Autonomous Underwater Vehicle Navigation Research Articles

High frame rate acoustic navigation of AUV based on range ambiguity suppression

Underwater acoustic navigation is extensively utilized across many industries due to its exceptional dependability and continuous real-time functionality. Due to the swift advancement of technology in recent years, the velocity of autonomous underwater vehicles (AUVs) has progressively increased. A high frame rate is required for the underwater acoustic navigation system in order to precisely obtain the trajectory of the AUV. Nevertheless, the discrepancy lies in the fact that the high frame rate and the unambiguous measurement could be seen as contradictory. Higher frame rates greatly raise the probability of range ambiguity, leading to various potential pseudo solutions that exacerbate the difficulty of accurate positioning and noticeably impact navigation performance. To address the aforementioned issue, a high frame rate acoustic navigation technique that relies on the suppression of range ambiguity has been studied in this paper. Initially, the relationship between measurements were investigated, specifically the time delay and Doppler shift, and the confusing periodic leap. Subsequently, the coupling model was employed to develop a high-precision acoustic navigation model, taking into account the statistical characteristics of measurements. We also examined a high-precision solving approach. Ultimately, the proposed method was validated through both simulation analysis and field testing to confirm its practicality and efficiency. The results demonstrate that it can attain a high level of accuracy in resolving the ambiguous problem and achieve high-precise acoustic navigation.

Cooperative motion planning and control of a group of autonomous underwater vehicles using twin-delayed deep deterministic policy gradient

The cooperative execution of complex tasks can lead to desirable outcomes and increase the likelihood of mission success. Nevertheless, coordinating the movements of multiple autonomous underwater vehicles (AUVs) in a collaborative manner is challenging due to nonlinear dynamics and environmental disturbances. The paper presents a decentralized deep reinforcement learning algorithm for AUVs that enables cooperative motion planning and obstacle avoidance. The goal is to formulate control policies for AUVs, empowering each vehicle to create its optimal collision-free path through adjustments in speed and heading. To ensure safe navigation of multiple AUVs, COLlision AVoidance (COLAV) plays a crucial role. Therefore, the implementation of a multi-layer region control strategy enhances the AUVs’ responsiveness to nearby obstacles, leading to improved COLAV. Furthermore, a reward function is formulated to consider four criteria: path planning, obstacle- and self-COLAV, as well as feasible control signals, with the aim of strengthening the proposed strategy. Notably, the devised scheme demonstrates robustness against disturbances A comparative study is conducted with the well-established Artificial Potential Field (APF) planning method. The simulation results indicate that the proposed system effectively and safely guides the AUVs to their goals and exhibits desirable generalizability.

A novel multi-source navigation algorithm based on factor graph in complex underwater environments of polar regions

Autonomous underwater vehicle (AUV) is an unmanned, cableless, self-propelled, autonomous navigation and control vehicle that maneuvers under unmanned or minimally monitored and intervened conditions. To solve the problem of increasing navigation accuracy error caused by time-varying measurement noise of AUV auxiliary sensors in complex underwater environments of polar regions, a multi-source navigation algorithm for AUV based on improved factor graph is proposed. The algorithm first adds a confidence function to the standard factor graph algorithm, which is used to adjust the covariance matrix of the navigation sensor to suppress the problem of time-varying measurement noise. Then, a sliding window is added to the factor graph algorithm and only nodes within the sliding window are optimized to ensure real-time performance. Finally, considering that inertial navigation has high accuracy in a short period of time, inertial navigation iterations are performed on nodes that are about to be marginalized within the sliding window to improve navigation accuracy. The analyses of the simulation experiment results show that the algorithm can effectively cope with the time-varying problem of measurement noise and ensure the accuracy and robustness of the AUV navigation system in the complex marine environment.

Improved ant colony optimization for safe path planning of AUV

In order to address the autonomous underwater vehicle navigation challenge for dam inspections, with the goal of enabling safe inspections and reliable obstacle avoidance, an improved smooth Ant Colony Optimization algorithm is proposed for path planning. This improved algorithm would optimize the smoothness of the path besides the robustness, avoidance of local optima, and fast computation speed. To achieve the goal of reducing turning time and improving the directional effect of path selection, a corner-turning heuristic function is introduced. Experimental simulation results show that the improved algorithm performs best than other algorithms in terms of path smoothness and iteration stability in path planning.

