Autonomous Underwater Vehicle Operations Research Articles

The depth-keeping performance of autonomous underwater vehicle advancing in waves integrating the diving control system with the adaptive fuzzy controller

This study aims to develop an experimental-based adaptive fuzzy controller for the diving control system (DCS) of autonomous underwater vehicle (AUV) to ensure high stability and rapid integration. The DCS is designed with a depth sensor and an attitude and heading reference system (AHRS) to sense the attitude vector and the current depth of AUV in real-time as the control inputs of attitude close-loop and to calculate yaw and pitch controls of AUV. Therefore, the DCS can integrate depth-keeping and attitude stability controls into the AUV. To verify the robustness and adaptability of the DCS with adaptive fuzzy control, the DCS with universal fuzzy control (A-type DCS), improved fuzzy control (B-type DCS), and adaptive fuzzy control (C-type DCS) have been analyzed. The experimental analysis indicates that the C-type DCS features higher robustness to external force interference than the other types of DCS. The present study establishes an adaption function based on depth-keeping control experiments and demonstrates robustness of the adaptive fuzzy control DCS to environmental disturbances. Consequently, the proposed adaptive fuzzy control DCS is a model-free design that resolves the problem of depth-keeping and attitude stability controls of AUV with unknown hydrodynamic coefficients, rudder dynamics and time-varying external forces.

Automated Tuning of Nonlinear Kalman Filters for Optimal Trajectory Tracking Performance of AUVs

The performance of navigation algorithms significantly determines the trajectory tracking accuracy of the guidance, navigation, and control (GNC) system of an autonomous underwater vehicle (AUV). In closed-loop operation, the interaction among path planning, control, and navigation plays a crucial role in the tracking accuracy of the overall GNC system. A Doppler velocity log (DVL) is often used for AUVs to measure velocity over the ground, positively affecting the closed-loop tracking error. However, a DVL may not be installed in miniaturized AUVs due to limited space and energy. In this paper, a navigation filter for an underactuated miniature AUV (nanoAUV) is considered that is mainly based on acoustic localization using a novel highly-miniaturized ultra-short baseline (USBL) system and a depth pressure sensor. The nanoAUV is being developed for subglacial lake exploration. We compare two unscented Kalman filters (UKF) with different prediction models - the classical strapdown inertial navigation systems (SINS) model and a hydrodynamic motion model (HMM). To enable a fair comparison, filter parameters are auto-tuned with Bayesian optimization (BO) for open-and closed-loop performance, which is novel in AUV navigation. The results indicate that BO performs similarly to particle swarm optimization (PSO) regarding sample efficiency for the proposed problem. To quantify the GNC tracking performance, we use extensive Monte Carlo simulations. Results suggest that with BO-tuned navigation filter parameters, the median tracking error is reduced by up to 50% compared to default parametrization.

Jaya optimization algorithm implemented on a new novel design of 6-DOF AUV body: a case study

The novel Jaya Optimization Algorithm (JOA) was utilized in this research to evaluate the efficiency of a new novel design of Autonomous Underwater Vehicle (AUV). The Three Proportional Integral Derivative (PID) controllers were used to obtain the optimum output for the AUV Trajectory, which can be considered as a main side of the research for solving the AUV Performance. The optimization technique has been developed to solving the motion model of the AUV in order to reduce the rotations of trajectory for the AUV 6-DOF Body in the axis’s in x, y and z for the overall positions, velocity were measured in 50 seconds as the velocity compared with the simulated time, and to execute the optimum output for the dynamic kinematics model based on the Novel Euler-6 DOF AUV Body Equation implemented on MATLAB R2021a Version.

