Autonomous Underwater Vehicle Navigation Research Articles

Experimental validation of magnetic navigation of marine robotic vehicles

Abstract: This paper describes the work developed towards the implementation of magnetic navigation (MAGNAV) methods for small, affordable underwater robotic vehicles. MAGNAV constitutes a particular implementation of a broader class of methods designated as geophysical navigation, which encompasses the already classical terrain-aided, or terrain-referenced, navigation approach. Relying on prior work by the authors, the paper proposes a particle filter navigation algorithm which may be combined with other position estimation methods to increase the robustness of the estimation process. The results of a series of tests performed in the water with a marine robotic vehicle in order to assess the impact of different sensor-vehicle configurations on the quality of the magnetic data used in MAGNAV are reported. The magnetic navigation solution proposed is validated in simulated trials using real magnetic and navigation data acquired with an autonomous marine vehicle in real trials using standard navigation sensors and an affordable total field magnetometer. The results obtained illustrate the high potential of the approach for geophysical navigation of autonomous underwater vehicles.

AUV-Based Plume Tracking: A Simulation Study

This paper presents a simulation study of an autonomous underwater vehicle (AUV) navigation system operating in a GPS-denied environment. The AUV navigation method makes use of underwater transponder positioning and requires only one transponder. A multirate unscented Kalman filter is used to determine the AUV orientation and position by fusing high-rate sensor data and low-rate information. The paper also proposes a gradient-based, efficient, and adaptive novel algorithm for plume boundary tracking missions. The algorithm follows a centralized approach and it includes path optimization features based on gradient information. The proposed algorithm is implemented in simulation on the AUV-based navigation system and successful boundary tracking results are obtained.

Development and Online Validation of an UKF-based Navigation Algorithm for AUVs

The development of precise and robust navigation strategies for Autonomous Underwater Vehicles (AUVs) is fundamental to reach the high level of performance required by complex underwater tasks, often including more than one AUV. One of the main factors affecting the accuracy of AUVs navigation systems is the algorithm used to estimate the vehicle motion, usually based on kinematic vehicle models and linear estimators. In this paper, the authors present a navigation strategy specifically designed for AUVs, based on the Unscented Kalman Filter (UKF). The algorithm proves to be effective if applied to this class of vehicles and allows to achieve a satisfying accuracy improvement compared to standard navigation algorithms. The proposed strategy has been experimentally validated in suitable sea tests performed near the Cala Minnola wreck (Levanzo, Aegadian Islands, Sicily, Italy). The vehicles involved are the Typhoon AUVs, developed and built by the Department of Industrial Engineering of the University of Florence during the THESAURUS Tuscany Region project and the European ARROWS project for exploration and surveillance of underwater archaeological sites. The proposed algorithm has been implemented online on the AUVs and tested. The validation of the proposed strategy provided accurate results in estimating the vehicle dynamic behavior, better than those obtained through standard navigation algorithms.

Vision-Based Autonomous Underwater Vehicle Navigation in Poor Visibility Conditions Using a Model-Free Robust Control

This paper presents a vision-based navigation system for an autonomous underwater vehicle in semistructured environments with poor visibility. In terrestrial and aerial applications, the use of visual systems mounted in robotic platforms as a control sensor feedback is commonplace. However, robotic vision-based tasks for underwater applications are still not widely considered as the images captured in this type of environments tend to be blurred and/or color depleted. To tackle this problem, we have adapted thelαβcolor space to identify features of interest in underwater images even in extreme visibility conditions. To guarantee the stability of the vehicle at all times, a model-free robust control is used. We have validated the performance of our visual navigation system in real environments showing the feasibility of our approach.

Fusion of Redundant Aided-inertial Sensors with Decentralised Kalman Filter for Autonomous Underwater Vehicle Navigation

<p class="p1">Most submarines carry more than one set of inertial navigation system (INS) for redundancy and reliability. Apart from INS systems, the submarine carries other sensors that provide different navigation information. A major challenge is to combine these sensors and INS estimates in an optimal and robust manner for navigation. This issue has been addressed by Farrell1. The same approach is used in this paper to combine different sensor measurements along with INS system. However, since more than one INS system is available onboard, it would be better to use multiple INS systems at the same time to obtain a better estimate of states and to provide autonomy in the event of failure of one INS system. This would require us to combine the estimates obtained from local filters (one set of INS system integrated with external sensors), in some optimal way to provide a global estimate. Individual sensor and IMU measurements cannot be accessed in this scenario. Also, autonomous operation requires no sharing of information among local filters. Hence a decentralised Kalman filter approach is considered for combining the estimates of local filters to give a global estimate. This estimate would not be optimal, however. A better optimal estimate can be obtained by accessing individual measurements and augmenting the state vector in Kalman filter, but in that case, corruption of one INS system will lead to failure of the whole filter. Hence to ensure satisfactory performance of the filter even in the event of failure of some INS system, a decentralised Kalman filtering approach is considered.</p>

Observability analysis of DVL/PS aided INS for a maneuvering AUV.

