Autonomous Underwater Vehicle Navigation Research Articles

A Hybrid Approach Based on Improved AR Model and MAA for INS/DVL Integrated Navigation Systems

In navigation of autonomous underwater vehicles (AUVs), the estimation of position is an important issue, especially, when the sensors such as gyroscopes contain a lot of noise, and the velocity information of Doppler velocity log (DVL) is affected by the motion attitude of the vehicle. In this paper, based on an improved auto regressive (AR) model, a real-time filter is utilized for gyroscope signal de-noising. Meanwhile, according to the characteristics of the AUV, the influence of the vehicle attitude on the DVL velocity measurement error is analyzed and a motion attitude assist (MAA) method based on error model is introduced for enhancing DVL velocity accuracy. In this paper, using the proposed hybrid approach, an inertial navigation system (INS)/DVL integrated navigation system is designed. The proposed approach is evaluated by simulation and experimental test in different acceleration bound, and the existence of the DVL outage for an AUV. The results indicate that the precisions of the velocity and position are improved effectively, especially in complex motion attitude and long sailing conditions.

Autonomous Underwater Vehicle Navigation Using Sonar Image Matching based on Convolutional Neural Network

This paper presents an image matching algorithm based on convolutional neural network (CNN) to aid in the navigating of an Autonomous Underwater Vehicle (AUV) where external navigation aids are not available. We aim to solve the problem where traditional image feature representations and similarity learning are not learned jointly and to improve the matching accuracy of sonar images in deep ocean with dynamic backgrounds, low-intensity and high-noise scenes. In our work, the proposed CNN-based model can train the texture features of sonar images without any manually designed feature descriptors, which can jointly optimize the representation of the input data conditioned on the similarity measure being used. The validation studies show the feasibility and veracity of the proposed method for many general and offset cases using collected sonar images.

Passive underwater acoustic tags using layered media.

Autonomous Underwater Vehicle (AUV) navigation requires accurate positioning information from the environment. Existing underwater navigation paradigms employ active acoustic transponders that assist in this task, but these more complex and costly systems require maintenance and power. This paper presents instead a passive underwater marker made of different horizontally stacked acoustically reflective materials that is cost effective and relatively simple to service. A marker's characteristic acoustic signature can be detected by AUVs as acoustic backscattering upon tag insonification, and hence be used for navigation purposes.

Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning

The autonomous underwater vehicle (AUV) is widely used to search for unknown targets in the complex underwater environment. Due to the unpredictability of the underwater environment, this paper combines the traditional frontier exploration method with deep reinforcement learning (DRL) to enable the AUV to explore the unknown underwater environment autonomously. In this paper, a grid map of the search environment is built by the grid method. The designed asynchronous advantage actor-critic (A3C) network structure is used in the traditional frontier exploration method for target search tasks. This network structure enables the AUV to learn from its own experience and generate search strategies for the various unknown environment. At the same time, DRL and dual-stream Q-learning algorithms are used for AUV navigation to further optimize the search path. The simulations and experiments in an unknown underwater environment with different layouts show that the proposed algorithm can accomplish target search tasks with a high success rate, and it can adapt to different environments. In addition, compared to other search methods, the frontier exploration algorithm based on DRL can search a wider environment faster, which results in a higher search efficiency and reduced search time.

Underwater terrain-aided navigation method based on improved Gaussian sum particle filtering

To solve the nonlinear Bayesian estimation problem in underwater terrain-aided navigation, a terrain-aided navigation method based on improved Gaussian sum particle filter is proposed. This method approximates the Bayesian function using multiple Gaussian components, and the components can be obtained by radial basis function neural network. This method has no resampling process, the particle depletion of particle filtering is eliminated in principle. The simulation shows that the proposed method has good matching performance, which is suitable for autonomous underwater vehicle navigation.