Underwater terrain-matching algorithm based on improved iterative closest contour point algorithm

Although an autonomous underwater vehicle (AUV) is noted for its good autonomy, concealment and anti-interference ability, errors in its inertial navigation system (INS) inevitably increase over time, leading to positional failure during long-term voyages. Terrain-assisted navigation can help the INS to correct its position. The traditional iterative closest contour point (ICCP) achieves high matching accuracy when the initial position error of the INS is small, but is prone to mismatching when the initial error is large. This study combines ICCP with particle swarm optimization (PSO) to overcome this problem. First, the global optimization ability of PSO is improved by changing the acceleration factor and introducing an artificial bee colony (ABC) onlooker bee greedy search (ABC- ωAPSO). Second, the Euclidean distance of ICCP is replaced by the Mahalanobis distance to abate the influence of system error on the matching accuracy. Finally, the initial position error is reduced by rough matching using the ABC- ωAPSO, which has global optimization capability. Next, fine matching is performed by ICCP. This two-step process resolves the sensitivity problem of ICCP to the initial position error. The experimental results revealed a good matching effect after the double-matching procedure. When the initial INS errors were 0.55′ to the east and 0.55′ to the north, the matching error was reduced to 89.3 m, suggesting that the approach can realize autonomous passive navigation of AUVs.

Optimizing AUV Navigation Using Factor Graphs with Side-Scan Sonar Integration

For seabed mapping, the prevalence of autonomous underwater vehicles (AUVs) employing side-scan sonar (SSS) necessitates robust navigation solutions. However, the positioning errors of traditional strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) systems accumulated significantly, further exacerbated by DVL’s susceptibility to failure in complex underwater conditions. This research proposes an integrated navigation approach that utilizes factor graph optimization (FGO) along with an improved pre-integration technique integrating SSS-derived position measurements. Firstly, the reliability of SSS image registration in the presence of strong noise and feature-poor environments is improved by replacing the feature-based methods with a Fourier-based method. Moreover, the high-precision inertial measurement unit (IMU) pre-integration method could correct the heading errors of SINS significantly by considering the Earth’s rotation. Finally, the AUV’s marine experimental results demonstrated that the proposed integration method not only offers improved SSS image registration and corrects initial heading discrepancies but also delivers greater system stability, particularly in instances of DVL data loss.

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.

A High-Precision Positioning Method for Autonomous Underwater Vehicles with Communication Delays

In underwater navigation of autonomous underwater vehicles (AUVs), communication delays frequently occur, leading to a reduction in positioning accuracy. To mitigate this challenge, this work introduces a novel method for relative angle correction, aiming to reconstruct measurement information. Initially, Doppler measurement data are assimilated into the reconstruction of measurement equations to determine the relative angle between the AUV and the observatory. Subsequently, the obtained angle information is integrated into the Extended Kalman Filter (EKF) for the reconstruction of measurement equations. The proposed method effectively reduces positioning errors caused by hydroacoustic communication delays, consequently enhancing AUV positioning accuracy. The efficacy of the proposed method is demonstrated through a simulation study. Simulation results reveal that the incorporation of Doppler angle correction in the reconstructed measurement information method significantly decreases the localization error by approximately 50% compared to EKF and by around 20% compared to the method lacking angle correction.

Experimental Analysis of Deep-Sea AUV Based on Multi-Sensor Integrated Navigation and Positioning

The operation of underwater vehicles in deep waters is a very challenging task. The use of AUVs (Autonomous Underwater Vehicles) is the preferred option for underwater exploration activities. They can be autonomously navigated and controlled in real time underwater, which is only possible with precise spatio-temporal information. Navigation and positioning systems based on LBL (Long-Baseline) or USBL (Ultra-Short-Baseline) systems have their own characteristics, so the choice of system is based on the specific application scenario. However, comparative experiments on AUV navigation and positioning under both systems are rarely conducted, especially in the deep sea. This study describes navigation and positioning experiments on AUVs in deep-sea scenarios and compares the accuracy of the USBL and LBL/SINS (Strap-Down Inertial Navigation System)/DVL (Doppler Velocity Log) modes. In practice, the accuracy of the USBL positioning mode is higher when the AUV is within a 60° observation range below the ship; when the AUV is far away from the ship, the positioning accuracy decreases with increasing range and observation angle, i.e., the positioning error reaches 80 m at 4000 m depth. The navigational accuracy inside and outside the datum array is high when using the LBL/SINS/DVL mode; if the AUV is far from the datum array when climbing to the surface, the LBL cannot provide accurate position calibration while the DVL fails, resulting in large deviations in the SINS results. In summary, the use of multi-sensor combination navigation schemes is beneficial, and accurate position information acquisition should be based on the demand and cost, while other factors should also be comprehensively considered; this paper proposes the use of the LBL/SINS/DVL system scheme.

