Autonomous Underwater Vehicle Navigation Research Articles

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Path planning is a key technology for autonomous underwater vehicle (AUV) navigation. With the emphasis and research on AUV, AUV path planning technology is continuously developing. Path planning techniques generally include environment modelling methods and path planning algorithms. Based on a brief description of the environment modelling methods, this paper focuses on the path planning algorithms commonly used by AUV. According to the basic principles of the algorithm, the AUV path planning algorithms are divided into four categories: artificial potential field methods, geometric model search methods, random sampling methods, and intelligent bionic methods. In this review, we summarize in detail the development and application of various path planning algorithms in recent years. Meanwhile, we analyse the advantages and disadvantages of various algorithms and their improvement methods. Obstacles, ocean currents, and undersea terrain have an impact on AUV path planning. Therefore, how to deal with the complex underwater environment adds some limits to AUV path planning algorithms. In addition to the external environment, path planning algorithms also need to consider AUV’s physical constraints, such as energy constraints and motion constraints. Then, we analyse the motion constraints in AUV path planning. Finally, we discuss the development direction of AUV path planning algorithm. Time-varying ocean currents, special obstacles, multiobjective constraints, and practicability will be the problems that AUV path planning algorithms need to solve.

Hierarchical Motion Planning of AUVs in Three Typical Marine Environments

Broad waters, harbor waters, and waterway waters make up more than 90% of autonomous underwater vehicles (AUV) navigation area, and each of them has its typical environmental characteristics. In this paper, a three-layer AUV motion planning architecture was designed to improve the planning logic of an AUV when completing complex underwater tasks. The AUV motion planning ability was trained by the improved deep deterministic policy gradient (DDPG) combined with the experience pool of classification. Compared with the traditional DDPG algorithm, the proposed algorithm is more efficient. Using the strategy obtained from the training and the motion planning architecture proposed in the paper, the tasks of AUVs searching in broad waters, crossing in waterway waters and patrolling in harbor waters were realized in the simulation experiment. The reliability of the planning system was verified in field tests.

Virtual DVL Reconstruction Method for an Integrated Navigation System Based on DS-LSSVM Algorithm

Autonomous underwater vehicle (AUV) mostly relies on an integrated navigation system, which consists of a strap-down inertial navigation system (SINS) and Doppler velocity log (DVL). The integrated system provides continuous and accurate navigation information when compared to stand-alone SINS or DVL. However, the dependence of DVL signals on the acoustic environment may cause any DVL malfunction due to marine organisms or strong wave-absorbing material. This article introduces a novel method utilizing Dempster–Shafer (DS) theory augmented by least squares support vector machines (LSSVMs) known as DS-LSSVM. The SINS and DVL data fusion are designed by DS theory whereas LSSVM models the SINS error. The virtual DVL is built by the proposed DS-LSSVM method, which makes AUV navigation purposes possible during the long-term DVL outage. The test results demonstrate the effectiveness of the proposed virtual DVL signal estimation method. In addition, the positioning accuracy of the proposed method outperforms that of the LSSVM method.

Problematic Issues of Intellectualizing the Control System of Autonomous Underwater Vehicles

The paper considers ways of intellectualizing the control processes of autonomous underwater vehicles (AUVs) by the example of solving three problems, on which the successful use of AUVs largely depends. The first problem is to create an AUV control system (CS), which ensures the achievement of the mission’s objective in the event of emergencies caused by both external and internal reasons, as well as deliberate and unintentional counteraction. It is shown that for the construction of the AUV’s CS the decentralized multi-agent structure is most suitable, in which each AUV system is an independent intellectual agent with its own control system. The control system must be equipped with a set of adaptive algorithms that ensure: control of AUV in the event of emergency situations, taking into account restrictions on the supply of electric energy, speed, accuracy of autonomous underwater navigation, range of hydro-acoustic communication; rational distribution of energy resources by AUV systems in accordance with the current situation; maintaining the functional stability of the AUV with a partial malfunction of technical means. The second problem is to create an underwater navigation system that ensures the accomplishment of AUV missions at great distances from the base point. Since AUV navigation using only on-board means (inertial navigation system and lag) does not provide the necessary accuracy, a prerequisite for AUV navigation over long distances is to conduct an observation using external sources, the choice of which in the circumstances is a non-trivial task. The third problem is to create a network underwater communication system (NUCS), which provides for the group application of AUVs. The ground analogue of NUCS is network radio communication. But if the latter is fairly well developed, then the former only takes the initial steps. This is due to both the later practical relevance of NUCS, and many fundamental physical factors that impede the development of NUCS, which include: a substantially limited frequency band that can be used in practice for signal transmission; a large propagation time of the hydroacoustic signal compared to the radio signal; the formation of extended shadow zones and fading of the connected signal due to its multipath propagation; significant Doppler distortions, fast variability of the characteristics of the hydroacoustic medium.