On AUV Navigation Based on Acoustic Sensing of the Seabed Profile

Abstract—A method for determining the current position of an autonomous underwater vehicle (AUV) based on an algorithm for determining the AUV velocity using acoustic sensing of the seabed profile is proposed. The method uses velocity measurement as an additional sensor of the absolute velocity whose data are shared with the inertial navigation system (INS) as the input data of a filter determining the AUV position.

Observability Degree-Based AUV Single Beacon Navigation Trajectory Optimization with Range-Only Measurements

Aiming at the problem of autonomous underwater vehicle navigation trajectory optimization using single beacon location under direct route condition, a nonlinear system model for AUV single beacon navigation is established, and the linearized system model with error states is constructed by polar coordinate transformation and simplification. Then, current disturbance is considered. To find out the optimum path to utilize range-only measurements, a novel observability degree-based analysis method is proposed, which gives a quantitative insight into convergence characteristics of the error states by using the eigenvalues of the normalized error covariance matrix. Simulation experiments are done to test convergence characteristics of AUV integrated navigation error states with single beacon range-only measurements under direct route control conditions. The experimental results show that the proposed control method is effective, and it has an important engineering application value and provides us with an optimized path.

An Underwater Digital Network on Acoustic Modems with EviNS Framework: A Case Study

The new technical solution proposed in this paper significantly reduces costs for creating underwater acoustic networks (UWAN) and their exploitation. Such a network has small energy-efficient nodes (modems that function, for example, as network communication devices, nodes of long-baseline bottom antenna, etc.). The UWAN performance parameters are considered with regard to the network application for positioning and navigation of autonomous underwater vehicles. Of particular interest are the results of the study on latencies of small data packet delivery in a network as well as the probability of delivering small data packets from the source to the final recipient (for example, control commands, sensor data, data on the positions of long-baseline nodes, and sync sequences). The solution is based on the development of a dedicated EviNS Framework (Evologics intelligent Networking Software Framework) installed directly on the platform of the underwater acoustic modem (UWAM) as part of its standard software. The main performance parameters of the digital UWAN, which uses part of the EviNS framework protocols, namely, a combination of the protocol with uncoordinated access to the medium and the routing protocol based on the sequence number controlled flooding, are discussed and the results of the experimental case study are given. The values of probabilities and latencies in data delivery from the source to the recipient obtained in the experiments provide the network user with the information necessary to schedule tasks involving group interaction and real-time coordination of the UWAN nodes, in particular, mobile nodes on underwater vehicles (at least, in the UWAN with a configuration and size similar to those given in the paper).

A single acoustic beacon-based positioning method for underwater mobile recovery of an AUV

This article presents a navigation method for an autonomous underwater vehicle being recovered by a human-occupied vehicle. The autonomous underwater vehicle is considered to carry underwater navigation sensors such as ultra-short baseline, Doppler velocity log, and inertial navigation system. Using these sensors’ information, a navigation module combining the ultra-short baseline positioning and inertial positioning is established. In this study, there is assumed to be no communication between the autonomous underwater vehicle and human-occupied vehicle; thus, to obtain the autonomous underwater vehicle position in the inertial coordinate, a conjecture method to obtain the human-occupied vehicle coordinates is proposed. To reduce the error accumulation of autonomous underwater vehicle navigation, a method called one-step dead reckoning positioning is proposed, and the one-step dead reckoning positioning is treated as a correction to combine with ultra-short baseline positioning by a data fusion algorithm. One-step dead reckoning positioning is a positioning method based on the previous time-step coordinates of the autonomous underwater vehicle.

Vertical Obstacle Avoidance and Navigation of Autonomous Underwater Vehicles with H∞ Controller and the Artificial Potential Field Method

An H∞ controller combined with an Artificial Potential Field Method (APFM) was applied to seabed navigation for Autonomous Underwater Vehicles (AUVs), aimed particularly at obstacle avoidance and bottom-following operations in the vertical plane. Depth control and altitude control prevented the AUV from colliding with the sea bottom or with obstacles and prevented the AUV from diving beyond its maximum depth limit when bottom following. Simulation and laboratory trials with various seabed contours indicated that with the H∞ controller, the AUV was able to safely reach appointed destinations without collisions. Tests also showed that the H∞ controller was robust and suppressed interference, hence ensuring the precision of its navigation control. The proposed H∞ controller combined with the APFM has thus been proved to be both feasible and effective.

