Single Autonomous Underwater Vehicle Research Articles

One-Way-Signal-Based Localization Method of Multiple Autonomous Underwater Vehicles for Distributed Ocean Surveys

This study proposes a simultaneous localization method of a group of autonomous underwater vehicles (AUVs) based on one-way signals to realize distributed oceanographic surveys. Each AUV group consists of a single high-performance AUV (parent AUV) and the other AUVs (child AUVs). The child AUVs estimate their states (position and heading) based on the parent AUV as a positioning reference. By assuming a situation in which many AUV groups are deployed, the child AUVs can receive positioning signals from multiple parent AUVs. Although only the direction information of the parent AUV can be obtained from a signal from one parent AUV, the child AUVs can estimate their states by receiving signals from different parent AUVs. Sea experiments were conducted using an autonomous buoy and an AUV. The effectiveness of the proposed method was evaluated through a navigation simulation based on the sensor data obtained from the experiments.

Research on Multiple-AUVs Collaborative Detection and Surrounding Attack Simulation.

Due to limitations in operational scope and efficiency, a single Autonomous Underwater Vehicle (AUV) falls short of meeting the demands of the contemporary marine working environment. Consequently, there is a growing interest in the coordination of multiple AUVs. To address the requirements of coordinated missions, this paper proposes a comprehensive solution for the coordinated development of multi-AUV formations, encompassing long-range ferrying, coordinated detection, and surrounding attack. In the initial phase, detection devices are deactivated, employing a path planning method based on the Rapidly Exploring Random Tree (RRT) algorithm to ensure collision-free AUV movement. During the coordinated detection phase, an artificial potential field method is applied to maintain AUV formation integrity and avoid obstacles, dynamically updating environmental probability based on formation movement. In the coordinated surroundings attack stage, predictive capabilities are enhanced using Long Short-Term Memory (LSTM) networks and reinforcement learning. Specifically, LSTM forecasts the target's position, while the Deep Deterministic Policy Gradient (DDPG) method controls AUV formation. The effectiveness of this coordinated solution is validated through an integrated simulation trajectory.

Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search

For underwater target exploration, multiple Autonomous Underwater Vehicles (AUVs) have shown significant advantages over single AUVs. Aiming at Multi-AUV task allocation, which is an important issue for collaborative work in underwater environments, this paper proposes a Multi-AUV task allocation method based on the Differential Evolutionary Gray Wolf Optimization (DE-GWO) algorithm. Firstly, the working process of the Multi-AUV system was analyzed, and the allocation model and objective function were established. Then, we combined the advantages of the strong global search capability of the Differential Evolutionary (DE) algorithm and the excellent convergence performance of Gray Wolf Optimization (GWO) to solve the task assignment of the Multi-AUV system. Finally, a reassignment mechanism was used to solve the problem of AUV failures during the task’s execution. In the simulation comparison experiments, the DE-GWO, GWO, DE, and Particle Swarm Optimization (PSO) algorithms were carried out for different AUV execution capabilities, respectively.

Cooperative Formation Control for Multiple AUVs With Intermittent Underwater Acoustic Communication in IoUT

Autonomous Underwater Vehicle (AUV) system has played an important role in complex Internet of Underwater Things (IoUT). As we know, single AUV is inefficient in collecting data in large-scale IoUT. In order to improve the timeliness of data, this paper uses multiple AUVs with specific formation shape to collect sensory data. However, the intermittent and unreliable characteristics of underwater acoustic communication between AUVs seriously affects the performance of formation control. To solve this issue, this paper mainly investigates formation control problem of leader-follower structured AUVs with state prediction estimation under the condition of unreliable underwater acoustic channel. Firstly, a novel formation control law derived from backstepping sliding mode method is proposed, and then a suitable Lyapunov functional is constructed to prove sufficient stability conditions. Secondly, Gaussian prediction model considering time delay and packet dropout has been designed to estimate the intermittent communication channel. Moreover, a new metric named formation uniform degree (FUD) is proposed to measure the degree of queue uniformity in a quantitative manner. Finally, extensive simulation results compared with traditional methods based on ideal channel model demonstrate the effectiveness of proposed formation control method.

