Multiple Autonomous Underwater Vehicles Research Articles

Multiple model AUV navigation methodology with adaptivity and robustness

Autonomous Underwater Vehicle (AUV) navigation and localization in the complex and changeable marine environment is crucial and challenging. The inaccurate noise covariance matrix may result in significant prediction errors or even filtering divergence for traditional navigation algorithms. Meanwhile, the outliers in sensor observations also have a substantial adverse effect on the AUV positioning accuracy. Therefore, in this paper, we propose an Improved Interacting Multiple Model-Unscented Kalman Filter (IIMM-UKF) with both adaptivity and robustness for AUV navigation. Firstly, the Variational Bayesian (VB) based UKF algorithm is proposed as adaptive sub-models of IIMM to estimate the time-varying measurement noise covariance adaptively. Secondly, an outliers detector sub-model is proposed to enhance the robustness of IIMM. Gaussian Process Regression (GPR) is used to regress state pseudo values for each sub-model as estimations of the filtering process when the outliers are detected by the residual χ2 detector. Multiple models work in parallel to achieve high-precision and robust AUV navigation. The performance of the IIMM-UKF algorithm has been evaluated on AUV with simulation and actual experimental data. In sea trials, the average AUV navigation accuracy of the IIMM-UKF is improved by 61.02% compared to EKF, 43.44% compared to UKF, and 35.54% compared to the IMM-UKF.

Research on Formation Control Method of Heterogeneous AUV Group under Event-Triggered Mechanism

The time-sampling control strategy has communication discontinuities in the control of multiple AUVs (autonomous underwater vehicles). To overcome this problem, a distributed event-triggered communication mechanism is proposed to make each AUV communicate only when its own state is updated, which reduces the frequency of communication and improves the stability. This mechanism has better adaptability for formation control between heterogeneous AUV groups. At the same time, two consistency control algorithms based on event-triggered for homogeneous and heterogeneous AUV groups are studied, respectively. The known consistency algorithms are applied to the control of heterogeneous AUV groups for comparative analysis. The simulation results demonstrate that the number of communication among AUVs under the event-triggered control strategy can be significantly reduced. Therefore, the stability of the system is improved. Compared with the traditional consensus algorithm, the algorithm proposed in this paper has advantages in the control of heterogeneous AUV groups.

Cooperative flow field estimation via relative and absolute motion-integration errors of multiple AUVs

This paper presents a cooperative flow field estimation algorithm based on the sensor measurements from a group of autonomous underwater vehicles (AUVs). The algorithm uses: (1) the deviation between actual and predicted relative positions between each vehicle and its neighbors (relative motion-integration error), which is available using local measurement, and (2) the deviation between the actual and predicted positions (absolute motion-integration error) of each vehicle, which is available from GPS when the AUVs are on the sea surface, to reconstruct the flow field. Firstly, we establish the relation between the motion-integration errors and the flow field, which is given in the form of a set of nonlinear equations. An iterative algorithm is then proposed to estimate the unknown flow field by solving an inverse problem for these nonlinear equations. Moreover, it is rigorously proven that under this algorithm, the estimation result converges to the real flow field. Finally, simulations are conducted to illustrate the effectiveness of the proposed algorithm.

Communication-constrained cooperative bathymetric simultaneous localisation and mapping with efficient bathymetric data transmission method

AbstractBathymetric simultaneous localisation and mapping (SLAM) methods yield accurate navigation results for autonomous underwater vehicles (AUVs) and can construct consistent seabed terrain maps. Multiple independently working vehicles can complete tasks like surveying and mapping efficiently, which means cooperative bathymetric SLAM using multiple AUVs is suitable for large-scale seabed mapping. However, the transmission of bathymetric measurements collected using a multi-beam echo sounder over a low bandwidth, noisy, and unreliable acoustic channel is difficult, making cooperative bathymetric SLAM very challenging. This paper develops a graph-based cooperative bathymetric SLAM system that can compress many bathymetric measurements into small-scale acoustic packets and yield accurate navigation results with a 10% loss of acoustic packets caused by unreliable acoustic communication. According to the simulation conducted using the field data, the new algorithm is shown to be robust and capable of providing accurate location and mapping results over a low bandwidth, noisy, and unreliable acoustic channel.