A dynamic path planning method for terrain-aided navigation of autonomous underwater vehicles

Terrain-aided navigation (TAN) is one of the most effective approaches for solving the long-range navigation of autonomous underwater vehicles. However, the positioning accuracy of TAN can be greatly influenced by the terrain information of matching areas. A TAN system might also fail catastrophically when the seabed topography changes. To address these problems, a dynamic path planning method for TAN, which includes environment modelling, offline path planning, and online re-planning, is proposed in this paper. Terrain standard deviation is applied to represent terrain information in environment modelling, and the Mahalanobis distance and roulette approach are introduced in the offline path planning approach. Furthermore, the topographic changes are identified and handled via an online re-planning approach. Simulation experiments are conducted, and comparisons are made with the A* algorithm. According to the experimental results, the proposed method improves the positioning accuracy of TAN during the mission, and its ability to deal with seabed topographic changes is also verified.

Parameter state estimation for bistatic sonar systems

The use of autonomous underwater vehicles (AUVs) cooperating in a network for anti-submarine warfare surveillance operations is of topical interest. Each AUV has to localise and track targets (submarines) robustly and precisely. This can be realised by a bistatic sonar configuration. The precise knowledge of the bistatic system parameters is mandatory for target tracking. These are in particular the positions of the acoustic sources, transmission times, and the position and heading of the AUV sonar sensor. However, these parameters often are not precisely known, e.g. for non-cooperative acoustic sources or due to navigation uncertainties of the AUV. Therefore, the authors consider the inverse problem, i.e. they estimate the bistatic system parameters by exploiting sonar echoes from known stationary `targets' like wrecks or small islands. Their implementation of the estimation method is based on the multihypothesis tracking technique. Results are discussed for two applications: The first one is estimation of the parameters of a non-cooperative source. The second application focuses on the estimation of the receiver parameters; in particular they show that their approach can be used to increase robustness of AUV navigation. Their algorithms are tested with simulated and real data recorded by the Centre for Maritime Research and Experimentation.

Controlled Lagrangian Particle Tracking: Error Growth Under Feedback Control

The method of Lagrangian particle tracking (LPT) is extended to autonomous underwater vehicles (AUVs) that are modeled as controlled particles. Controlled LPT (CLPT) evaluates the performance of ocean models used for the navigation of AUVs by computing the differences between predicted vehicle trajectories and actual vehicle trajectories. Such difference, measured by the controlled Lagrangian prediction error (CLPE), demonstrates growth rate that is influenced by the accuracy of the ocean model and the strength of the feedback control laws used for vehicle navigation. Despite the limited accuracy and resolution of the ocean model, the error growth can be bounded by feedback control when localization service is available, which is a unique property of CLPT. Theoretical relationship among CLPE growth rate, quality of ocean models, and feedback control are established for two control strategies: a transect-following controller and a station-keeping controller that are often used by AUVs. Upper bounds for error growth are derived and verified by both simulation and experimental data collected during the operations of underwater gliders in coastal ocean.