Performance evaluation and heat transfer mechanism for battery thermal management of autonomous underwater vehicles based on phase change material cooling

Battery thermal management is of great significance to enhance the comprehensive performance of autonomous underwater vehicles(AUVs). However, few relevant studies have been conducted on the AUV’s battery thermal management (BTM). In this work, the phase change material (PCM) cooling was proposed for AUV’s BTM with taking into account natural convection. The temperature behavior of the battery and the melting behavior of PCM were studied under different parameters including thickness of PCM and eccentricity between PCM and battery. In addition, the enhanced heat transfer mechanism of the PCM melting process was further revealed based on the temperature deviation index. More importantly, the dimensionless temperature control rate (TCR) factor and the dimensionless heat storage rate (HSR) factor were introduced to quantitatively evaluate and optimize the eccentricity on the enhancement performance. The results show that PCMs have an excellent performance in the battery thermal management of AUVs. Unlike the traditional view, the whole PCM melting process was further subdivided into three stages according to the thermal energy storage mode or heat transfer types. Increasing the eccentricity can accelerate the melting of the PCM, while the reduced time of complete PCM melting was not linearly correlated with the eccentricity. The optimal eccentricity between PCM and battery was 10 mm, i.e., the eccentricity ratio ε = 0.625. The temperature control performance rate and heat storage performance rate were enhanced by 29.4 % and 5.7 % at e = 10 mm, respectively. These findings can provide guidelines for accurate design of the PCM cooling system for the AUV’s battery thermal management.

Review of Obstacle Detection Systems for Collision Avoidance of Autonomous Underwater Vehicles Tested in a Real Environment

The high efficiency of obstacle detection system (ODS) is essential to obtain the high performance of autonomous underwater vehicles (AUVs) carrying out a mission in a complex underwater environment. Based on the previous literature analysis, that include path planning and collision avoidance algorithms, the solutions which operation was confirmed by tests in a real-world environment were selected for this paper consideration. These studies were subjected to a deeper analysis assessing the effectiveness of the obstacle detection algorithms. The analysis shows that over the years, ODSs being improved and provide greater detection accuracy that results in better AUV response time. Almost all analysed methods are based on the conventional approach to obstacle detection. In the future, even better ODSs parameters could be achieved by using artificial intelligence (AI) methods.

Finite-Time Extended State Observe Based Fault Tolerant Control for Autonomous Underwater Vehicle with Unknown Thruster Fault

This paper investigates the problem of fault tolerant control (FTC) for autonomous underwater vehicles (AUVs) with multiple thrusters in the presence of current disturbances, thruster faults, and modelling uncertainty. This paper focuses on the problems of reducing the energy consumption caused by the chattering of control signals and improving the tracking accuracy of an AUV operating in deep-sea environments. In view of the problem of large energy consumption in some other methods, a fault tolerant control method for multiple-thruster AUVs based on a finite-time extended state observer (FTESO) is proposed. More specifically, a FTESO based on an integral sliding mode surface is designed to estimate the generalized uncertainty compounded using current disturbances, thruster faults, and modelling uncertainty. The fast finite-time uniformly ultimately bounded stability of the proposed FTESO is analyzed. Then, based on the estimated value of FTESO, an FTC method based on non-singular fast terminal sliding mode surfaces is developed for AUVs. The finite-time convergence of the closed-loop control system is proved theoretically. In this design, two different sliding mode surfaces are used to design FTESO and FTC, in order to avoid the appearance of singularities. Moreover, a parameter adjustment method is designed to improve tracking accuracy. Finally, comparative numerical simulations show that the proposed control scheme is effective at reducing energy consumption and improving tracking accuracy.

A fault diagnosis method with multi-source data fusion based on hierarchical attention for AUV

Multi-source data fusion is an important method to improve the performance of Autonomous Underwater Vehicle (AUV) fault diagnosis. However, most of the current fault diagnosis methods are based on a single data source or treat multi-source data as single. Firstly, we demonstrate the necessity of multi-source data fusion and propose a universal data hierarchy. Then, a hierarchical attention based multi-source data fusion method is proposed for fault diagnosis (HAMFD). The method consists of an encoder–decoder network, a fusion network stacked with encoders and attention mechanisms, and a fault recognition method based on attention distribution. The fusion network uses the encoder and hierarchical attention to extract the deep features, and fuse the features hierarchically. We use the multi-layer attention distribution to explain the fault evaluation and realize fault recognition. A random mask fusion strategy is designed for redundancy and a feature orthogonalization method is proposed for the strong coupling among multiple data sources. The proposed method is validated on the monitoring data of Qianlong-2 AUV obtained during the sea trial in the South China Sea. The fault detection rate is more than 98%, the recognition rate is about 100% for strong faults, and more than 90% for other faults.