Multi-Objective Path Planning of Autonomous Underwater Vehicles Driven by Manta Ray Foraging

Efficient navigation of multiple autonomous underwater vehicles (AUVs) plays an important role in monitoring underwater and off-shore environments. It has encountered challenges when AUVs work in complex underwater environments. Traditional swarm intelligence (SI) optimization algorithms have limitations such as insufficient path exploration ability, susceptibility to local optima, and difficulty in convergence. To address these issues, we propose an improved multi-objective manta ray foraging optimization (IMMRFO) method, which can improve the accuracy of trajectory planning through a comprehensive three-stage approach. Firstly, basic model sets are established, including a three-dimensional ocean terrain model, a threat source model, the physical constraints of AUV, path smoothing constraints, and spatiotemporal coordination constraints. Secondly, an innovative chaotic mapping technique is introduced to initialize the position of the manta ray population. Moreover, an adaptive rolling factor “S” is introduced from the manta rays’ rolling foraging. This allows the collaborative-vehicle population to jump out of local optima through “collaborative rolling." In the processes of manta ray chain feeding and manta ray spiral feeding, Cauchy reverse learning is integrated to broaden the search space and enhance the global optimization ability. The optimal Pareto front is then obtained using non-dominated sorting. Finally, the position of the manta ray population is mapped to the spatial positions of multi-AUVs, and cubic spline functions are used to optimize the trajectory of multi-AUVs. Through detailed analysis and comparison with five existing multi-objective optimization algorithms, it is found that the IMMRFO algorithm proposed in this paper can significantly reduce the average planned path length by 3.1~9.18 km in the path length target and reduce the average cost by 18.34~321.872 in the cost target. In an actual off-shore measurement process, IMMRFO enables AUVs to effectively bypass obstacles and threat sources, reduce risk costs, and improve mobile surveillance safety.

An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs

An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs

Research on Obstacle Detection and Avoidance of Autonomous Underwater Vehicle Based on Forward-Looking Sonar.

Due to the complexity of the ocean environment, an autonomous underwater vehicle (AUV) is disturbed by obstacles when performing tasks. Therefore, the research on underwater obstacle detection and avoidance is particularly important. Based on the images collected by a forward-looking sonar on an AUV, this article proposes an obstacle detection and avoidance algorithm. First, a deep learning-based obstacle candidate area detection algorithm is developed. This algorithm uses the You Only Look Once (YOLO) v3 network to determine obstacle candidate areas in a sonar image. Then, in the determined obstacle candidate areas, the obstacle detection algorithm based on the improved threshold segmentation algorithm is used to detect obstacles accurately. Finally, using the obstacle detection results obtained from the sonar images, an obstacle avoidance algorithm based on deep reinforcement learning (DRL) is developed to plan a reasonable obstacle avoidance path of an AUV. Experimental results show that the proposed algorithms improve obstacle detection accuracy and processing speed of sonar images. At the same time, the proposed algorithms ensure AUV navigation safety in a complex obstacle environment.

Hybrid Layer of Improved Interfered Fluid Dynamic System and Nonlinear Model Predictive Control for Navigation and Control of Autonomous Underwater Vehicles

This study introduces a hybrid control structure called Improved Interfered Fluid Dynamic System Nonlinear Model Predictive Control (IIFDS-NMPC) for the path planning and trajectory tracking of autonomous underwater vehicles (AUVs). The system consists of two layers; the upper layer utilizes the Improved Interfered Fluid Dynamic System (IIFDS) for path planning, while the lower layer employs Nonlinear Model Predictive Control (NMPC) for trajectory tracking. Extensive simulation experiments are conducted to determine optimal parameters for both static and dynamic obstacle scenarios. Additionally, real-world testing is performed using the BlueRov2 platform, incorporating multiple dynamic and static obstacles. The proposed approach achieves real-time control at a frequency of 100 Hz and exhibits impressive path tracking accuracy, with a root mean square (RMS) of 0.02 m. This research provides a valuable framework for navigation and control in practical applications.