Multiple Model Algorithm for Single-Beacon Navigation of Autonomous Underwater Vehicle without Its A Priori Position. Part 2. Simulation

The algorithm presented earlier is detailed for the case when an autonomous underwater vehicle (AUV) is equipped with a strapdown inertial navigation system (SINS) based on angular rate sensors and a water speed log. The algorithm simulation results for a SINS of navigation grade, a dual axis water speed log, two trajectories of the AUV motion when approaching the beacon to a distance of 100 m; two values of the AUV’s speed; continuous and fragmented diagrams of received acoustic measurements are presented. The results show that the algorithm provides the AUV positioning accuracy of several meters even in the situations when the received acoustic measurements are discontinuous. It has been found that the accuracy characteristics provided by the algorithm adequately reflect the level of real errors.

Application of Passive Underwater Landmarks for Autonomous Unmanned Underwater Vehicles Navigation

The article describes the design of passive underwater landmarks (PUL) and their use for navigation of autonomous unmanned underwater vehicles (AUV). The procedure of AUV observation using PUL is considered. It is shown that with proper maneuvering of the AUV during observation, the accuracy of determining its coordinates is limited only by the accuracy of the PUL positioning during installation. This accuracy also affects the distance between neighboring PULs installed along the AUV’s route. If the AUV is equipped with a high-precision autonomous navigation system, the PULs can be installed at the intervals of several hundred kilometers.

A Novel Neural Network-Based SINS/DVL Integrated Navigation Approach to Deal with DVL Malfunction for Underwater Vehicles

A navigation grade Strapdown Inertial Navigation System (SINS) combined with a Doppler Velocity Log (DVL) is widely used for autonomous navigation of underwater vehicles. Whether the DVL is able to provide continuous velocity measurements is of crucial importance to the integrated navigation precision. Considering that the DVL may fail during the missions, a novel neural network-based SINS/DVL integrated navigation approach is proposed. The nonlinear autoregressive exogenous (NARX) neural network, which is able to provide reliable predictions, is employed. While the DVL is available, the neural network is trained by the body frame velocity and its increment from the SINS and the DVL measurements. Once the DVL fails, the well trained network is able to forecast the velocity which can be used for the subsequent navigation. From the experimental results, it is clearly shown that the neural network is able to provide reliable velocity predictions for about 200 s–300 s during DVL malfunction and hence maintain the short-term accuracy of the integrated navigation.

Multiple Model Algorithm for Single-Beacon Navigation of Autonomous Underwater Vehicle without Its A Priori Position. Part 1. Mathematical Formulation

The article considers a problem of autonomous underwater vehicle (AUV) positioning with acoustic measurements of range and possibly radial velocity relative to a single beacon, velocity components and coordinate increments from an inertial navigation system, and the data from a water-speed log or a ground log. Interruptions in acoustic measurements reception are acceptable. Availability of a priori information on relative position of AUV and the beacon is not assumed. In order to solve the problem, the author proposes a multiple model algorithm based on a bank of extended Kalman filters which independently estimate the initial horizontal range and errors of the used data under different hypotheses about a value of AUV initial azimuth relative to the beacon. Current coordinates of AUV are determined by the outputs of filters, taking into account the a posteriori probabilities of the corresponding hypotheses. The algorithm is rather simple for programming and does not require much computational power.