Guidance and control of autonomous surface underwater vehicles for target tracking in ocean environment by deep reinforcement learning

This paper studies a guidance and control framework of multiple autonomous surface underwater vehicles (multi-ASUV) based on deep reinforcement learning (DRL) for target tracking. The framework enables the vehicles to complete the standoff tracking and sampling tasks in a predetermined circular trajectory centered on the target and maintain predetermined relative positions during the process to obtain high-precision spatio-temporal synchronization data. We design an end-to-end architecture that maps the sensor inputs to control commands and develop autonomous capable of achieving the hybrid objective of cooperative guidance, standoff tracking, and dynamic obstacle avoidance without having prior knowledge about the goal or the environment. The results demonstrate the feasibility of the end-to-end DRL method with higher accuracy than the traditional “guidance-control” two-step method. Meanwhile, the obstacle avoidance and standoff tracking experiment for swarm and the sampling experiment in the mesoscale eddy area are stimulated further to verify the proposed framework’s effectiveness and robust ability.

Synchronous-Clock Range-Angle Relative Acoustic Navigation: A Unified Approach to Multi-AUV Localization, Command, Control, and Coordination

This paper presents a scalable acoustic navigation approach for the unified command, control, and coordination of multiple autonomous underwater vehicles (AUVs). Existing multi-AUV operations typically achieve coordination manually by programming individual vehicles on the surface via radio communications, which becomes impractical with large vehicle numbers; or they require bi-directional intervehicle acoustic communications to achieve limited coordination when submerged, with limited scalability due to the physical properties of the acoustic channel. Our approach utilizes a single, periodically broadcasting beacon acting as a navigation reference for the group of AUVs, each of which carries a chip-scale atomic clock and fixed ultrashort baseline array of acoustic receivers. One-way travel-time from synchronized clocks and time-delays between signals received by each array element allow any number of vehicles within receive distance to determine range, angle, and thus determine their relative position to the beacon. The operator can command different vehicle behaviors by selecting between broadcast signals from a predetermined set, while coordination between AUVs is achieved without intervehicle communication by defining individual vehicle behaviors within the context of the group. Vehicle behaviors are designed within a beacon-centric moving frame of reference, allowing the operator to control the absolute position of the AUV group by repositioning the navigation beacon to survey the area of interest. Multiple deployments with a fleet of three miniature, low-cost SandShark AUVs performing closed-loop acoustic navigation in real-time provide experimental results validated against a secondary long-baseline positioning system, demonstrating the capabilities and robustness of our approach with real-world data.

Cooperative Localization Based Multi-AUV Trajectory Planning for Target Approaching in Anchor-Free Environments

Target approaching is an essential ability in marine applications, such as underwater rescue. The recent advances in autonomous underwater vehicles (AUVs) make them ideal options to deliver the target approaching in anchor-free environments, where no device with a known position (anchor) is pre-deployed. To approach a moving target, the trajectory planning of AUVs is an indispensable technical guarantee. However, due to the lack of anchors and the error accumulation during the navigation, the position errors of AUVs grow without bound and degrade trajectory planning performance. Thus, an AUV localization procedure is required and should be used in combination with the trajectory planning. In this paper, we research the multiple-AUV trajectory planning based on AUV cooperative localization and propose a framework named multiple-AUV cooperative localization based trajectory planning (MCLTP). Under MCLTP, scalable cooperative localization for multiple AUVs (SCLMA) and trajectory planning for multiple AUVs (TPMA) algorithms are derived. The design of SCLMA focuses on improving the localization accuracy by alleviating the accuracy degradation caused by the accumulated errors in the inertial measurements. TPMA formulates the trajectory planning into a convex optimization problem with balanced target detection probability and tracking accuracy. Furthermore, the considerations of the practical constraints, such as the kinematic and communication limitations of AUVs, collision and obstacle avoidance, make TPMA reliable in the complex marine environments. Simulation results reveal the advantages of the proposed methods by comparisons with the state-of-the-art alternative algorithms.