Multi-Branch Dilation Convolution CenterNet for Object Detection of Underwater Vehicles

Object detection occupies a very important position in the fishing operation and autonomous navigation of underwater vehicles. At present, most deep-learning object detection approaches, such as R-CNN, SPPNet, R-FCN, etc., have two stages and are based on anchors. However, the previous methods generally have the problems of weak generalization ability and not high enough computational efficiency due to the generation of anchors. As a well-known one-stage anchor-free method, CenterNet can accelerate the inference speed by omitting the step of generating anchors, whereas it is difficult to extract sufficient global information because of the residual structure at the bottom layer, which leads to low detection precision for the overlapping targets. Dilation convolution makes the kernel obtain a larger reception field and access more information. Multi-branch structure can not only preserve the whole area information, but also efficiently separate foreground and background. By combining the dilation convolution and multi-branch structure, multi-branch dilation convolution is proposed and applied to the Hourglass backbone network in CenterNet, then an improved CenterNet named multi-branch dilation convolution CenterNet (MDC-CenterNet) is built, which has a stronger ability of object detection. The proposed method is successfully utilized for detection of underwater organisms including holothurian, scallop, echinus and starfish, and the comparison result shows that it outperforms the original CenterNet and the classical object detection network. Moreover, with the MS-COCO and PASCAL VOC datasets, a number of comparative experiments are performed for showing the advancement of our method compared to other best methods.

Underwater Wireless Sensor Network-Based Delaunay Triangulation (UWSN-DT) Algorithm for Sonar Map Fusion

Abstract Robust and fast image recognition and matching is an important task in the underwater domain. The primary focus of this work is on extracting subsea features with sonar sensor for further Autonomous Underwater Vehicle navigation, such as the robotic localization and landmark mapping applications. With the assistance of high-resolution underwater features in the Side Scan Sonar (SSS) images, an efficient feature detector and descriptor, Speeded Up Robust Feature, is employed to seabed sonar image fusion task. In order to solve the nonlinear intensity difference problem in SSS images, the main novelty of this work is the proposed Underwater Wireless Sensor Network-based Delaunay Triangulation (UWSN-DT) algorithm for improving the performances of sonar map fusion accuracy with low computational complexity, in which the wireless nodes are considered as underwater feature points, since nodes could provide sufficiently useful information for the underwater map fusion, such as the location. In the simulated experiments, it shows that the presented UWSN-DT approach works efficiently and robustly, especially for the subsea environments where there are few distinguishable feature points.

Self-propulsion performance predictions of AUV based on response surface methodology

The self-propulsion performance of an autonomous underwater vehicle (AUV) is predicted based on computational fluid dynamics (CFD) and response surface methodology (RSM). The innovations are that a data-driven predictive framework is proposed and a model of coupled rotation speed, self-propulsion velocity and power is developed. In the present study, the discretized propeller method and multiple reference frames (MRF) model are adopted. The design of experiment (DOE) and numerical simulations are executed with inlet velocity and propeller rotation speed as inputs and drag, thrust and torque as outputs. Using the simulation results as sample data, the prediction accuracy of multiple models is compared. The polynomial model is applied to fit the response points, describing the relationship between inputs and outputs. The goodness of fit and predictive ability is assessed using the adjusted coefficient of determination (Radj2), root mean square error (RMSE) and relative error (RE). Finally, the predicted results are further validated by experiments, and all relative errors are below 6%. A linear function of self-propulsion velocity versus rotation speed and a specific cubic function of propeller input power versus self-propulsion velocity is obtained. The prediction method and achievements are beneficial for the navigation, control and safety of AUVs.

AUV Path Planning Considering Ocean Current Disturbance Based on Cloud Desktop Technology.

In the field of ocean energy detection, Autonomous Underwater Vehicles (AUVs) offer significant advantages in terms of manpower, resource, and energy efficiency. However, the unpredictable nature of the ocean environment, particularly the real-time changes in ocean currents, poses navigational risks for AUVs. Therefore, effective path planning in dynamic environments is crucial for AUVs to perform specific tasks. This paper addresses the static path planning problem and proposes a model called the noise net double DQN network with prioritized experience replay (N-DDQNP). The N-DDQNP model combines a noise network and a prioritized experience replay mechanism to address the limited exploration and slow convergence speed issues of the DQN algorithm, which are caused by the greedy strategy and uniform sampling mechanism. The proposed approach involves constructing a double DQN network with a priority experience replay and an exploration mechanism using the noise network. Second, a compound reward function is formulated to take into account ocean current, distance, and safety factors, ensuring prompt feedback during the training process. Regarding the ocean current, the reward function is designed based on the angle between the current direction and the AUV's heading direction, considering its impact on the AUV's speed. As for the distance factor, the reward is determined by the Euclidean distance between the current position and the target point. Furthermore, the safety factor considers whether the AUV may collide with obstacles. By incorporating these three factors, the compound reward function is established. To evaluate the performance of the N-DDQNP model, experiments were conducted using real ocean data in various complex ocean environments. The results demonstrate that the path planning time of the N-DDQNP model outperforms other algorithms in different ocean current scenarios and obstacle environments. Furthermore, a user console-AUV connection has been established using spice cloud desktop technology. The cloud desktop architecture enables intuitive observation of the AUV's navigation posture and the surrounding marine environment, facilitating safer and more efficient underwater exploration and marine resource detection tasks.

