Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles

This paper extends the progress of single beacon one‐way‐travel‐time (OWTT) range measurements for constraining XY position for autonomous underwater vehicles (AUV). Traditional navigation algorithms have used OWTT measurements to constrain an inertial navigation system aided by a Doppler Velocity Log (DVL). These methodologies limit AUV applications to where DVL bottom‐lock is available as well as the necessity for expensive strap‐down sensors, such as the DVL. Thus, deep water, mid‐water column research has mostly been left untouched, and vehicles that need expensive strap‐down sensors restrict the possibility of using multiple AUVs to explore a certain area. This work presents a solution for accurate navigation and localization using a vehicle's odometry determined by its dynamic model velocity and constrained by OWTT range measurements from a topside source beacon as well as other AUVs operating in proximity. We present a comparison of two navigation algorithms: an Extended Kalman Filter (EKF) and a Particle Filter(PF). Both of these algorithms also incorporate a water velocity bias estimator that further enhances the navigation accuracy and localization. Closed‐loop online field results on local waters as well as a real‐time implementation of two days field trials operating in Monterey Bay, California during the Keck Institute for Space Studies oceanographic research project prove the accuracy of this methodology with a root mean square error on the order of tens of meters compared to GPS position over a distance traveled of multiple kilometers.     

Cooperative localization of a team of AUVs by a tetrahedral configuration

This paper investigates the principles of a Cooperative Localization Algorithm for a team of at least three Autonomous Underwater Vehicles (AUVs) with respect to a surface support ship, without the use of Ultra-Short Baseline (USBL). It is assumed that each AUV is equipped with a low-cost Inertial Measurement Unit (IMU), a compass and a depth sensor, but only one of them has a high accuracy navigation sensor such as the Doppler Velocity Log (DVL). The surface boat locates itself by means of Global Positioning System (GPS). Range measurements provided by acoustic modems allow to avoid an unbounded error growth in the position estimate of each AUV. A geometric method, based on a tetrahedral configuration to obtain a deterministic fix for position, is proposed. This method allows to extend the advantages of the use of the DVL to the position estimate of other vehicles not equipped with DVL. The paper addresses also some of the problems related to the limitations of acoustic communication. The algorithm has been implemented and tested in simulations for a fleet of three AUVs and a surface support ship.     

基于移动长基线的多AUV 协同导航

基于扩展卡尔曼滤波（EKF）理论研究了多AUV 协同导航定位的移动长基线算法．移动长基线多AUV 协同导航结构中,主AUV 内部装备高精度导航设备,从AUV 内部装备低精度导航设备,外部均装备水声装置测量 相对位置关系,利用移动长基线算法融合内部和外部传感器信息,实时获取从AUV 的位置信息．建立了协同导航 系统数学模型,设计了EKF 协同导航算法,在各种测试情况下通过仿真验证了所推导的分析结果,对EKF 和几何 解方程算法的导航效果进行了比较．研究结果表明,以主AUV 作为移动的长基线节点时,通过EKF 算法可以显著 提高群体的导航定位精度．     

An Acoustic Network Navigation System

This work describes a system for acoustic‐based navigation that relies on the addition of localization services to underwater networks. The localization capability has been added on top of an existing network, without imposing constraints on its structure/operation. The approach is based on the inclusion of timing information within acoustic messages through which it is possible to know the time of an acoustic transmission in relation to its reception. Exploiting such information at the network application level makes it possible to create an interrogation scheme similar to that of a long baseline. The advantage is that the nodes/autonomous underwater vehicles (AUVs) themselves become the transponders of a network baseline, and hence there is no need for dedicated instrumentation. The paper reports at sea results obtained from the COLLAB–NGAS14 experimental campaign. During the sea trial, the approach was implemented within an operational network in different configurations to support the navigation of the two Centre for Maritime Research and Experimentation Ocean Explorer (CMRE OEX) vehicles. The obtained results demonstrate that it is possible to support AUV navigation without constraining the network design and with a minimum communication overhead. Alternative solutions (e.g., synchronized clocks or two‐way‐travel‐time interrogations) might provide higher precision or accuracy, but they come at the cost of impacting on the network design and/or on the interrogation strategies. Results are discussed, and the performance achieved at sea demonstrates the viability to use the system in real, large‐scale operations involving multiple AUVs. These results represent a step toward location‐aware underwater networks that are able to provide node localization as a service.     

