Autonomous Underwater Vehicle Operations Research Articles

A Control Method for Path Following of AUVs Considering Multiple Factors Under Ocean Currents

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%.

Enhancing underwater thruster anomaly detection with support vector glow encoding description

As the driving system of autonomous underwater vehicles (AUVs), the healthy operation of underwater thrusters is crucial to ensure the performance of AUVs. Due to the challenge of obtaining faulty samples during normal operation, anomaly detection of underwater thrusters is often carried out using only normal samples. To address this, Support Vector Glow Encoding Description (SVGED) is proposed for anomaly detection of underwater thrusters trained with only normal samples. Specifically, the deep features of the operational vibration signals collected from the underwater thruster are extracted by a deep convolutional autoencoder based on a glow model. The glow-encoded data are then used to identify the boundary of normal operation for the underwater thruster. When a new sample enters the trained SVGED model, it can be effectively identified as representing a normal or anomalous condition of the underwater thruster. The developed SVGED was evaluated using AUV experiments. When using the proposed SVGED method, the accuracy reaches 99.81%, Results show that the proposed method can effectively real time detect anomalies in the underwater thruster compared to other methods. It lays the foundation for ensuring the healthy operation of AUVs.

Research on Autonomous Underwater Vehicle Path Optimization Using a Field Theory-Guided A* Algorithm

Autonomous Underwater Vehicles (AUVs) have become indispensable tools in the fields of ocean exploration, resource exploitation, and environmental monitoring. Path planning and obstacle avoidance are crucial to improve the operational capabilities of AUVs. However, most algorithms focus only on macro-global or micro-local path planning and rarely address both problems simultaneously. This study extends the classical A* algorithm by integrating field theory principles. The resulting Field Theory Augmented A* (FT-A*) algorithm combines the constraints in the AUV’s dynamics and the threats posed by obstacles to ensure a safe navigation distance. The paths planned by the FT-A* algorithm were subsequently re-optimised in conjunction with Dubins curves, taking into account path smoothness and redundant node problems. Simulation experiments confirm that the improved algorithm can effectively help AUVs navigate safely around obstacles, which greatly improves navigation safety and increases the arithmetic power and navigation efficiency. The proposed FT-A* algorithm provides a robust solution for underwater path planning and demonstrates great practical value for AUV operation in complex marine environments.

Critical Parameter Analysis for Optimized Gliding Performance of Autonomous Underwater Vehicles

Critical Parameter Analysis for Optimized Gliding Performance of Autonomous Underwater Vehicles

Event-triggered adaptive fault-tolerant control with pre-specified performance for AUVs trajectory tracking

This paper investigates the problem of fault-tolerant control of the preset performance of autonomous underwater vehicles (AUVs) in case of external disturbances and actuator failures. To begin with, a specific new nonlinear auxiliary function with dual performance constraints is designed to establish the foundation for achieving prescribed performance in the closed-loop system. Subsequently, an event-triggered fault tolerant controller employing Nussbaum gain technique is introduced to accomplish the precision trajectory tracking control of the AUVs by predetermining control index. It is shown that the effect by resulting in actuator faults is on-line compensated by an adaptive estimator. Theoretical analyses demonstrate the boundedness of the signals of the designed closed-loop control system, and the Zeno phenomenon can be effectively avoided. Finally, simulation experiments are conducted to verify the effectiveness of the proposed methods.

Few-shot fault diagnosis of underwater thrusters based on semi-supervised prototypical network with SimAM attention and auxiliary classifier

Autonomous Underwater Vehicles (AUVs) are essential equipment for ocean development, and the propeller is the power component of an AUV. Fault diagnosis of the propeller is crucial to ensure the normal operation of AUVs. However, in practical engineering, the lack of sufficient labeled data poses a challenge for applying deep learning in propeller fault diagnosis. This paper proposes a semi-supervised prototypical network with SimAM attention and auxiliary classifier (SSPN-SimAM-AC) for few-shot fault diagnosis of underwater thrusters. Specifically, we design a rule for label assignment to screen for out-of-distribution samples. The SimAM attention mechanism is added to the feature extractor to enhance the capability of extracting sample features. Using a semi-supervised prototypical network, the prototypes are updated with unlabeled data, and based on this, an auxiliary classifier is designed to achieve better fault identification. The effectiveness of this method is validated using two sets of propeller fault data, and the experimental results demonstrate that the method can diagnose propeller faults of underwater vehicles under small sample conditions.

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.