Modeling and robust quantitative control for AUV-tow-load system

This paper models a point-mass load tethered to a single autonomous underwater vehicle (AUV) and proposes a quantitative control strategy, which is able to drive the load to track the desired trajectory closely. The AUV-tow-load system is modeled as three connected subsystems: load position subsystem, cable direction subsystem, and AUV attitude subsystem. Improving on the current prescribed performance control methods, where the transient-state and steady-state tracking behaviors cannot be preassigned accurately, a quantitative controller is designed for the point-mass load position, achieving predefined tracking performance without violating user-defined quantitative indices including overshoot, convergence time, and steady-state accuracy. An uncertainties and disturbances estimator (UDE) is designed and incorporated into the control inputs to compensate for the effects of uncertain dynamics and external disturbances. Rigorous theoretical proof is presented, showing that all tracking errors converge to the neighborhood of zero with quantitatively designed tracking behaviors, obtaining uniformly ultimately boundedness. Simulation results are provided to validate the efficiency of the proposed method.

A Fusion Designed Improved Elastic Potential Field Method in AUV Underwater Target Interception

In this article, the single autonomous underwater vehicle (AUV) interception problem is analyzed and studied, and a new improved elastic potential field (EPF)-particle swarm-sliding window fusion strategy is proposed to achieve fast interception of underwater moving targets. First, the Sliding Window Fitting Prediction Method is used to fit and predict the trajectory of the target object. To improve the interception efficiency, the Elastic Predictive Interception Method is used to predict the position of the target object at the next moment, and the tracking point is replaced by the prediction point to intercept moving targets. Second, an expansion potential field method is proposed to replace the gradient potential field in the artificial potential field (APF), and the relatively appropriate influence range of the obstacle is found through the particle swarm optimization (PSO) algorithm. Then, the AUV converges gradually from the starting point to the target point through the stepping interception scheme. This method can make up for some shortcomings of the APF method and find a better interception path while making a rapid path planning decision. Simulation results show the interception effect of improved EPF under elastic prediction conditions, and the effectiveness of the proposed method is verified by a comparative analysis.

Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles

This research presents a hybrid approach for path planning of autonomous underwater vehicles (AUVs). During path planning, static obstacles affect the desired path and path distance which result in collision penalties. In this study, the merits of grey wolf optimization (GWO) and genetic algorithm (GA) of bionic-inspired algorithms are integrated to implement a hybrid grey wolf optimization (HGWO) algorithm which allows AUVs to reach their destination safely in an obstacle rich environment. The proposed hybrid path planner is employed for path planning of a single AUV based on collision avoidance. It uses the GA as an initialization generator to overcome the random initialization problem of GWO. In this research, the total cost is considered to be a function of path distance and collision penalties. Further, the application of the proposed hybrid path planner is extended for cooperative path planning of AUVs while avoiding collision using communication consensus. Simulation results are obtained for both a single AUV and multiple AUV path planning in a 3D obstacle rich environment using a proportional-derivative controller. The Kruskal–Wallis test is employed for a non-parametric statistical analysis, where the independence of the results given by the algorithms is demonstrated.

Electric-field-coupled oscillators for collective electrochemical perception in biohybrid robotics

This work explores the application of nonlinear oscillators coupled by an electric field in water, inspired by weakly electric fish. Such coupled oscillators operate in clear and colloidal (mud, bottom silt) water and represent a collective electrochemical sensor that is sensitive to global environmental parameters, the geometry of the common electric field and spatial dynamics of autonomous underwater vehicles (AUVs). Implemented in hardware and software, this approach can be used to create global awareness in a group of robots, which possess limited sensing and communication capabilities. Using oscillators from different AUVs enables extension of the range limitations related to the electric dipole of a single AUV. Applications of this technique are demonstrated for detecting the number of AUVs, distances between them, perception of dielectric objects and synchronization of behavior. Recognizing self-/nonself-generated signals by electric fish is re-embodied in a technological way through an ‘electrical mirror’ for discrimination between ‘collective self’ and ‘collective nonself’. These approaches have been implemented in several research projects with bioinspired/biohybrid systems in fresh and salt water, and electrochemical sensing in fluidic media.