An AUV localization and path planning algorithm for terrain-aided navigation

Terrain-aided navigation (TAN) holds high potential for long-term accurate navigation of autonomous underwater vehicles (AUVs), and path planning algorithms are essential in TAN to decrease positioning errors by avoiding flat areas. This study proposed an AUV localization and path planning algorithm for TAN, which consists of a value function calculation and online path planning. In the value function calculation, the topographic complexity is treated as a factor that influences AUV state transition probabilities to calculate the optimal policy; meanwhile, the online path planning applies a particle filter to localize and command AUVs, and particle weights are calculated according to topographic complexity. Simulation experimental results demonstrate that this algorithm could provide paths with accurate TAN location results and good maneuvering performance.

Neural-Network-Based AUV Navigation for Fast-Changing Environments

For an autonomous underwater vehicle (AUV), navigation is a key functionality. Dead-reckoning (DR) navigation is an important class among all the AUV navigation methods. In DR, the measurement errors of inertial sensors (such as gyroscopes and accelerometers) lead to accumulated errors with time, which affect navigation accuracy significantly. Especially, accumulated errors in fast-changing environments, such as waves near or on the surface, are tough to handle. In this article, we propose a neural-network-based AUV navigation method for fast-changing environments, called NN-DR. NN-DR employs the neural network to predict pitch angles accurately, which is our core contribution. In NN-DR, we smoothly integrate the Kalman filter, neural network, and velocity compensation to reduce accumulated errors. Extensive simulation experiments are conducted to test the correctness and stability of NN-DR, and the results show that NN-DR is very effective in lowering accumulated errors. For instance, at time 300 s, NN-DR achieves superior performance on accuracy for navigation, about 160 times than the state-of-the-art DR methods, demonstrating great advantage on AUV navigation for fast-changing environments.

Model identification of a Marine robot in presence of IMU-DVL misalignment using TUKF

In today’s world, control and navigation of autonomous underwater vehicles (AUVs) are quite challenging issues. In these fields, obtaining an identified dynamic model of AUV is a vital part. In this paper, a method for parameter estimation of an AUV planar model is proposed, which uses augmented state space technique and Square Root Transformed Unscented Kalman Filter (SR-TUKF) as an estimator. Furthermore, by modeling, misalignment between Inertial Measurement Unit (IMU) and Doppler Velocity Log (DVL) is estimated, simultaneously. Parameter identification is conducted using data of an AUV, equipped with Gyroscope, DVL and Encoder for measuring control inputs, in a planar maneuver. According to the experimental results, the output of the identified model has matched appropriately to the output of sensors in validation maneuvers. With and without considering the estimated misalignment between IMU-DVL, navigation is performed by fusing inertial navigation system (INS) with DVL. The results show an improvement in the end point error with respect to the travel distance by 53 percent in a maneuver with a distance of 6557 meters. Therefore, the proposed method for estimation of a planar model of AUV and misalignment of sensors has a plausible result, experimentally verified by raw data of a simple maneuver.

2D Estimation of Velocity Relative to Water and Tidal Currents Based on Differential Pressure for Autonomous Underwater Vehicles

Reliable navigation of autonomous underwater vehicles (AUVs) depends on the quality of their state estimation. Providing robust velocity estimation thus plays an important role. While water currents are main contributors to the navigational uncertainty of AUVs, they are also an important variable for oceanographic research. For both reasons, water current estimation is desirable during AUV operations. State of the art velocity estimation relies on expensive acoustic sensors with considerable energy requirements and a large form factor such as Doppler Velocity Logs (DVL) and Acoustic Doppler Current Profilers (ADCP), while water currents are either estimated with the same sensors, or with algorithms that require accurate position feedback. In this letter, we introduce a low-cost, lightweight and energy efficient sensor (DPSSv2) to estimate fluid relative velocity in 2D based on differential pressure. The sensor is validated in field trials on-board an AUV in the presence of tidal currents. We further show that, while moving against the currents, our device is capable of estimating tidal currents in situ with comparable accuracy to a DVL, given a source for absolute vehicle velocity. Additionally, we establish the limitations of the current design of DPSSv2 while moving with the currents.

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

Development of Autonomous Underwater Vehicles (AUVs) has permitted the automatization of many tasks originally achieved with manned vehicles in underwater environments. Teams of AUVs designed to work within a common mission are opening the possibilities for new and more complex applications. In underwater environments, communication, localization, and navigation of AUVs are considered challenges due to the impossibility of relying on radio communications and global positioning systems. For a long time, acoustic systems have been the main approach for solving these challenges. However, they present their own shortcomings, which are more relevant for AUV teams. As a result, researchers have explored different alternatives. To summarize and analyze these alternatives, a review of the literature is presented in this paper. Finally, a summary of collaborative AUV teams and missions is also included, with the aim of analyzing their applicability, advantages, and limitations.