Study on the interaction characteristics of hull-propeller-appendages of autonomous underwater vehicle based on unsteady conditions

In order to improve the hydrodynamic performance of autonomous underwater vehicle (AUV), a new method is proposed to study the AUV hull-propeller-appendages interaction in coupled mode, considering the coupling effect between the AUV hull, propeller and appendages, and to predict the self-thrust point. Based on the Reynolds mean Navier-Stokes (RANS) solver and the RNG k−ε turbulence model, the hydrodynamic performance of the propeller and AUV is predicted. The error between the predicted results and the experimental results is within 5%, which verified the correctness of the modeling method based on CFD. Finally, the thrust and torque generated by the open-water propeller and the propeller after the boat, the hydrodynamic characteristics of the naked AUV and the AUV with appendages, and the interaction between hull-propeller-appendages under different working conditions are studied. Experiments are carried out in Qiandao Lake, Zhejiang Province, China. The numerical simulation results of AUV at 1.76 m/s are compared with the experimental results of 1.5 m/s, and the correctness of the proposed interaction method is verified. The research results not only provide theoretical guidance for the design and operation of AUV, but also have important significance for improving the performance and reliability of AUV in complex underwater environments.

Autonomous Underwater Vehicle Path Planning Based on Improved Salp Swarm Algorithm

Aiming at the problem of path planning for autonomous underwater vehicle (AUV) to cope with the influence of obstacles and eddies in complex marine environments, a path planning method based on an improved salp swarm algorithm (ISSA) is proposed. Firstly, the motion model of the AUV and eddy current model are constructed, including the relationship between position, velocity, attitude, and control inputs. Secondly, the improved SSA is proposed, which introduces the Levy flight strategy to enhance the algorithm’s optimization seeking ability and adds a nonlinear convergence factor to enhance the convergence ability of the algorithm. The stability and robustness of the improved algorithm are verified by test functions. Finally, the ISSA is applied to AUV path planning, which optimizes the AUV travel distance, improves the search efficiency and accuracy, and avoids the local optimum of the algorithm. The ISSA enhances the adaptive ability and robustness of the algorithm by introducing a dynamic adjustment strategy and feedback mechanism. Experimental verification is carried out using a simulated marine environment. The results show that the ISSA is better than the traditional algorithm in terms of path length as well as algorithm stability, and can effectively improve the navigation performance of AUV.

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.

L*—An index for evaluating long range performance of autonomous underwater vehicles (AUVs)

Autonomous underwater vehicles (AUVs) have various applications in both military and civilian fields. A wider operation area and more complex tasks require better overall range performance of AUVs. However, until recently, there have been few unified criteria for evaluating the range performance of AUVs. In the present work, a unified range index, i.e., L*, considering the cruising speed, the sailing distance, and the volume of an AUV, is proposed for the first time, which can overcome the shortcomings of previous criteria using merely one single parameter, and provide a uniform criterion for the overall range performance of various AUVs. After constructing the expression of the L* index, the relevant data of 49 AUVs from 12 countries worldwide have been collected, and the characteristics of the L* range index in different countries and different categories were compared and discussed. Furthermore, by analyzing the complex factors affecting the range index, methods to enhance the L* range index value, such as efficiency enhancement and drag reduction, have been introduced and discussed. Under this condition, the work proposes a unified and scientific criterion for evaluating the range performance of AUVs for the first time, provides valuable theoretical insight for the development of AUVs with higher performance, and then arouses more attention to the application of the cutting-edge superlubricity technology to the field of underwater vehicles, which might greatly help to accelerate the coming of the era of the superlubricitive engineering.

Multi-Objective Optimization Study on the Coupling Mechanism of Underwater Wireless Power Transfer Systems

Magnetically coupled resonant wireless power transfer (MCR-WPT) technology offers longer effective transmission distances and higher efficiency compared to traditional charging methods, making it better suited to the prolonged and efficient operation of autonomous underwater vehicles. This paper first establishes a traditional mathematical model and then refines it while analyzing the variations in the self-inductance and mutual inductance of underwater coils. To further enhance the system’s performance, a multi-objective optimization of the coupling mechanism is conducted. An orthogonal experiment is employed to determine the effects of various influencing factors on the coils’ self-inductance and mutual inductance. Subsequently, an RBF neural network is used to create a regression prediction model based on the results of the orthogonal experiment. The NSGA-II algorithm is then applied for the multi-objective optimization of the coupling mechanism, resulting in a Pareto front solution set. The optimized efficiency is 93.35%, representing an approximately 6% improvement over the original system, with the power density increasing from 1.267×106 W/m3 before optimization to 4.782×106 W/m3 after optimization. Significant enhancement in system performance is achieved.