Autonomous underwater vehicle formation control and obstacle avoidance using multi-agent generative adversarial imitation learning

Autonomous underwater vehicle (AUV) is widely used in complex underwater missions such as bottom survey and data collection. Multiple AUVs can cooperatively complete tasks that single AUV cannot accomplish. Recently, multi-agent reinforcement learning (MARL) has been introduced to improve multi-AUV control in uncertain marine environments. However, it is very difficult and even unpractical to design effective and efficient reward functions for various tasks. In this paper, we implemented multi-agent generative adversarial imitation learning (MAGAIL) from expert demonstrated trajectories for formation control and obstacle avoidance of multi-AUV. In addition, decentralized training with decentralized execution framework was adopted to alleviate the communication problem in underwater environments. Moreover, to facilitate the discriminator to accurately judge the quality of AUV’s trajectory in the two tasks and increase the convergence speed, we improved upon MAGAIL by dividing the state–action pairs of expert trajectory for each AUV into two groups and updating discriminator by randomly selecting equal number of state–action pairs from both groups. Our experimental results on a simulated AUV system modeling Sailfish 210 of our lab in the Gazebo simulation environment show that MAGAIL allows control policies of multi-AUV to obtain a better performance than traditional multi-agent deep reinforcement learning from fine-tuned reward function — IPPO. Moreover, control policies trained via MAGAIL in simple tasks can generalize better to complex tasks than those trained via IPPO.

GELS: Generalized Energy-Based Leader Selection in Bio-Inspired Leader‐Follower AUV Network

Abstract An autonomous underwater vehicle (AUV) is a self-propelled, untethered underwater vehicle with minimal or no human supervision. However, the capability of a single AUV to cover large areas underwater is very limited. In this regard, a swarm of AUVs has gained significant attention in the recent years for underwater applications such as ocean exploration, mine-sweeping, surveillance, rescue missions, seabed mapping, environmental monitoring, etc. Notably, the swarm-based network can be further classified into two types, based on the architecture: cooperative multi-AUV network and leader‐follower multi-AUV network. Of these, the leader‐follower multi-AUV network with a Fixed Leader is preferable due to its simple architecture and less operational complexity. However, the inherent energy limitations of the leader in the leader‐follower model become the bottleneck for an efficient mechanism. Hence, there is a need to design a better mechanism for a multi-AUV network. In this paper, a Generalized Energy-based Leader Selection (GELS) algorithm is proposed for a multi-AUV network to increase the AUV network's transmission range and duration. Significantly, GELS can be superimposed and integrated with the existing algorithms. This proposed work integrates GELS with two bio-inspired mechanisms: Ant Colony Optimization and Particle Swarm Optimization. The dynamic leader selection using GELS results more in the travel distance and duration than the Fixed Leader network. This is because, in GELS, based on residual energy, the followers could also be allowed to lead the network; whereas, in fixed leader the followers cannot lead the network.

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.

Multi-AUV Collaborative Data Collection Algorithm Based on Q-Learning in Underwater Acoustic Sensor Networks

Intelligent data collection is a key component in underwater acoustic sensor networks, and it plays an important role in seabed environment monitoring, marine resource detection and marine disaster early warning. Owing to the particularities of the underwater environment, such as reduced infrastructure and noisy communication channels, the data collected by underwater nodes are more efficiently transmitted to a control center on the surface by way of an autonomous underwater vehicle (AUV). However, with the increasing complexity of the underwater tasks, using a single AUV for data collection cannot meet the requirements of low latency and low power consumption. To solve this problem, a multi-AUV collaborative data collection algorithm that reduces the load of data collection task on a single AUV is proposed. The algorithm is divided into two stages: multi-AUV task allocation and Q-learning-based AUV path planning. The data transmission of the clusters is regarded as a set of different tasks, which are assigned to the AUVs for completion. Subsequently, path planning is performed to guide the AUVs, so that the tasks are completed promptly and at a reduced cost. Simulation results show that the proposed algorithm can leverage the energy consumption of a network and extend its lifetime. The performance of the proposed algorithm in energy consumption is increased by about 10%, and the delay of data collection is also significantly reduced.