A high-resolution AUV navigation framework with integrated communication and tracking for under-ice deployments

We developed an environmentally adaptive under-ice navigation framework that was deployed in the Arctic Beaufort Sea during the United States Navy Ice Exercise in March 2020 (ICEX20). This navigation framework contained two subsystems developed from the ground up: (1) an on-board hydrodynamic model-aided navigation (HydroMAN) engine, and (2) an environmentally and acoustically adaptive integrated communication and navigation network (ICNN) that provided acoustic navigation aiding to the former. The HydroMAN synthesized measurements from an inertial navigation system (INS), ice-tracking Doppler velocity log (DVL), ICNN and pressure sensor into its self-calibrating vehicle flight dynamic model to compute the navigation solution. The ICNN system, which consisted of four ice buoys outfitted with acoustic modems, trilaterated the vehicle position using the one-way-travel-times (OWTT) of acoustic datagrams transmitted by the autonomous underwater vehicle (AUV) and received by the ice buoy network. The ICNN digested salinity and temperature information to provide model-assisted real-time OWTT range conversion to deliver accurate acoustic navigation updates to the HydroMAN. To decouple the contributions from the HydroMAN and ICNN subsystems towards a stable navigation solution, this article evaluates them separately: (1) HydroMAN was compared against DVL bottom-track aided INS during pre-ICEX20 engineering trials where both systems provided similar accuracy; (2) ICNN was evaluated by conducting a static experiment in the Arctic where the ICNN navigation updates were compared against GPS with ICNN error within low tens of meters. The joint HydroMAN-ICNN framework was tested during ICEX20, which provided a nondiverging high-resolution navigation solution—with the majority of error below 15 m—that facilitated a successful AUV recovery through a small ice hole after an 11 km untethered run in the upper and mid-water column.     

Inspection of an underwater structure using point‐cloud SLAM with an AUV and a laser scanner

This paper presents experimental results using a newly developed 3D underwater laser scanner mounted on an autonomous underwater vehicle (AUV) for real‐time simultaneous localization and mapping (SLAM). The algorithm consists of registering point clouds using a dual step procedure. First, a feature‐based coarse alignment is performed, which is then refined using iterative closest point. The robot position is estimated using an extended Kalman filter (EKF) that fuses the data coming from navigation sensors of the AUV. Moreover, the pose from where each point cloud was collected is also stored in the pose‐based EKF‐SLAM state vector. The results of the registration algorithm are used as constraint observations among the different poses within the state vector, solving the full‐SLAM problem. The method is demonstrated using the Girona 500 AUV, equipped with a laser scanner and inspecting a 3D sub‐sea infrastructure inside a water tank. Our results prove that it is possible to limit the navigation drift and deliver a consistent high‐accuracy 3D map of the inspected object.     

Tracking and Following a Tagged Leopard Shark with an Autonomous Underwater Vehicle

This paper presents a prototype system that enables an autonomous underwater vehicle (AUV) to autonomously track and follow a shark that has been tagged with an acoustic transmitter. The AUV's onboard processor handles both real‐time estimation of the shark's two‐dimensional planar position, velocity, and orientation states, as well as a straightforward control scheme to drive the AUV toward the shark. The AUV is equipped with a stereo‐hydrophone and receiver system that detects acoustic signals transmitted by the acoustic tag. The particular hydrophone system used here provides a measurement of relative bearing angle to the tag, but it does not provide the sign (+ or ?) of the bearing angle. Estimation is accomplished using a particle filter that fuses bearing measurements over time to produce a state estimate of the tag location. The particle filter combined with a heuristic‐based controller allows the system to overcome the ambiguity in the sign of the bearing angle. The state estimator and control scheme were validated by tracking both a stationary tag and a moving tag with known positions. Offline analysis of these data showed that state estimation can be improved by optimizing diffusion parameters in the prediction step of the filter, and considering signal strength of the acoustic signals in the resampling stage of the filter. These experiments revealed that state estimate errors were on the order of those obtained by current long‐distance shark‐tracking methods, i.e., manually driven boat‐based tracking systems. Final experiments took place in SeaPlane Lagoon, Los Angeles, where a 1‐m leopard shark (Triakis semifasciata) was caught, tagged, and released before being autonomously tracked and followed by the proposed AUV system for several hours. © 2013 Wiley Periodicals, Inc.     

A Hybrid Deep Sea Navigation System of LBL/DR Integration Based on UKF and PSO-SVM

In order to improve the navigation accuracy of human occupied vehicle(HOV)precisely and efficiently,an innovative hybrid approach based on unscented Kalman filter(UKF)and support vector machine(SVM)is proposed to fuse integrated navigation data.HOV is generally equipped with long baseline(LBL)acoustic positioning system and dead reckoning(DR)as an integrated navigation system.UKF is adopted to estimate the state of the dynamic model because of its good performance in filtering nonlinear problems.An accurate and stable filtering result can be obtained when both LBL and DR are online.At the same time,SVM is utilized to train DR information with the result when LBL outrages,and the particle swarm optimization(PSO)algorithm is employed for SVM parameters optimization.Therefore,the integrated navigation system can maintain a good performance when the LBL is off-line.Simulation results with the real navigation data of Jiaolong HOV show that the methodology proposed here is able to meet the needs of HOV application.     