A Pruning and Distillation Based Compression Method for Sonar Image Detection Models

Accurate underwater target detection is crucial for the operation of autonomous underwater vehicles (AUVs), enhancing their environmental awareness and target search and rescue capabilities. Current deep learning-based detection models are typically large, requiring substantial storage and computational resources. However, the limited space on AUVs poses significant challenges for deploying these models on the embedded processors. Therefore, research on model compression is of great practical importance, aiming to reduce model parameters and computational load without significantly sacrificing accuracy. To address the challenge of deploying large detection models, this paper introduces an automated pruning method based on dependency graphs and successfully implements efficient pruning on the YOLOv7 model. To mitigate the accuracy degradation caused by extensive pruning, we design a hybrid distillation method that combines output-based and feature-based distillation techniques, thereby improving the detection accuracy of the pruned model. Finally, we deploy the compressed model on an embedded processor within an AUV to evaluate its performance. Multiple experiments confirm the effectiveness of our proposed method in practical applications.

Anti-Offset Multicoil Underwater Wireless Power Transfer Based on a BP Neural Network

Autonomous underwater vehicles (AUVs) are now widely used in both civilian and military applications; however, wireless charging underwater often faces difficulties such as disturbances from ocean currents and errors in device positioning, making proper alignment of the charging devices challenging. Misalignment between the primary and secondary coils can significantly impact the efficiency and power of the wireless charging system energy transfer. To address the issue of misalignment in wireless charging systems, this paper proposes a multiple transfer coil wireless power transfer (MTCWPT) system based on backpropagation (BP) neural network control combined with nonsingular terminal sliding mode control (NTSMC) to enhance further the system robustness and efficiency. To achieve WPT in the ocean, a coil shielding case structure was equipped. In displacement experiments, the proposed multi-transmitting coil system could achieve stable power transfer of 40 W and efficiency of over 78.5% within a displacement range of 8 cm. The system robustness was also validated. This paper presents a new AUV energy supply solution based on MTCWPT. The proposed MTCWPT system can significantly improve the navigation performance of AUVs.

A Gaussian-Process-Based Model Predictive Control Approach for Trajectory Tracking and Obstacle Avoidance in Autonomous Underwater Vehicles

To achieve the efficient and precise control of autonomous underwater vehicles (AUVs) in dynamic ocean environments, this paper proposes an innovative Gaussian-Process-based Model Predictive Control (GP-MPC) method. This method combines the advantages of Gaussian process regression in modeling uncertainties in nonlinear systems, and MPC’s constraint optimization and real-time control abilities. To validate the effectiveness of the proposed GP-MPC method, its performance is first evaluated for trajectory tracking control tasks through numerical simulations based on a 6-degrees-of-freedom, fully actuated, AUV dynamics model. Subsequently, for 3D scenarios involving static and dynamic obstacles, an AUV horizontal plane decoupled motion model is constructed to verify the method’s obstacle avoidance capability. Extensive simulation studies demonstrate that the proposed GP-MPC method can effectively manage the nonlinear motion constraints faced by AUVs, significantly enhancing their intelligent obstacle avoidance performance in complex dynamic environments. By effectively handling model uncertainties and satisfying motion constraints, the GP-MPC method provides an innovative and efficient solution for the design of AUV control systems, substantially improving the control performance of AUVs.

Research on model predictive control of autonomous underwater vehicle based on physics informed neural network modeling

In the rapidly evolving field of Autonomous Underwater Vehicles (AUVs), achieving precise control remains a critical endeavor. This study presents a pioneering integration of Model Predictive Control (MPC) with a Physics-Informed Neural Network (PINN), aiming to enhance control system precision and operational efficiency in AUVs. The efficacy of MPC lies in its adept handling of the intricate constraints and inherent nonlinear dynamics intrinsic to AUV systems. Concurrently, the PINN architecture incorporates the fundamental physical laws represented by Partial Differential Equations (PDEs), augmenting the predictive fidelity of the system. Firstly, this research implements the novel PINN-enhanced MPC framework for trajectory tracking and conducts a comparative evaluation against adaptive proportional-integral-derivative (PID) and Gaussian-process-based MPC controllers. This comparative analysis elucidates the advancements in control mechanisms attributable to the PINN integration. Furthermore, this study meticulously assesses the PINN-MPC's proficiency in navigating through static and dynamic obstacles within three-dimensional marine environments, a critical capability for AUV operations. Through extensive and meticulous simulations, the proposed approach demonstrates notable progress in overcoming environmental challenges and executing intricate operational tasks, such as obstacle avoidance, with heightened efficiency and dexterity. This research constitutes a substantial contribution to the theoretical advancement and elucidation of control systems in the AUV domain, bearing profound practical implications. It lays the foundation for the development of increasingly sophisticated, advanced, and reliable AUV missions, signifying a crucial advancement in the realms of underwater exploration and operational technology.