Singularity avoidance adaptive output‐feedback fixed‐time consensus control for multiple autonomous underwater vehicles subject to nonlinearities

AbstractThis article investigates the singularity avoidance adaptive output‐feedback fixed‐time consensus control problem for multiple autonomous underwater vehicles (AUVs) subject to nonlinearities. A distributed state observer is designed to reconstruct the unmeasurable coupling states of each AUV system. In order to handle the unknown nonlinearities of the multi‐AUV systems, the fuzzy logic systems are adopted to approximate unknown hydrodynamic parameters. By combining adaptive fixed‐time control with backstepping technology, an adaptive singularity avoidance fixed‐time consensus control algorithm is developed, where a new error switching mechanism is designed for avoiding the singularity problem caused by the differentiation of the virtual control law. Subsequently, a singularity avoidance distributed adaptive output‐feedback fixed‐time fuzzy controller is presented, under which an auxiliary function is designed to handle the difficulty of the fixed‐time stability analysis under the output‐feedback control strategy, where two types of observer errors are needed in the designed Lyapunov function to achieve the fixed‐time stability. Then, based on the fixed‐time stability criterion, it is proved that both the consensus tracking performance and the closed‐loop stability can be guaranteed in fixed time. Finally, a simulation example verifies the effectiveness of the developed control strategy.

Event-based secure consensus of multiple AUVs under DoS attacks

In this paper, the event-based triggering method is adopted to investigate the secure consensus issue of multiple autonomous underwater vehicles (AUVs) under denial-of-service (DoS) attacks. DoS attack is a form of time-sequence-based cyber attack, which can destroy the normal service of the control target or network. First, based on an event-triggered mechanism, a novel secure control protocol is proposed. Second, the upper bounds of attack duration and attack frequency are given to ensure that multiple AUVs under DoS attacks can reach consensus. Third, an event-triggered mechanism with exponential variables is developed to avoid the continuous update of the controller, thereby reducing the burdens of communication and calculation. Zeno behavior can be strictly ruled out for each AUV under this triggering mechanism. Finally, the simulation results illustrate the feasibility of the proposed scheme.

Learning‐based robust control methodologies under information constraints

Learning‐based robust control methodologies under information constraints

Reinforcement Learning-Based Trajectory Optimization for Data Muling With Underwater Mobile Nodes

This paper addresses trajectory optimization problems for underwater data muling with mobile nodes. In the underwater data muling scenario, multiple autonomous underwater vehicles (AUVs) sample a mission area, and autonomous surface vehicles (ASVs) visit the navigating AUVs to retrieve the collected data. The optimization objectives are to simultaneously maximize fairness in data transmissions and minimize the travel distance of the surface nodes. We propose an nearest- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> reinforcement learning algorithm, which chooses only from the nearest- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> AUVs as candidates for the next node for data transmissions. We use the distance between AUVs and the ASV as the state, selected AUVs as the action. A reward is designed as the function of both the data volume transmitted and the ASV travel distance. In the scenario with multiple ASVs, an AUV association strategy is presented to support the use of multiple surface nodes. We conduct computer simulations for performance evaluation. The effects from the number of AUVs, the size of the mission area, and the state number are investigated. The simulation results show that the proposed algorithm outperforms traditional methods in terms of the fairness and ASV travel distance.

Multi-AUV Adaptive Path Planning and Cooperative Sampling for Ocean Scalar Field Estimation

Accurate estimation of water quality is significant for ocean resource development. The autonomous discovery and intensive sampling of high chlorophyll-a concentration area is meaningful for early warning of algal blooms. In this article, an online path planning method with heterogeneous strategies and low-communication cooperation is proposed, and it is used for multiple autonomous underwater vehicles (AUVs) adaptive sampling. The environmental scalar field is modeled as a Gaussian process (GP), which is learned from the accumulated measurements gathered by the multi-AUV system equipped with sensors. The heterogeneous strategy cooperative sampling (HSCS) method is proposed, so that every AUV can plan its own path based on the newly designed differential evolution (DE)-based path planner. Meanwhile, the sparse variation GP (SVGP) is introduced to summarize local measurements, which endows AUVs to share information effectively with weak communication environment. Simulation experiments are carried out to test the performance of the proposed algorithms, and the simulation result shows that the proposed method can reduce the estimate error by at least 15.6% with limited communication, and the coverage integrity of the interesting region is guaranteed.