Experimental Evaluation of Accuracy and Efficiency of Alternating Landmark Navigation by Multiple AUVs

This paper presents the experimental evaluation of the alternating landmark navigation (ALN) by multiple autonomous underwater vehicles (AUVs) using latest results of sea experiments and tank tests in terms of accuracy and efficiency. Self-localization is essential for AUV navigation, and AUVs are typically supported by long base line or super short base line of the support system. Against these methods, the ALN was proposed, where each AUV alternately becomes a landmark, which remains stationary on the seafloor, whereas the other AUVs can navigate based on acoustic positioning with the landmark AUV. The ALN realizes stable navigation without any support. Our goal is to realize surveying near the seafloor using only AUVs. The performance of the ALN in obstacle-existing environments was evaluated through the simulation to discuss the expandability of the ALN. The ALN was implemented on AUVs “Tri-Dog 1,” “Tri-TON,” and “Tri-TON 2.” On the basis of the sea experiments and the tank tests conducted with these AUVs, the performance of the ALN was verified. In particular, accuracy and efficiency of the ALN were evaluated through the tank test. Finally, it is shown that the ALN can realize accurate and efficient survey of the seafloor by only AUVs without any support.

Observability Analysis of Heading Aided INS for a Maneuvering AUV

Underwater navigation of autonomous underwater vehicles (AUVs) is a challenging task that requires the fusion of multiple sensors used as aiding to the vehicle inertial navigation system. In this paper, we focus on the problem of fusing heading measurements under different maneuvering conditions. We analyze the observability of the heading measurement fusion problem and derive the observable and unobservable subspaces as a function of the AUV’s maneuver. Using this analysis, we are able to predict the convergence of the error states for different maneuvers and thus identify the quality of the navigation solution at different mobility patterns. As expected, the results show that the down misalignment error and gyro z-axis bias are always observable. In addition, our results showed that for some maneuvers, the rest of the gyro biases also become observable. Our numerical simulation and sea experiments verify our analytic solutions.

Autonomious Undetrwater Vehicles Navigation and Localization Systems: A Survey

Localization and navigation are two serious and challenging obstacles in underwater communication due to the physical nature of the sea water environment and the fast attenuation of higher frequency signals. This paper presents a review of the most used techniques for localization and navigation of Autonomous Underwater Vehicles (AUVs) in underwater environment.

Development of a Trimaran ASV

A trimaran is proposed as an autonomous surface vehicle aimed to oceanographic field missions and research on navigation of autonomous underwater vehicles. The paper presents a discussion comparing this kind of vessel to the monohull and catamaran types. Naval architecture aspects of the vehicle design are summarized, and details on the hardware and software control architecture are presented as well.

An Integrated System for Geophysical Navigation of Autonomous Underwater Vehicles.

This paper presents a geophysical navigation system for small, affordable underwater robotic vehicles that exploits the information provided by geomagnetic features observed on the seafloor in order to correct the unavoidable drift in position error incurred by dead-reckoning navigation. The approach described combines a sequential estimation algorithm previous developed by the authors with a complementary profile correlation method. The outcome of this integration enhances the estimated position of a marine robotic vehicle in difficult operation conditions.

Visual navigation of an autonomous underwater vehicle based on the global search of image correspondences

A task of autonomous underwater vehicle (AUV) navigation is considered in the paper. The images obtained from an onboard stereo camera are used to build point clouds attached to a particular AUV position. Quantized SIFT descriptors of points are stored in a metric tree to organize an effective search procedure using a best bin first approach. Correspondences for a new point cloud are searched in a compact group of point clouds that have the largest number of similar descriptors stored in the tree. The new point cloud can be positioned relative to the other clouds without any prior information about the AUV position and uncertainty of this position. This approach increases the reliability of the AUV navigation system and makes it insensitive to data losses, textureless seafloor regions and long passes without trajectory intersections. Several algorithms are described in the paper: an algorithm of point clouds computation, an algorithm for establishing point clouds correspondence, and an algorithm of building groups of potentially linked point clouds to speedup the global search of correspondences. The general navigation algorithm consisting of three parallel subroutines: image adding, search tree updating, and global optimization is also presented. The proposed navigation system is tested on real and synthetic data. Tests on real data showed that the trajectory can be built even for an image sequence with 60% data losses with successive images that have either small or zero overlap. Tests on synthetic data showed that the constructed trajectory is close to the true one even for long missions. The average speed of image processing by the proposed navigation system is about 3 frames per second with a middle-price desktop CPU.

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.