Model-Aided Localization and Navigation for Underwater Gliders Using Single-Beacon Travel-Time Differences.

An accurate motion model and reliable measurements are required for autonomous underwater vehicle localization and navigation in underwater environments. However, without a propeller, underwater gliders have limited maneuverability and carrying capacity, which brings difficulties for modeling and measuring. In this paper, an extended Kalman filter (EKF)-based method, combining a modified kinematic model of underwater gliders with the travel-time differences between signals received from a single beacon, is proposed for estimating the glider positions in a predict-update cycle. First, to accurately establish a motion model for underwater gliders moving in the ocean, we introduce two modification parameters, the attack and drift angles, into a kinematic model of underwater gliders, along with depth-averaged current velocities. The attack and drift angles are calculated based on the coefficients of hydrodynamic forces and the sensor-measured angle variation over time. Then, instead of satisfying synchronization requirements, the travel-time differences between signals received from a single beacon, multiplied by the sound speed, are taken as the measurements. To further reduce the EKF estimation error, the Rauch-Tung-Striebel (RTS) smoothing method is merged into the EKF system. The proposed method is tested in a virtual spatiotemporal environment from an ocean model. The experimental results show that the performance of the RTS-EKF estimate is improved when compared with the motion model estimate, especially by 46% at the inflection point, at least in the particular study developed in this article.

Navnet: AUV Navigation Through Deep Sequential Learning

Achieving accurate navigation and localization is crucial for Autonomous Underwater Vehicle (AUV). Traditional navigation algorithms, such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), require the system model and measurement model for state estimation to obtain the AUV position. However, this may introduce modeling errors and state estimation errors which will affect the final precision of AUV navigation system to a certain extent. To avoid these problems, in this paper, we proposed a deep framework - NavNet - by taking AUV navigation as a deep sequential learning problem. Firstly, the proposed NavNet can take raw sensor data at different frequencies as input, which benefits from the sequential learning capability of Recurrent Neural Network (RNN). Secondly, NavNet takes advantage of a simplified attention mechanism and Fully Connected (FC) layers to output AUV displacements per unit time, which accomplishes low-frequency AUV navigation by accumulation of it. More importantly, there is no need for the model building and state estimation with NavNet, which avoids the import of relevant errors. We compare the performance of NavNet to EKF and UKF using collected data by running Sailfish in the sea. Experimental results show that NavNet has an excellent performance in terms of both the navigation accuracy and fault tolerance. In addition, a reliable fusion strategy of NavNet and conventional method is applied to achieve high-frequency AUV navigation. The experimental results show that the proposed architecture can be a reliable supplement to limit the error growth of conventional algorithms.

Experimental evaluation of outliers filtering techniques in networked acoustic localisation systems

Localisation-aware underwater networks are gaining increasing attention in the marine robotics community thanks to their ability of providing navigational services. This can be beneficial in a number of applications, as for instance to support the navigation of Autonomous Underwater Vehicles (AUVs) when traditional aiding systems are impractical or not cost effective. However, the unreliability of the acoustic channel, together with the additional overhead and constraints introduced by the network itself, result in localisation measurements that are intrinsically sporadic. This makes the outlier filtering problem of localisation measurements obtained through networked underwater systems particularly important and challenging. This paper uses experimental data to compare the integration of two different outlier filtering methodologies in an existing network-aided AUV navigation filter. The first method aims at pre-filtering the measurements to identify and discard potential outliers before they are fused in the navigation filter. The second one modifies the correction step of the Kalman filter to integrate measurements in an outlier-robust way. Results show that when the navigation filter is made outlier-robust the navigation performance increases and the system becomes less sensitive to tuning, a key characteristic for fielded systems.