Adaptive dynamic event-triggered consensus control of multiple autonomous underwater vehicles

This paper addresses the dynamic event-triggered consensus problem of multiple autonomous underwater vehicles (AUVs) via the adaptive control method. First, due to limited bandwidth resources, an adaptive dynamic event-triggered mechanism (ADEM) with coefficients automatically adjusted according to the triggering errors is proposed to reduce the transmission burden. second, for the AUV model, an auxiliary variable is introduced to simplify the AUV system to a first-order model that is easy to analyse. Meanwhile, a novel adaptive dynamic event-triggered consensus control protocol is developed for each AUV to achieve consensus. Third, it is shown that, with the ADEM, the minimum interevent time between any two consecutive triggering instants can be prolonged to avoid the Zeno phenomenon for each AUV. Finally, the simulation results demonstrate the advantage of the proposed scheme.

Formation Control of a Multi-Autonomous Underwater Vehicle Event-Triggered Mechanism Based on the Hungarian Algorithm

Among the key technologies of Autonomous Underwater Vehicle (AUV) leader–follower formations control, formation reconfiguration technology is one of the main technologies to ensure that multiple AUVs successfully complete their tasks in a complex operating environment. The biggest drawback of the leader–follower formations technology is the failure of the leader and the excessive communication pressure of the leader. Aiming at the problem of leader failure in multi- AUV leader–follower formations, the Hungarian algorithm is used to reconstruct the failed formation with a minimum cost, and the improvement of the Hungarian algorithm can solve the problem of a non-standard assignment. In order to solve the problem of an increased leader communication task after formation reconfiguration, the application of an event-triggered mechanism (ETM) can reduce unnecessary and useless communication, while the efficiency of the ETM can be improved through increasing the event-triggered conditions of the sampling error threshold. The simulation results of multi-AUV formation control show that the Hungarian algorithm proposed in this paper can deal with the leader failure in the multi-AUV leader–follower formation, and the ETM designed in this paper can reduce about 90% of the communication traffic of the formation which also proves the highly efficient performance of the improved ETM in the paper.

Leader Follower Formation Control for Underwater Transportation Using Multiple Autonomous Underwater Vehicles

The successful ability to conduct underwater transportation using multiple autonomous underwater vehicles (AUVs) is important for the commercial sector to undertake precise underwater installations on large modules, whilst for the military sector it has the added advantage of improved secrecy for clandestine operations. The technical requirements are the stability of the payload and internal collision avoidance while keeping track of the desired trajectory considering the underwater effects. Here, a leader-follower formation control strategy was developed and implemented on the transportation system of AUVs. PID controllers were used for the vehicles and a linear feedback controller for maintaining the formation. A Kalman Filter (KF) was designed to estimate the full state of the leader under disturbance, noise and limited sensor readings. The results demonstrate that though the technical requirements are met, the thrust oscillations under disturbance and noise produce the undesired heading angles.&#x0D;

Low-cost high-performance seafloor surveying by multiple autonomous underwater vehicles

This paper examines the performance of multiple autonomous underwater vehicle (AUV) navigation using a static landmark AUV with respect to estimation accuracy, survey efficiency, and system cost. An alternating landmark navigation system has previously been developed in which moving AUVs estimate their states from a stationary AUV on the seafloor. Observational coverage can be expanded by alternating the landmark role among all AUVs. This paper focuses on how to extend the method in two or more AUV cases. In the extended method, two AUVs alternate between the moving and landmark roles, while other AUVs move and estimate their own states from the landmark AUV. It is expected that as the number of AUVs increased, survey efficiency would be improved because all AUVs except the landmark AUV can navigate simultaneously. All the AUVs can achieve accurate state estimation because of the landmark AUV. Through navigational simulation in cases of 3 to 30 AUVs, the performance was examined in terms of estimation accuracy, efficiency, and cost of the system. It was verified that the method can achieve low-cost high-performance seafloor surveying by selecting the most suitable navigation style for each mission.