Overview of Research on Transformation of Multi-AUV Formations

Because of their wide detection range and rich functions, autonomous underwater vehicles (AUVs) are widely used for observing the marine environment, for exploring natural resources, for security and defense purposes, and in many other fields of interest. Compared with a single AUV, a multi-AUV formation can better perform various tasks and adapt to complex underwater environments. With changes in the mission or environment, a change in the UAV formation may also be required. In the last decade, much progress has been made in the transformation of multi-AUV formations. In this paper, we aim to analyze the core concepts of multi-AUV formation transformation; summarize the effects of the AUV model, underwater environment, and communication between AUVs within formations on formation transformation; and elaborate on basic theories and implementation approaches for multi-AUV formation transformation. Moreover, this overview includes a bibliometric analysis of the related literature from multiple perspectives. Finally, some challenging issues and future research directions for multi-AUV formation transformation are highlighted.

A Survey of Autonomous Underwater Vehicle Formation: Performance, Formation Control, and Communication Capability

Autonomous underwater vehicles (AUVs) are submersible underwater vehicles controlled by onboard computers. AUV formation is a cooperative control which focuses on controlling multiple AUVs to move in a group while executing tasks. In contrast to a single AUV, multi-AUV formation represents higher efficiency and better stability for many applications, such as oil and gas industries, hydrographic surveys, and military missions, etc. To achieve better formation, there are several key factors, including AUV performance, formation control, and communication capability. However, most studies in the field of AUV formation mainly focus on formation control methods. We observe that the research of communication capability and AUV performance of multiple AUV formation is still in an early stage. It is beneficial to researchers to present a comprehensive survey of the state of the art of AUV formation research and development. In this paper, we study AUV, formation control, and underwater acoustic communication capability in detail. We propose a classification framework with three dimensions, including AUV performance, formation control, and communication capability. This framework provides a comprehensive classification method for future AUV formation research. It also can be used to compare different methods and help engineers choose suitable formation methods for various applications. Moreover, our survey discusses formation architecture with communication constraints and we identify some common misconceptions and questionable research for formation control related to communication.

Underwater Acoustical Localization of the Black Box Utilizing Single Autonomous Underwater Vehicle Based on the Second-Order Time Difference of Arrival

Underwater acoustic localization (UWAL) is a useful technique, and is applied in finding the acoustical sources, such as the black box. The existing UWAL methods are mainly based on the time of arrival (TOA), the time difference of arrival (TDOA), and the direction of arrival (DOA), which need the time synchronization, the knowledge of signal period, and the high precision angular measurement, respectively. However, these conditions are difficult to be satisfied in the scene of locating the black box utilizing single autonomous underwater vehicle. In this article, we propose the definition of second-order time difference of arrival (STDOA) and establish the localization model based on the STDOA. The proposed STDOA-based method eliminates the unknown signal period and overcomes the restriction of the traditional TDOA-based method. Combined with the particle filter, target tracking algorithm based on the STDOA is also presented. Precision analysis and the simulation results validate the effectiveness of the STDOA-based method and prove that the proposed method achieves a comparable localization precision compared with the TOA-based method and the TDOA-based method, and a higher precision compared to the DOA-based method.

Multiautonomous underwater vehicle consistent collaborative hunting method based on generative adversarial network

The time-varying ocean currents and the delay of underwater acoustic communication have caused the uncertainty of single autonomous underwater vehicle (AUV) tracking target and the inconsistency of multi-AUV coordination, which make it difficult for multiple AUVs to form a hunting alliance. To solve the above problems, this article proposes the multi-AUV consistent collaborative hunting method based on generative adversarial network (GAN). Firstly, the three-dimensional (3D) kinematic model of AUV is established for the underwater 3D environment. Secondly, combined with the Laplacian matrix, the topology of the hunting alliance in the ideal environment is established, and the control rate of AUV is calculated. Finally, using the GAN network model, the control relationship after environmental interference is used as the input of the generative model. The control rate in the ideal environment is used as the comparison object of the discriminative model. Using the iterative training of GAN to generate a control rate that adapts to the current interference environment and combining multi-AUV topological hunting model to achieve successful hunting of noncooperative target, the experimental results show that the algorithm reduces the average hunting time to 62.53 s and the success rate of hunting is increased to 84.69%, which is 1.17% higher than the particle swarm optimization-constant modulus algorithm (PSO-CMA) algorithm.