Recently, ocean exploration has increased considerably through the use of autonomous underwater vehicles (AUV). A key enabling technology is the precision of the AUV navigation capability. In this paper, we focus on understanding the limitation of the AUV navigation system. That is, what are the observable error-states for different maneuvering types of the AUV? Since analyzing the performance of an underwater navigation system is highly complex, to answer the above question, current approaches use simulations. This, of course, limits the conclusions to the emulated type of vehicle used and to the simulation setup. For this reason, we take a different approach and analyze the system observability for different types of vehicle dynamics by finding the set of observable and unobservable states. To that end, we apply the observability Gramian approach, previously used only for terrestrial applications. We demonstrate our analysis for an underwater inertial navigation system aided by a Doppler velocity logger or by a pressure sensor. The result is a first prediction of the performance of an AUV standing, rotating at a position and turning at a constant speed. Our conclusions of the observable and unobservable navigation error states for different dynamics are supported by extensive numerical simulation.

A new method of target tracking by EKF using bearing and elevation measurements for underwater environment

Underwater moving object detection/tracking is critical in various applications such as exploration of natural undersea resources, acquiring of accurate scientific data to maintain regular surveillance of missions, navigation and tactical surveillance. Real time object detection/tracking which tends to obstacle avoidance is possible with an autonomous underwater vehicle (AUV) fitted with sensor(sonar). To bring these applications into effective use, there is a need to evaluate various solutions for the safe navigation of AUV in the significant underwater environment. Convergence time becomes a problem and plays an increasingly important role in safe navigation of AUV applications. To achieve this, several methods, i.e. Kalman Filter (KF), Extended Kalman Filter (EKF) and Particle Filter (PF) have been investigated, although all these methods have their own limitations. In this paper, a new method has been developed wherein tracking algorithm using EKF has been extended to the Bearing and Elevation only Tracking (BEOT) method. By using Monte Carlo approach, the performance of this algorithm has been analyzed. Consequently, the time of convergence has been calculated and accordingly the results have been plotted.

Review of AUV Underwater Terrain Matching Navigation

Underwater terrain matching navigation technology is an important research area for the underwater navigation of Autonomous Underwater Vehicles (AUVs). Terrain matching navigation can realise long-term, subtle, all-weather, and high-precision underwater AUV navigation. In this paper, the research status of the application of AUV underwater terrain matching navigation is reviewed, the system composition, theory and terrain matching methods of underwater terrain matching navigation are summarised and the advantages of a multi-beam bathymetric system in underwater terrain matching navigation are discussed. The current research thoughts are summarised, the key issues are pointed out, and possible future development trends are discussed.

State Estimation and Compression Method for the Navigation of Multiple Autonomous Underwater Vehicles With Limited Communication Traffic

This study proposes a state estimation and compression method for navigating multiple autonomous underwater vehicles (AUVs) toward wide area surveys near seafloors. In the proposed method, a moving AUV navigates by referencing a stationary landmark AUV on the seafloor. By alternating the landmark role, all AUVs can cover a wide area while maintaining low positioning errors. The moving AUV estimates the states (positions and headings) of both moving and landmark AUVs by a stochastic approach called a particle filter. When AUVs exchange their landmark roles, they must share their estimated states. However, state sharing is precluded by the low data rate of acoustical communications in underwater environments. To overcome the problem, this paper proposes a state compression method in which AUVs approximate their states by “particle clustering” based on a clustering method (k-means) and a model evaluation method (Akaike information criterion). The compression method enables AUVs to share their states by communicating small amounts of data. The proposed method was evaluated in simulations of two AUVs navigating over a 300 × 300-m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> seafloor area. Throughout the simulation, the proposed method maintained stable positioning and successful state sharing with small communication data size.

Typhoon at CommsNet13: Experimental experience on AUV navigation and localization

This paper presents two acoustic-based techniques for Autonomous Underwater Vehicle (AUV) navigation within an underwater network of fixed sensors. The proposed algorithms exploit the positioning measurements provided by an Ultra-Short Base Line (USBL) transducer on-board the vehicle to aid the navigation task. In the considered framework the acoustic measurements are embedded in the communication network scheme, causing time-varying delays in ranging with the fixed nodes. The results presented are obtained with post-processing elaborations of the raw experimental data collected during the CommsNet13 campaign, organized and scientifically led by the NATO Science and Technology Organization Centre for Maritime Research and Experimentation (CMRE). The experiment involved several research institutions and included among its objectives the evaluation of on-board acoustic USBL systems for navigation and localization of AUVs. The ISME groups of the Universities of Florence and Pisa jointly participated to the experiment with one Typhoon class vehicle. This is a 300 m depth rated AUV with acoustic communication capabilities originally developed by the two groups for archaeological search in the framework of the THESAURUS project. The CommsNet13 Typhoon, equipped with an acoustic modem/USBL head, navigated within the fixed nodes acoustic network deployed by CMRE. This allows the comparison between inertial navigation, acoustic self-localization and ground truth represented by GPS signals (when the vehicle was at the surface).

