-agent deep deterministic policy gradient (MADDPG) algorithm for training multi-UUV autonomous decision-making scenarios in dynamic environments. The simulation experiment results verify that the autonomous decision-making method based on the MATD3 algorithm can effectively solve the multi-UUV collaborative task decision-making problem in unknown dynamic environments, and the task process satisfies the requirements of real-time, safety and economy.

Hydrodynamic Analysis and Drag-Reduction Design of an Unmanned Underwater Vehicle Based on Computational Fluid Dynamics

Hydrodynamic Analysis and Drag-Reduction Design of an Unmanned Underwater Vehicle Based on Computational Fluid Dynamics

Highly Effective Electrolytes Toward High‐Performance Aluminum/Seawater Batteries

AbstractThe poor performance of metal/water batteries caused by self‐corrosion of anodes and low catalytic activity of cathodes has been a long‐standing challenge, greatly limiting their practical applications, in particular the underwater unmanned vehicle (UUV) application. We have fabricated an Al/seawater battery using simulated seawater with an appropriate pH and added with polyacrylic acid (PAA) as the electrolyte. This electrolyte simultaneously greatly retards self‐corrosion of the Al anode by in situ forming a PAA‐Al3+ complex film on it and increases the electrocatalytic activity toward the hydrogen evolution reaction by improving the electronic structure of Pt. When utilizing the multielement‐doped Al sheet as the anode and nickel foam supported loading‐amount‐optimized Pt/C catalyst as the cathode and adopting the developed new electrolyte, the obtained Al/H2O battery exhibits an energy density of 2271 Wh kg−1, which is the highest among those of all the reported batteries, and a power density of 20.87 mW cm−2, which outperforms all the reported metal/H2O batteries. This work not only develops a new type of high‐performance Al/H2O batteries for practical applications such as UUVs, but provides scientific insight into the design of superior electrolytes, which could be further extended for improving the performances of various metal batteries.

Embedded UUV conformal MEMS vector hydrophone

As the core equipment of Underwater acoustic detection, MEMS vector hydrophones are applied to Unmanned Underwater Vehicles (UUV), which can better detect underwater targets. Most of the MEMS vector hydrophones currently used are long-tube cylindrical, relatively large in volume and weight, which is not suitable for installation on UUV. The co-vibration vector hydrophone is small in size and weight, but it needs elastic suspension due to the requirements of the working principle. Therefore, according to the needs of practical engineering applications, this paper designs an Embedded UUV Conformal MEMS Vector Hydrophone (ECVH), establishes an encapsulation model through theoretical analysis, and uses COMSOL6.0 to simulate its acoustic performance. The sensitivity and directionality of the ECVH are verified in the standing wave barrel. When the test frequency is 630 Hz, the sensitivity of the vector channel is −182.3 dB, and the depth of the ' 8 ' direction concave point is greater than 35 dB. The sensitivity of the sound pressure channel is −182 dB, which is omnidirectional. Finally, it is mounted on UUV for preliminary signal acquisition verification test. The test results show that the ECVH can work normally on UUV, which provides a new idea for underwater small platform test.

(Electronics and Photonics Division Award) Research Advances Wide- and Ultrawide-Bandgap Power Switching Devices

Wide bandgap semiconductors such as SiC and GaN (WBG) and emerging ultrawide-bandgap materials such as Ga2O3, AlGaN, and Diamond (UWBG) represent the next-generation materials for high performance medium voltage and high voltage power switch technology. Such devices have a wide range of immediate Naval applications, including high-power satellite communications and radar, unmanned underwater and aerial vehicles, ship drive components, and hybrid vehicle inverters. Vertical SiC power device technology has matured rapidly over the past two decades, owing to advances in substrates, a fundamental understanding of epitaxial growth to eliminate performance-limiting defects, as well as device design breakthroughs. This has enabled breakthroughs in highly integrated module design for medium voltage power conversion with switching frequency >100 kHz. In parallel, lateral GaN-based high electron mobility transistor (HEMT) technology has been highly successful for RF power amplifiers and is well positioned to supersede GaAs-based microwave circuits. Recently, GaN-based vertical and lateral power devices have attracted significant interest due to promising device results coupled with progress in native substrate, epitaxial growth, and processing technology developments. This seminar will present an overview of the WBG/UWBG power device efforts at NRL. This research has four primary focus areas – 1) thermal management in III-N power device structures by diamond thin film integration, 2) characterization of substrate materials and homoepitaxial layers used for drift regions of power devices identify appropriate specifications and benchmark performance, 3) process module development, particularly for selective area doping by ion implantation, and 4) pilot production manufacturing of 1.2kV-class PiN diodes including reliability assessments to identify and mitigate performance limiting defects.