Accurate and Efficient Seafloor Observations With Multiple Autonomous Underwater Vehicles: Theory and Experiments in a Hydrothermal Vent Field

This letter reports the sea experiments of a navigation method for multiple autonomous underwater vehicles (AUVs) that relies on a single high-performance AUV, which can estimate its self-state (position and pose) accurately without any support. In the method, there is a parent AUV and child AUVs. While the latter have minimal navigational sensors, such as a microelectromechanical gyro, a single-axis fiber optic gyro and cameras, the former can estimate its self-state without any support using high-grade navigational sensors, such as an inertial navigation system and a Doppler velocity log. As all the AUVs have acoustic positioning and communication devices, the child AUVs can also estimate their self-state as accurately as the parent AUV; thanks to relative positioning with the parent AUV. We performed the sea experiments in a hydrothermal vent field using AUVs. Post-processing state estimation using sensor data from the experiments showed that the proposed method can realize long-distance navigation while maintaining an accuracy more than five times that for navigation independently performed by a child AUV.

A multiple autonomous underwater vehicles hazard decision method based on information fusion

Autonomous underwater vehicle (AUV) plays an important role in ocean research. Compared with single AUV system, multi-AUV system has higher stability, robustness and high efficiency. Multiple AUVs give greater credibility than a single AUV in decision making. In this paper, the problem of multiple AUV making dangerous decisions in dangerous environments is studied. Single AUV can not make an accurate decision when facing some complex situations. We propose to fuse multiple AUV data to obtain more accurate dangerous decisions. The multi-AUV system adopts a distributed multi-robot structure, each AUV is an individual. A transferable information model and Dempster-Shafer evidence theory are used. A new multi-AUV hazard discrimination model is proposed for the final hazard decisionmaking. Verification by calculation, new decision-making method can effectively improve the discriminant ability of multi-AUV system when facing danger. This new hazard decision method improves the survivability of multiple AUVs in actual ocean exploration.

Complete Coverage Autonomous Underwater Vehicles Path Planning Based on Glasius Bio-Inspired Neural Network Algorithm for Discrete and Centralized Programming

For the complete coverage path planning of autonomous underwater vehicles (AUVs), a new strategy with Glasius bio-inspired neural network (GBNN) algorithm with discrete and centralized programming is proposed. The basic modeling for multi-AUVs complete coverage problem based on grid map and neural network is discussed first. Then, the design for single AUV complete coverage is introduced based on GBNN algorithm which is a new developed tool with small amount of calculation and high efficiency. In order to solve the difficulty of single AUV full coverage task of large water range, the multi-AUV full coverage discrete and centralized programming is proposed based on GBNN algorithm. The simulation experiment is conducted to confirm that through the proposed algorithm, multi-AUVs can plan reasonable and collision-free coverage path and reach full coverage on the same task area with division of labor and cooperation.

A Novel Single-Beacon Navigation Method for Group AUVs Based on SIMO Model

Single-beacon navigation for group autonomous underwater vehicles (AUVs) suffers from long navigation period and asynchronization. This paper addresses the problem by introducing the single-input-multiple-output (SIMO) model, where, a single AUV transmits the signal and other AUVs receive the signal. Utilizing the time-delay of signals from the beacon and the sender, the navigation for all AUVs is achieved simultaneously, and the navigation period is dramatically decreased. Moreover, based on the new SIMO model, a tracking method utilizing extended Kalman filter is also put forward to increase the navigation precision and reduce the computational burden. Finally, a coordinate fusion algorithm based on the location, error, and time-delay is presented, which fuses the independent AUV coordinates to be a higher precision holistic coordinate system for group AUVs. Simulation results show the effectiveness of the proposed method.