Towards autonomy in ROV operations

This paper presents an on-going research project focusing on the development of technology to enable autonomy in ROV operations. The project is a collaborative project between Norwegian offshore industry and academia. Currently, there is a large focus in research on the development of navigation, guidance and control for autonomous underwater vehicles (AUV). This is important as there will be a future demand for subsea inspection, maintenance and repair (IMR) operations with non-cabled systems. A future scenario is to have AUVs stationed on the seafloor in subsea garages. However, state of the art for IMR operations on the Norwegian Continental Shelf is to apply vessel supported ROVs in IMR operations. Efficiency in such operations will imply large cost and time savings. Increased autonomy enables the ROV operator to shift from manual to automatic control utilizing autonomous functions for a number of specific tasks. The research project presented in this paper is novel and the goal is to improve the capabilities of the ROV leaving the operator mainly to supervise operation. The paper discusses different aspects of the technology requirements. This may be useful for researchers working in the area of AUV research, relating this research to industrial needs. The presented project will develop novel integrated sensor platforms with robust perception methods and collision-free motion planning algorithms for subsea inspection and light intervention operations. Moreover, the project will also focus on subsea factory design enabling autonomous operations. The results will be tested, verified and demonstrated in full-scale test beds, as well as at an offshore location.

AUV Terrain-Aided Navigation using a Doppler Velocity Logger

The potential of terrain-aided navigation (TAN) of autonomous underwater vehicles (AUVs) has been demonstrated in the last ten years in a series of trials where high localization accuracy was obtained during long periods of time in different types of terrain. Despite these recent advances, it is recognized by the navigation community that further research is necessary to transform TAN into a mature navigation methodology. The present paper describes the work developed towards the development of terrain navigation methods for small AUVs, relying on standard navigation sensors and dispensing with the need of dedicated sensors for terrain data acquisition. The research described addresses the problem of implementation of terrain navigation in underwater scenarios characterized by smooth sea-bottom topography and very shallow water, where the terrain information available for navigation is scarce. The TAN algorithms and the data fusion methods whose tests are documented in the paper build upon prior theoretical work published by the authors. The experimental results reported were obtained recently through trials in the water performed with an autonomous surface vehicle acting as a proxy to an AUV.

Trajectory Generation for Bathymetry based AUV Navigation and Localization

This paper presents a path planning algorithm to improve the localization estimate of an autonomous underwater vehicle (AUV) based on bathymetry maps without the aid of external landmarks. A particle filter is used in order to fuse the data from an Inertial Measurement Unit (IMU), a downward pointing sonar and an a priori given bathymetry map. Since this method's performance is dependent on the rugosity of the map, a path planning algorithm is proposed in order to avoid such regions and optimize the particle filter performance. By guiding the vehicle to navigate in regions where the filter performance is acceptable, an efficient landmark-free localization algorithm is devised.

Development of a Navigation Algorithm for Autonomous Underwater Vehicles

In this paper, the authors present an underwater navigation system for Autonomous Underwater Vehicles (AUVs) which exploits measurements from an Inertial Measurement Unit (IMU), a Pressure Sensor (PS) for depth and the Global Positioning System (GPS, used during periodic and dedicated resurfacings) and relies on either the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF) for the state estimation. Both (EKF and UKF) navigation algorithms have been validated through experimental navigation data related to some sea tests performed in La Spezia (Italy) with one of Typhoon class vehicles during the NATO CommsNet13 experiment (held in September 2013) and through Ultra-Short BaseLine (USBL) fixes used as a reference (ground truth). Typhoon is an AUV designed by the Department of Industrial Engineering of the Florence University for exploration and surveillance of underwater archaeological sites in the framework of the Italian THESAURUS project and the European ARROWS project. The obtained results have demonstrated the effectiveness of both navigation algorithms and the superiority of the UKF (very suitable for AUV navigation and, up to now, still not used much in this field) without increasing the computational load (affordable for on-line on-board AUV implementation).