A Novel Hill Climbing-Golden Section Search Maximum Energy Efficiency Tracking Method for Wireless Power Transfer Systems in Unmanned Underwater Vehicles

Efficiency has always been one of the most critical indicators for evaluating wireless power transfer (WPT) systems. To achieve fast maximum energy efficiency tracking (MEET), this paper provides an innovative control method utilizing the hill climbing-golden section search (HC-GSS) method of an LCC-S compensated WPT system. The receiver side includes a buck-boost converter that regulates the output current or voltage to meet output requirements. In the meantime, the buck-boost converter on the transmitter side is managed by the HC-GSS approach for MEET by minimizing the input power under the premise of output stability. Compared with the conventional P&O method, the HC-GSS method can eliminate the trade-off between the oscillation and convergence rate because it is designed for different system stages. In this WPT system, there is no need for direct communication between the transmitter and receiver. Therefore, the system is potentially cheaper to implement and does not suffer from annoying communication delays, which are prevalent in underwater environments for unmanned underwater vehicles’ (UUV) WPT systems. Both the simulation and experiment results show that this method can improve the efficiency of the WPT system without communication. The proposed method remains valid with coupler displacement as it does not include the mutual inductance of the system.

The path planning algorithm for UUV based on the fusion of grid obstacles of artificial potential field

The artificial potential field approach (APFA) has the advantages of short computation time and easy implementation, widely used in path planning. The problems of local oscillation and susceptibility to local minima for APFA are considered in three dimensions (3D) path planning application of unmanned underwater vehicle (UUV), and the artificial potential field grid method (APFG) is proposed. The core principles of APFG include three key methods: firstly, obstacles are rasterized in a 3D map, and repulsive points are attached to the grid surface to alter the range of repulsive influence, thereby compensating for the issue of longer planned paths caused by the idealized modeling in traditional APFA; secondly, the repulsion function of the grid surface is modified to ensure that the potential field is parallel to the grid surface, thus addressing the problem of local oscillation in UUV path planning. Additionally, the introduction of virtual target points helps prevent the UUV from getting trapped in local optima, effectively overcoming the drawbacks of local oscillation and convergence to local minima in 3D path planning for UUV. The simulations show that APFG effectively overcomes the shortcomings of local oscillations and the tendency to fall into local minima for UUV in 3D path planning, and the path planning is smooth and efficient.

An Output Feedback Approach for 3-D Prescribed Time Stabilization Control of Unmanned Underwater Vehicles

An Output Feedback Approach for 3-D Prescribed Time Stabilization Control of Unmanned Underwater Vehicles

Sample-efficient and surrogate-based design optimization of underwater vehicle hulls

Physics simulations like computational fluid dynamics (CFD) are a computational bottleneck in computer-aided design (CAD) optimization processes. To overcome this bottleneck, one requires either an optimization framework that is highly sample-efficient, or a fast data-driven proxy (surrogate model) for long-running simulations. Both approaches have benefits and limitations. Bayesian optimization is often used for sample efficiency, but it solves one specific problem and struggles with transferability; alternatively, surrogate models can offer fast and often more generalizable solutions for CFD problems, but gathering data for and training such models can be computationally demanding. In this work, we leverage recent advances in optimization and artificial intelligence (AI) to explore both of these potential approaches, in the context of designing an optimal unmanned underwater vehicle (UUV) hull. For first approach, we investigate and compare the sample efficiency and convergence behavior of different optimization techniques with a standard CFD solver in the optimization loop. For the second approach, we develop a deep neural network (DNN) based surrogate model to approximate drag forces that would otherwise be computed via the CFD solver. The surrogate model is in turn used in the optimization loop of the hull design. Our study finds that the Bayesian Optimization—Lower Condition Bound (BO-LCB) algorithm is the most sample-efficient optimization framework and has the best convergence behavior of those considered. Subsequently, we show that our DNN-based surrogate model predicts drag force on test data in tight agreement with CFD simulations, with a mean absolute percentage error (MAPE) of 1.85%. Combining these results, we demonstrate a two-orders-of-magnitude speedup (with comparable accuracy) for the design optimization process when the surrogate model is used. To our knowledge, this is the first study applying Bayesian optimization and DNN-based surrogate modeling to the problem of UUV design optimization, and we share our developments as open-source software.