AUV Path Planning Algorithm for Terrain Aided Navigation

Terrain aided navigation (TAN) technology can yield accurate navigation results in long-endurance underwater operation of autonomous underwater vehicles (AUVs). Most TAN research focus on the high-precision terrain matching algorithms. However, terrain matching algorithms estimate the position of the vehicle by observing a unique combination of features in the terrain, making it difficult to locate a vehicle with flat terrain. This paper presents a path planning method for AUV seabed TAN process to decrease positioning errors by avoiding flat areas. In this paper, a TAN path planning algorithm based on sector search is proposed. Parameters of the sector search algorithm are determined through theoretical analysis, which means the generated path can stably provide accurate TAN location results. Meanwhile, a control coefficient is proposed to maximize the terrain information contained in the sector searching area, bring out a high success rate of AUV positioning. The TAN performance of the paths generated using the proposed path planning method was verified in simulations, which show that the TAN can yield accurate positioning results stably on the path planned.

Expediting recovery of autonomous underwater vehicles in dynamic mission environments: A system‐of‐systems challenge for underwater warfare

AbstractAs autonomous underwater vehicle (AUV) adoption increases, operators demand advanced behaviors from commercial off‐the‐shelf systems. However, new behaviors can often only be deployed operationally once assured. This paper overviews research into expediting recovery to an operator's vessel through a custom homing behavior, demonstrating technology advancement in conjunction with test and evaluation activities. Advanced docking infrastructure and frameworks are still under development, yet current AUV operations require rapid and reliable recovery when mission factors change. Homing is achieved with a directional acoustic transponder providing range and bearing data to the AUV from the operator's vessel. A converted measurement Kalman filter processes range and bearing data that generates dynamic waypoints for the AUV through MOOS‐IvP as a backseat driver; a universal approach and filtering that is unique from prior AUV research. Results from simulations and field trials were analysed through a modular and experimental Test & Evaluation framework that was adopted specifically to help verify and validate the new AUV behavior, including systematic variations in recovery boat manoeuvres. The process includes documented use for the first time in AUV research of combinatorial screening (high throughput testing) and an average standardized residual metric to focus development early, encourage constructive iteration and build operational and engineering trust. Consistent homing was demonstrated with localization within 0.3 m and homing within 1.8 m of a moving digital acoustic transponder.

Data-driven optimization design of a novel pressure hull for AUV

The design of the pressure hull, one of the most important components, has great significance for improving the performance of autonomous underwater vehicles (AUVs). In this paper, a novel ring-stiffened arched cylindrical (RAC) hull is presented based on the traditional ring-stiffened cylindrical hull, which has the advantages of smooth outline, large compressibility, and small buoyancy factor. Then, a data-driven approach for efficiently designing the pressure hull with an irregular structure is proposed based on the finite element simulation, and its framework is introduced in detail. With the data-driven approach, the optimization design of the RAC structure for AUV is performed, which considers the effect of temperature variation on the deformation. The optimization results indicate that the mass of the pressure hull can be reduced by 10.3% by replacing the traditional ring-stiffened cylindrical hull with the RAC hull proposed in this paper. Finally, some pressure tests are carried out to verify the proposed approach, which is also appropriate for designing irregular structures in other engineering fields.

Mission Management Technique for Multi-sensor-based AUV Docking

This study presents a mission management technique that is a key component of underwater docking system used to expand the operating range of autonomous underwater vehicle (AUV). We analyzed the docking scenario and AUV operating environment, defining the feasible initial area (FIA) level, event level, and global path (GP) command to improve the rate of docking success and AUV safety. Non-holonomic constraints, mounted sensor characteristic, AUV and mission state, and AUV behavior were considered. Using AUV and docking station, we conducted experiments on land and at sea. The first test was conducted on land to prevent loss and damage of the AUV and verify stability and interconnection with other algorithms; it performed well in normal and abnormal situations. Subsequently, we attempted to dock under the sea and verified its performance; it also worked well in a sea environment. In this study, we presented the mission management technique and showed its performance. We demonstrated AUV docking with this algorithm and verified that the rate of docking success was higher compared to those obtained in other studies.