A Survey on Underwater Computer Vision

Underwater computer vision has attracted increasing attention in the research community due to the recent advances in underwater platforms such as of rovers, gliders, autonomous underwater vehicles (AUVs) , and the like, that now make possible the acquisition of vast amounts of imagery and video for applications such as biodiversity assessment, environmental monitoring, and search and rescue. Despite growing interest, underwater computer vision is still a relatively under-researched area, where the attention in the literature has been paid to the use of computer vision techniques for image restoration and reconstruction, where image formation models and image processing methods are used to recover colour corrected or enhanced images. This is due to the notion that these methods can be used to achieve photometric invariants to perform higher-level vision tasks such as shape recovery and recognition under the challenging and widely varying imaging conditions that apply to underwater scenes. In this paper, we review underwater computer vision techniques for image reconstruction, restoration, recognition, depth, and shape recovery. Further, we review current applications such as biodiversity assessment, management and protection, infrastructure inspection and AUVs navigation, amongst others. We also delve upon the current trends in the field and examine the challenges and opportunities in the area.

ANALYSIS OF AUTOMATIC CONTROL SYSTEMS OF A MULTIPURPOSE AUTONOMOUS UNMANNED UNDERWATER VEHICLE WITH COMPLEX MOTION DYNAMICS

The overview article is devoted to the analysis of control systems of autonomous unmanned underwater vehicles, which belong to robotic underwater complexes and are used to provide search and deep-sea emergency and rescue operations, survey of various underwater objects (main pipelines, bottom structures, ship hulls, etc.), conducting scientific oceanographic surveys research, monitoring of the marine environment. The work examines scientific literary sources, which provide the results of development and research of orientation, navigation and motion control systems of multi-purpose autonomous unmanned underwater vehicles with complex movement dynamics, in particular when performing missions in a limited space with movement along complex trajectories. Special attention is focused on such modes of movement of devices as trajectory tracking, path-following and way point tracking. An analysis of the means of navigation and control, which can be used to ensure different modes of movement, was carried out, as well as the directions of development of both the unmanned underwater vehicles themselves and their control systems were considered.
 In order to substantiate the development of a control system for a multi-purpose maneuverable mini-class autonomous vehicle, the main requirements are formulated. It is proposed to conduct a synthesis and research of the control system based on the simulation of the dynamics of the movement of the mini-vehicle for different modes of movement in terms of parameters and trajectories, to develop algorithms for the adaptive control of the vehicle, to improve the system orientation and navigation, built on the basis of MEMS sensors. An information model of the development process is proposed and substantiated, which specifies the sequence of determining important parameters and characteristics of the vehicle and performing important stages of synthesis and analysis of the control system.

Neutrosophic MCDM Methodology for Risk Assessment of Autonomous Underwater Vehicles

Due to its usefulness in several industries and the military, researchers have concentrated on developing autonomous underwater vehicles (AUVs). However, AUV navigation continues to be a difficult challenge to solve owing to the variety of underwater settings. The usage of AUVs, or autonomous underwater vehicles, is not without dangers like malfunction, ecological risks, loss of communications, cybersecurity risks, collisions, and others. There are many criteria to assess these risks technical, operational, economic, and regulatory. So, the methods of multi-criteria decision-making (MCDM) is used to deal with these various criteria. The analytical hierarchy process (AHP) method is an MCDM methodology, that can be used to compute the weights of criteria. The AHP is integrated with the single-valued neutrosophic set (SVNS) to deal with uncertain data in the assessment process. The risks of AUVs are ranked after computing the weights of the criteria. The results show that malfunctions are the highest risk.