A multi-AUV cooperative navigation method

Cooperative navigation is one of the key methods for multiple autonomous underwater vehicles (AUVs) to obtain accurate positions when performing tasks underwater. In the realistic state-space model of the multi-AUV cooperative navigation system, where the system noise does not satisfy the additivity, it is necessary to augment the dimension of the state variables before nonlinear filtering. Aiming at the problem that the error of traditional algorithms increases linearly with the dimension of state-space, a cooperative navigation method based on Augmented Embedded Cubature Kalman filter (AECKF) algorithm is proposed. The experiment results show that the AECKF cooperative navigation algorithm has better positioning accuracy and stability than the traditional algorithm.

Distributed adaptive neural network constraint containment control for the benthic autonomous underwater vehicles

Multiple Autonomous Underwater Vehicles (AUV) can complete complex ocean exploration missions cooperatively. This paper proposes a containment control algorithm under time-varying constraints by using a neural network for control of multiple benthic AUVs which are called the Ocean Bottom Flying Node (OBFN) systems. The multiple OBFNs in the presence of nonlinear model uncertainties are under the directed topology. First, we define the auxiliary variable and low-order filter. The anti-windup saturation auxiliary system is constructed in presence of the input saturation. Further, the adaptive law and neural network are designed to compensate environmental disturbances and systems model uncertainties, respectively. Moreover, in order to ensure the control performance of OBFN, an exponential boundary constraint is imposed which could constrain the system error convergent rates and bounds. Lyapunov stability theorem and graph theory are used to prove that the multiple OBFN systems are uniformly ultimately bounded. Finally, simulation results for multiple OBFNs illustrate the effectiveness of the proposed algorithm.

AUV path planning for coverage search of static target in ocean environment

Aiming at the path planning problem for searching static target by autonomous underwater vehicles (AUVs) in ocean environment, this paper proposes a hierarchical method based on the maximum cumulative detection reward or search efficiency. First, the mission sea region is described in the form of target probability map, and the subregions with high search value are extracted by the method of expectation-maximization (EM). For multiple AUVs, we propose the bidirectional negotiation with biased min-consensus (BN-BMC) algorithm to determine the allocation and prioritization of subregions to be searched. On this basis, we utilize an adaptive oval spiral coverage (AOSC) strategy to plan the coverage path within each subregion, and take the Dubins path satisfying the AUV kinematic constraint as the transition path between subregions. Finally, the simulation results validate the advantage of our method.

Parent–child‐based navigation method of multiple autonomous underwater vehicles for an underwater self‐completed survey

AbstractThis study proposes a navigation method of multiple autonomous underwater vehicles (AUVs) where low‐cost AUVs (child AUVs) estimate their self‐states including their position, yaw angle, and horizontal moving velocity accurately thanks to a single high‐performance AUV (parent AUV) without any external support. Since the child AUVs have only simple navigational sensors, positioning performance is not enough. The parent AUV, on the other hand, can conduct accurate self‐state estimation using high‐grade navigational sensors without any external support. The parent AUV supports the child AUVs through all processes of deployment, surveying, recovery, and surfacing. This study focuses on the accurate positioning method for the phase of the surveying. The child AUVs have acoustic devices for positioning and communication or cameras; hence, they build a parent‐centered wireless positioning and communication network and can also estimate their self‐states as accurately as the parent AUV, thanks to the positioning relative to the parent AUV. Field experiments with AUVs were conducted in a deep sea (hydrothermal vent field) and a shallow area. We confirmed that long‐range navigation could be achieved while keeping an accuracy that is 5–10 times greater than that achieved by the child AUV alone based on state estimation using sensor data from the experiments in postprocessing. The performance of the proposed system does not depend on the child AUV's performance. We also verified that the proposed method has a certain scalability and a high‐level performance in terms of accuracy, efficiency, and cost by a comparison with other existing navigation methods.