Under-ice acoustic navigation using real-time model-aided range estimation.

The long baseline (LBL) underwater navigation paradigm relies on the conversion of travel times into pseudoranges to trilaterate position. For real-time autonomous underwater vehicle (AUV) operations, this conversion assumes an isovelocity sound speed. For re-navigation, computationally and/or labor-intensive acoustic modeling may be employed to reduce uncertainty. This work demonstrates a real-time ray-based prediction of the effective sound speed along a path from source to receiver. This method was implemented for an AUV-LBL system in the Beaufort Sea in an ice-covered and a double-ducted propagation environment. Given the lack of Global Navigation Satellite Systems (GNSS) data throughout the vehicle's mission, the pseudorange performance is first evaluated on acoustic transmissions between GNSS-linked beacons. The mean real-time absolute range error between beacons is roughly 11 m at distances up to 3 km. A consistent overestimation in the real-time method provides insights for improved eigenray filtering by the number of bounces. An operationally equivalent pipeline is used to reposition the LBL beacons and re-navigate the AUV, using modeled, historical, and locally observed sound speed profiles. The best re-navigation error is 1.84 ± 2.19 m root mean square. The improved performance suggests that this approach extends the single meter accuracy of the deployed GNSS units into the water column.

Towards an online risk model for autonomous marine systems (AMS)

Considering that few or no human operators are directly involved in the operation of Autonomous Marine Systems (AMS), an online risk model is necessary to enhance the intelligence of the AMS, its situation awareness, and decision-making. The current study identifies the criteria for an online risk model for AMS, which can be used to assess its validity and effectiveness.Taking an under-ice Autonomous Underwater Vehicle (AUV) operation as an example, the current work investigates how different risk analysis methods, namely the Preliminary Hazard Analysis (PHA), the Systems Theoretic Process Analysis (STPA), and Procedural Hazard and Operability Analysis (HAZOP), contribute to fulfilling the different criteria for online risk modeling of AMS. The analysis results show that STPA can be considered a good basis for developing an online risk model due to its relatively good coverage of the identified evaluation criteria, especially its ability to handle the interaction between system and software failure. In addition, considering some shortcomings of using STPA and the changing role of human operators in the AMS operation, PHA and Procedural HAZOP can be used as complementary tools. It is expected that the analysis results and conclusions can be adapted to other AMS as well.

Research on accurate modeling of hydrodynamic interaction forces on AUVs operating in tandem

The hydrodynamic interaction force between autonomous underwater vehicles (AUVs) operating in tandem can adversely affect the hydrodynamic prediction of individual AUVs and the formation control of the tandem fleet. Accurate modeling of hydrodynamic interaction forces is crucial to the model-based controller design of AUVs. In this paper, we innovatively propose a modeling method that can describe the hydrodynamic interaction relationship accurately and continuously (when the spacing between adjacent AUVs changes). First, an accurate hydrodynamic model for individual AUVs in tandem fleets is established by modifying the typical model through the consideration of memory effects. Second, a model of hydrodynamic interaction forces is derived by subtracting hydrodynamic forces acting on a single AUV operating independently from those acting on an AUV operating in tandem. To enable this model to continuously describe the hydrodynamic interaction relationship when the spacing changes, this model is further derived by taking the spacing as an independent variable based on the parameter conversion method. Finally, planar motion mechanism (PMM) tests are carried out using the computational fluid dynamics (CFD) method to verify the accuracy and continuity of the proposed modeling method. The results show that compared with the typical model, the average error of hydrodynamic forces predicted by the proposed model is reduced by 5.36%, which verifies the feasibility and effectiveness of the proposed modeling method.

AURORA, a multi-sensor dataset for robotic ocean exploration

The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, this paper presents a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks. This dataset combines AUV navigation data, sidescan sonar, multibeam echosounder data and seafloor camera image data, and associated sensor acquisition metadata to provide a detailed characterisation of surveys carried out by the National Oceanography Centre (NOC) in the Greater Haig Fras Marine Conservation Zone (MCZ) of the U.K in 2015.