Применение подводных пассивных ориентиров для навигации автономных необитаемых подводных аппаратов

The article describes the design of passive underwater landmarks (PUL) and their use for navigation of autonomous unmanned underwater vehicles (AUV). The procedure of AUV observation using PUL is considered. It is shown that with proper maneuvering of the AUV during observation, the accuracy of determining its coordinates is limited only by the accuracy of the PUL positioning during installation. This accuracy also affects the distance between neighboring PULs installed along the AUV’s route. If the AUV is equipped with a high-precision autonomous navigation system, the PULs can be installed at the intervals of several hundred kilometers.

Single-Hydrophone Low-Cost Underwater Vehicle Swarming

Swarms of robots are starting to appear in aerial and ground robotics across multiple operational domains: be it for search-and-rescue mapping, or Olympic light shows, controlled groups of drones are able to be coordinated in groups. This capability does not currently extend to the underwater domain, for a reason of basic physics: the attenuation of light in water is 10 orders of magnitude greater than that in air, rendering ineffective the navigation and communication technologies used in terrestrial robotics. Autonomous underwater vehicle (AUV) navigation is either expensive or unwieldy, requiring high-power sensors such as inertial navigation sensors (INS) that cost hundreds of thousands of dollars, frequent surfacing for GPS, or the deployment of geo-located acoustic beacons, limiting the operational area which is a severe constraint given the scale of oceanographic sensing. To do swarms of underwater vehicles will require a navigation and communication scheme that allows vehicles to remain together in an area while prosecuting a mission without these constraints. This paper suggests a system that would make underwater swarming possible by using single-transducer, Doppler-based and multi-frequency attenuation-based acoustic navigation without time-synchronization for multi-vehicle swarming. In this solution, a leader with good navigation carries a multi-frequency sound source. Scalable numbers of followers equipped with a custom low-cost acoustic package then adapt heading based on Doppler-shifted frequency and range using multi-frequency difference in absorption. In this paper, the theory behind this technique is presented and tested under different simulation conditions. Design of a prototype low-cost system is also described.

A Novel Underwater Simultaneous Localization and Mapping Online Algorithm Based on Neural Network

The navigation and localization of autonomous underwater vehicles (AUVs) in seawater are of the utmost importance for scientific research, petroleum engineering, search and rescue, and military missions concerning the special environment of seawater. However, there is still no general method for AUVs navigation and localization, especially in the featureless seabed. The reported approaches to solving AUVs navigation and localization problems employ an expensive inertial navigation system (INS), with cumulative errors and dead reckoning, and a high-cost long baseline (LBL) in a featureless subsea. In this study, a simultaneous localization and mapping (AMB-SLAM) online algorithm, based on acoustic and magnetic beacons, was proposed. The AMB-SLAM online algorithm is based on multiple randomly distributed beacons of low-frequency magnetic fields and a single fixed acoustic beacon for location and mapping. The experimental results show that the performance of the AMB-SLAM online algorithm has a high robustness. The proposed approach (the AMB-SLAM online algorithm) provides a low-complexity, low-cost, and high-precision online solution to the AUVs navigation and localization problem in featureless seawater environments. The AMB-SLAM online solution could enable AUVs to autonomously explore or autonomously intervene in featureless seawater environments, which would enable AUVs to accomplish fully autonomous survey missions.

On AUV Control with the Aid of Position Estimation Algorithms Based on Acoustic Seabed Sensing and DOA Measurements.

This article discusses various approaches to the control of autonomous underwater vehicles (AUVs) with the aid of different velocity-position estimation algorithms. Traditionally this field is considered as the area of the extended Kalman filter (EKF) application: It became a universal tool for nonlinear observation models and its use is ubiquitous. Meanwhile, the specific characteristics of underwater navigation, such as an incomplete sets of measurements, constraints on the range metering or even impossibility of range measurements, observations provided by rather specific acoustic beacons, sonar observations, and other features seriously narrow the applicability of common instruments due to a high level of uncertainty and nonlinearity. The AUV navigation system, not being able to rely on a single source of position estimation, has to take into account all available information. This leads to the necessity of various complex estimation and data fusion algorithms, which are the matter of the present article. Here we discuss some approaches to the AUV position estimation such as conditionally minimax nonlinear filtering (CMNF) and unbiased pseudo-measurement filters (UPMFs) in conjunction with velocity estimation based on the seabed profile acoustic sensing. The presented estimation algorithms serve as a basis for a locally optimal AUV motion control algorithm, which is also presented.