Improving Navigational Accuracy for AUVs Based on an Adaptive Modified Square-Root Cubature Kalman Filter

This paper addresses an adaptive modified square-root cubature Kalman filter for the navigation of autonomous underwater vehicles (AUVs). The standard square-root cubature Kalman filter (SCKF) implements the CKF using square-root filtering to reduce computational errors. It can be modified due to the nonlinear system with a linear measurement function. The modification leads to a decrease computational complexity. Sage-Husa noise statistics estimator is combined with the Modified SCKF to estimate the unknown and changing system process noise variance. The experimental results show that compared with the MSCKF and the EKF algorithm, the adaptive MSCKF show the best accuracy for a real system with unknown process noise variance.

Cooperative Navigation for Multiple AUVs Based on Relative Range Measurements with a Single Leader

his paper deals with the cooperative navigation problem of multiple autonomous underwater vehicles (AUV). A novel method which does not depend on a beacon network like in long baseline positioning system is proposed. The principle of this approach is to realize the cooperative localization of AUVs by using relative range measurements between the leader and the follower vehicles by means of an extended Kalman filter. Simulation results that validate the effectiveness of this approach are presented.

Comparing Autonomous Underwater Vehicle (AUV) and Vessel-based Tracking Performance for Locating Acoustically Tagged Fish

Autonomous underwater vehicles (AUV’s) are increasingly used to collect physical, chemical, and biological information in the marine environment. Recent efforts include merging AUV technology with acoustic telemetry to provide information on the distribution and movements of marine fish. We compared surface vessel and AUV tracking capabilities under rigorous conditions in coastal waters near Juneau, Alaska. Tracking surveys were conducted with a REMUS 100 AUV equipped with an integrated acoustic receiver and hydrophone. The AUV was programmed to navigate along predetermined routes to detect both reference transmitters at 20–500 m depths and tagged fish and crabs in situ. Comparable boat surveys were also conducted. Transmitter depth had a major impact on tracking performance. The AUV was equally effective or better than the boat at detecting reference transmitters in shallow water, and significantly better for transmitters at deeper depths. Similar results were observed for tagged animals. Red king crab, Paralithodes camtschaticus, at moderate depths were recorded by both tracking methods, while only the AUV detected Sablefish, Anoplopoma fimbria, at depths exceeding 500 m. Strong currents and deep depths caused problems with AUV navigation, position estimation, and operational performance, but reflect problems encountered by other AUV applications that will likely diminish with future advances, enhanced methods, and increased use.

Preliminary Numerical Study on Designing Navigation and Stability Control Systems for ITS AUV

In this paper, the numerical study of designing on navigation and stability control system for AUV is studied. The study started by initiating hydrostatic forces, added masses, lift force, drag forces and thrust forces. Determining the hydrodynamic force which is the basic need to know the numerical case study on designing on navigation and stability control system for AUV where Autonomous Underwater vehicles (AUV). AUV is capably underwater vehicle in moving automatically without direct control by humans according to the trajectory. The result of numerical study is properly to be the reference for the next developing for AUV.

AUV Navigation and Localization: A Review

Autonomous underwater vehicle (AUV) navigation and localization in underwater environments is particularly challenging due to the rapid attenuation of Global Positioning System (GPS) and radio-frequency signals. Underwater communications are low bandwidth and unreliable, and there is no access to a global positioning system. Past approaches to solve the AUV localization problem have employed expensive inertial sensors, used installed beacons in the region of interest, or required periodic surfacing of the AUV. While these methods are useful, their performance is fundamentally limited. Advances in underwater communications and the application of simultaneous localization and mapping (SLAM) technology to the underwater realm have yielded new possibilities in the field. This paper presents a review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods. In addition, we highlight areas of future research potential.

Typhoon at CommsNet 2013: experimental experience on AUV navigation and localization

The CommsNet 2013 experiment took place in September 2013 in the La Spezia Gulf, North Tyrrhenian Sea. Organized and scientifically led by the NATO S&T Org. Ctr. for Maritime Research and Experimentation (CMRE, formerly NURC), with the participation of several research institutions, the experiment included among its objectives the evaluation of on-board acoustic Ultra-Short Base Line (USBL) systems for navigation and localization of Autonomous Underwater Vehicles (AUVs). The ISME groups of the Universities of Florence and Pisa jointly participated to the experiment with one Typhoon class vehicle. This is a 300 m depth rated AUV with acoustic communication capabilities originally developed by the two groups for archaeological search. The CommsNet 2013 Typhoon, equipped with an acoustic modem/USBL head, navigated within the fixed nodes acoustic network deployed by CMRE. This allows the comparison between inertial navigation, acoustic self-localization and ground truth represented by GPS signals (when the vehicle was at the surface). The preliminary results of the experiment show that the acoustic USBL self-localization is effective, and it has the potential to improve the overall vehicle navigation capabilities.