Real-time implementation of an online path replanner for an AUV operating in a dynamic and unexplored environment

This study describes the implementation of an online path planner in an autonomous underwater vehicle (AUV) system by using an open-source system architecture, MOOS-IvP. The path planner employed a path replanning scheme and the selective differential evolution quantum-behaved particle swarm optimization (SDEQPSO) algorithm. The implementation was based on a modular framework to ensure the robustness of the path replanner during a mission. The performance of the path replanner was evaluated and verified under stochastic processes in hardware-in-the-loop (HIL) tests, in which the replanner interacted with the onboard controllers and actuators of an Explorer AUV. The experimental results showed the path replanner can be run seamlessly with the hardware onboard an Explorer AUV in real time to generate and continuously refine a safe and feasible path for a dynamic and unexplored environment.

TRANSIENT ANALYSIS OF HYDRODYNAMIC INTERACTION EFFECTS ON AN AUTONOMOUS UNDERWATER VEHICLE IN PROXIMITY OF A MOVING SUBMARINE

When an Autonomous Underwater Vehicle (AUV) is operating close to a moving submarine, the hydrodynamic interaction between the two vehicles can prevent the AUV from maintaining its desired trajectory. This can lead to mission failure and, in extreme cases, collision with the submarine. This paper outlines the transient interaction influence on the hydrodynamic coefficients of an AUV operating in close proximity and in relative motion to a larger moving submarine. The effects of relative motion on the interaction behaviour were investigated via two manoeuvres, i.e. the AUV overtaking and being overtaken by the submarine at different relative forward velocities and lateral distances. The results presented are from a series of Computational Fluid Dynamics (CFD) simulations on axisymmetric AUV and submarine hull forms, with validation of the CFD model carried out through scaled captive model experiments. The results showed that an AUV becomes less susceptible to the interaction influence when overtaking at speeds higher than the submarine. The implications of the interaction influence on the AUV’s ability to safely manoeuvre around the submarine are also discussed.

A Review of Risk Analysis Research for the Operations of Autonomous Underwater Vehicles

Risk analysis for autonomous underwater vehicles (AUVs) is essential to assist decision making for safer operations. This study aims to provide a systematic review of risk analysis research to enhance the safety performance of AUVs. Forty-two domain articles were retrieved and analyzed. Critical risk factors and causal relationships of AUV operations were identified. A comparative analysis of evolving methods and models was performed by categorizing them as qualitative, semi-quantitative, and quantitative. Future trends of research in this field were also outlined. The study observes that as AUV technologies gradually mature, environmental factors, human factors, and their interactive impacts are gathering more attention. Quantitative risk analysis methods have recently played a key role in improving the accuracy and handling the uncertainties of risk estimation. The study recommends devoting efforts to dynamic risk analysis, addressing limited historical data, intelligent risk analysis, and multi-vehicles risk analysis for future works. This study is expected to help AUV stakeholders gain comprehensive insights into fundamental concepts and evolving methods for risk analysis of AUVs. At the same time, it is expected to highlight future directions to bridge existing gaps.

Reimagining Autonomous Underwater Vehicle Charging Stations with Wave Energy

The vast capabilities of autonomous underwater vehicles (AUVs)—such as in assisting scientific research, conducting military tasks, and repairing oil pipelines—are limited by high operating costs and the relative inaccessibility of power in the open ocean. Wave powered AUV charging stations may address these issues. With projected increases in usage of AUVs globally in the next five years, AUV charging stations can enable less expensive and longer AUV missions. This paper summarizes the design process and investigates the feasibility of a wave powered, mobile AUV charging station, including the choice of a wave energy converter and AUV docking station as well as the ability to integrate the charging station with an autonomous surface vehicle. The charging station proposed in this paper meets many different commercial, scientific, and defense needs, including continuous power availability, data transmission capabilities, and mobility. It will be positioned as a hub for AUV operations, enabling missions to run autonomously with no support ship. The potential market for this design is very promising, with an estimated $1.64 million market size just for AUV technologies by 2025.

The future of autonomous underwater vehicle control

The future of autonomous underwater vehicle control