Оценка безопасности движения большого автономного необитаемого подводного аппарата

The problems of ensuring the safe motion of a large autonomous underwater vehicle (AUV) when it passes through sections with different environmental conditions are considered by the example of the development of the Northern Sea Route. An analysis was made of the influence of various environmental conditions - open and coastal water, fast ice and ice conditions - on the safety of the operation of a large AUV. An approach is proposed to assess the vehicle safety in various environments using a dynamic Bayesian network and conditional probability tables. To test the algorithms for controlling the the vehicle motion and the vehicle itself, a digital test site was developed in the form of a stand for simulating the functioning of the AUV. The development of digital twins in the form of software simulators of the vehicle devices and mechanisms made it possible to test control algorithms on the simulation stand both during normal operation of its systems and in the event of malfunctions due to functioning in various environments. The results obtained on a simulation stand for a large AUV operating on the farthest and most complicated part of the Northern Sea Route - the Vilkitski Strait, helped us to assess the safety of the vehicle operation and outline the ways to improve the safety by upgrading the algorithms for controlling the AUV motion in difficult environmental conditions.

AUV instantaneous velocity estimation method based on azimuth and Doppler shift

Autonomous underwater vehicle (AUV), as a new underwater operation platform, has been widely used in the field of ocean engineering. AUV underwater condition monitoring is an important guarantee for the safety and effective execution of AUV operations. To meet the requirement of real-time and high-precision underwater instantaneous velocity monitoring of AUV, this paper proposed an AUV instantaneous velocity estimation method. The method takes the target azimuth and Doppler shift as the observation information. It comprehensively uses the geometric relationship among the target azimuth, heading angle, radial velocity angle, and the physical relationship between Doppler and radial velocity to construct an AUV instantaneous velocity estimation model based on distributed dual acoustic observation nodes. We derive the analytical solution of AUV speed and heading angle and analyze the influence of each observation error on the estimation precision of the algorithm. The research and practical data processing results show that the proposed method can effectively estimate the instantaneous velocity of AUV. Compared with the traditional method based on position difference, it has the advantages of high estimation precision, is not limited by frame interval, and is adaptive to many kinds of motion modes. When AUV moves in a straight line at a uniform speed, the precision of speed estimation and heading angle estimation of the algorithm proposed in the paper is 0.506 m/s and 6.146°, which is equivalent to that of the traditional method based on position difference with 50 frame intervals.

Ultrasound-Powered Wireless Underwater Acoustic Identification Tags for Backscatter Communication.

Autonomous underwater vehicle (AUV) operations are limited by currently achievable underwater localization and navigation solutions; hence, the development of low-cost and passive (i.e., operable without an active power supply) acoustic underwater markers (or tags) can provide accurate localization information to AUVs improving their situational awareness, especially when operating in small scales or confined missions. This work presents an acoustic identification (AID) tag that can be powered wirelessly with ultrasonic power transfer from a remote acoustic source (e.g., mounted on an interrogating AUV) and provide localization information using backscatter communication. The AID tag harvests energy from the acoustic signal generated from the AUV and communicates by modulating the reflected signals from an embedded piezoelectric transducer. A scaled broadband AID tag prototype that achieves concurrent acoustic energy harvesting (tuned around 1.3 MHz) and backscatter communication (in wider frequency band 600 and 800 kHz) using frequency-domain multiplexing is implemented using a custom broadband impedance matching-based transducer design approach. During concurrent power and data operation, this prototype AID tag achieves data rates up to 200 kb/s using amplitude- and frequency-based modulation communication. The use of broadband schemes to achieve robust communications in low SNR (tested here down to -6 dB) is also demonstrated using linear frequency-modulated data carriers. Finally, the extension to full-scale devices of this AID tag concept and potential applications for short-range AUV routing and navigation such as homing and docking are discussed.

Triangle codes and tracer lights based absolute positioning method for terminal visual docking of autonomous underwater vehicles

PurposeDocking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.Design/methodology/approachThis paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.FindingsThe authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.Originality/valueThis fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.