A 3-DOF caudal fin for precise maneuvering of thunniform-inspired unmanned underwater vehicles

Unmanned underwater vehicles (UUVs) will see increased use in scientific research, military operations and maintenance of industrial infrastructure. Many of these applications require that the vehicle possess a long range while retaining precise maneuvering or station-keeping capabilities. Both current UUVs and biological swimmers, often considered the basis for the next generation of UUVs, face a trade-off between the two characteristics. Here, we introduce a novel hybrid propeller concept that enables thunniform-inspired vehicles, which imitate nature’s most efficient swimmers, to also achieve high maneuverability. The propeller can produce enhanced three-dimensional kinematics of the caudal fin. An optimization procedure based on real-time experimental data is used to obtain the best kinematics to maneuver the vehicle, and a 4-step strategy is uncovered that results in a 49% increase in maneuverability with respect to conventional 2-D kinematics. The proposed mechanism is shown to be effective for a wide range of fin geometries and stiffness values.

Inductive Wireless Power Transfer for Autonomous Underwater Vehicles using Split Core Transformer and Resonant LLC Circuit

Unmanned Underwater Vehicles (UUVs) are pivotal for underwater exploration and maintenance. Autonomous Underwater Vehicles (AUVs), with their potential to reduce operational time and environmental impact, are gaining increased interest. However, they face important technological challenges, particularly in power supply. This study focuses into the application of Inductive Wireless Power Transfer (IWPT) for continuous AUV operation, employing tightly coupled split core transformers (SCT) designed for near-field power transfer. Robust isolation and alignment mechanisms are proposed to overcome the effects of seawater environment. An IWPT device with an SCT and resonant LLC circuit is simulated and experimentally tested. Finite Element Method studies highlight the advantage of isolating the device from the seawater environment, especially at high frequencies. LLC simulation and experimental results demonstrate an efficiency in power transfer of 93.2 % and 87.1 %, respectively, transferring up to 312 W. However, global efficiency of the experimental device drops to 76.4 %, highlighting the need for optimizations in circuit design.

Method of Forming and Algorithm of Saving a Photo Map of the Seafloor during its Photography by an Unmanned Underwater Vehicle

The paper proposes a new method and algorithm for creating a photo map of the seabed from photographs obtained from on-board photo and video systems of autonomous uninhabited underwater vehicles during fulfillment of their various missions. This photo map is formed from photographs of the seabed by sequentially overlapping them. At the same time, data on the position of the underwater vehicle received from the on-board navigation system at the time of receipt of each photograph is used to position each photo image on the generated photo map. And to rotate and scale these photographic images, data on the orientation of the underwater vehicle in the global coordinate system and its height above the bottom, also obtained from the on-board navigation system, are used. To provide quick access to the photo map, it is proposed to store it on an on-board data storage device in the form of a tile map, widely used when creating interactive geographical maps, for example, Google Maps. However, the formation of a tile map is usually carried out on the basis of all the already received photographic images, as is done with satellite images, which cannot be applied on board underwater vehicles when data from the photo-video system is received sequentially. Therefore, a special algorithm has been developed and implemented that generates a tile map during the movement of the underwater vehicle and updates only the area of the tile map in which the photo is currently being taken. This minimizes the computational load and ensures the formation of a photo map of the bottom using standard on-board computing devices. The conducted semi-natural experiments with data obtained as a result of performing real missions using the MMT-3000 underwater vehicle confirmed the high efficiency of using the developed method and algorithm.

Insights into Flexible Bioinspired Fins for Unmanned Underwater Vehicle Systems through Deep Learning.

The last few decades have led to the rise of research focused on propulsion and control systems for bio-inspired unmanned underwater vehicles (UUVs), which provide more maneuverable alternatives to traditional UUVs in underwater missions. Recent work has explored the use of time-series neural network surrogate models to predict thrust and power from vehicle design and fin kinematics. We expand upon this work, creating new forward neural network models that encapsulate the effects of the material stiffness of the fin on its kinematic performance, thrust, and power, and are able to interpolate to the full spectrum of kinematic gaits for each material. Notably, we demonstrate through testing of holdout data that our developed forward models capture the thrust and power associated with each set of parameters with high resolution, enabling highly accurate predictions of previously unseen gaits and thrust and FOM gains through proper materials and kinematics selection. As propulsive efficiency is of utmost importance for flapping-fin UUVs in order to extend their range and endurance for essential operations, a non-dimensional figure of merit (FOM), derived from measures of propulsive efficiency, is used to evaluate different fin designs and kinematics and allow for comparison with other bio-inspired platforms. We use the developed FOM to analyze optimal gaits and compare the performance between different fin materials. The forward model demonstrates the ability to capture the highest thrust and FOM with good precision, which enables us to improve thrust generation by 83.89% and efficiency by 137.58% with proper fin stiffness and kinematics selection, allowing us to improve material selection for bio-inspired fin design.