洋流影响下基于运动矢径的AUV协同定位方法

针对水下自主航行器（AUV）协同定位受水下未知定常洋流影响的问题,给出一种洋流影响下基于运动矢径的AUV协同定位方法.利用AUV的运动学方程和基于运动矢径的量测方程,建立AUV的导航模型;通过扩展的卡尔曼滤波,设计了协同定位滤波算法;利用该算法对洋流速度进行估计,以补偿AUV定位误差.仿真结果表明,该算法能有效估计未知定常洋流速度的大小,并对AUV定位误差进行实时补偿,显著提高了AUV的定位精度.     

基于粒子滤波的AUV组合导航方法

讨论了粒子滤波器和RB(Rao-Blackwellised)粒子滤波器两种滤波方法在组合导航中的应用,给出了组合导航算法用于自治水下航行器(AUV)的具体数学模型,并且与拓展卡尔曼滤波器的导航结果进行比较.利用AUV湖上试验验证了3种算法的导航性能,试验结果表明RBPF组合导航算法能够获得最好的导航精度;然而通过对算法进行分析,发现其计算复杂度高于其余两种滤波算法.     

Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system

This paper describes the implementation of an intelligent navigation system, based on the integrated use of the global positioning system (GPS) and several inertial navigation system (INS) sensors, for autonomous underwater vehicle (AUV) applications. A simple Kalman filter (SKF) and an extended Kalman filter (EKF) are proposed to be used subsequently to fuse the data from the INS sensors and to integrate them with the GPS data. The paper highlights the use of fuzzy logic techniques to the adaptation of the initial statistical assumption of both the SKF and EKF caused by possible changes in sensor noise characteristics. This adaptive mechanism is considered to be necessary as the SKF and EKF can only maintain their stability and performance when the algorithms contain the true sensor noise characteristics. In addition, fault detection and signal recovery algorithms during the fusion process to enhance the reliability of the navigation systems are also discussed herein. The proposed algorithms are implemented to real experimental data obtained from a series of AUV trials conducted by running the low-cost Hammerhead AUV, developed by the University of Plymouth and Cranfield University.     

Improving the coordination efficiency of limited‐communication multi–autonomus underwater vehicle operations using a multiagent architecture

This research addresses the problem of coordinating multiple autonomous underwater vehicle (AUV) operations. An intelligent mission executive has been created that uses multiagent technology to control and coordinate multiple AUVs in communication‐deficient environments. By incorporating real‐time vehicle prediction, blackboard‐based hierarchical mission plans, mission optimization, and a distributed multiagent–based paradigm in conjunction with a simple broadcast communication system, this research aims to handle the limitations inherent in underwater operations, namely poor communication, and intelligently control multiple vehicles. In this research, efficiency is evaluated and then compared to the current state of the art in multiple AUV control. The research is then validated in real AUV coordination trials. Results will show that compared to the state of the art, the control system developed and implemented in this research coordinates multiple vehicles more efficiently and is able to function in a range of poor communication environments. These findings are supported by in‐water validation trials with heterogeneous AUVs. © 2010 Wiley Periodicals, Inc.     

Teach‐and‐repeat path following for an autonomous underwater vehicle

This paper presents a teach‐and‐repeat path‐following method for an autonomous underwater vehicle (AUV) navigating long distances in environments where external navigation aides are denied. This method utilizes sonar images to construct a series of reference views along a path, stored as a topological map. The AUV can then renavigate along this path, either to return to the start location or to repeat the route. Utilizing unique assumptions about the sonar image‐generation process, this system exhibits robust image‐matching capabilities, providing observations to a discrete Bayesian filter that maintains an estimate of progress along the path. Image‐matching also provides an estimate of offset from the path, allowing the AUV to correct its heading and effectively close the gap. Over a series of field trials, this system demonstrated online control of an AUV in the ocean environment of Holyrood Arm, Newfoundland and Labrador, Canada. The system was implemented on an International Submarine Engineering Ltd. Explorer AUV and performed multiple path completions over both a 1 and 5 km track. These trials illustrated an AUV operating in a fully autonomous mode, in which navigation was driven solely by sensor feedback and adaptive control. Path‐following performance was as desired, with the AUV maintaining close offset to the path.     

Terrain‐aided navigation for long‐endurance and deep‐rated autonomous underwater vehicles

Terrain‐aided navigation (TAN) is a localisation method which uses bathymetric measurements for bounding the growth in inertial navigation error. The minimisation of navigation errors is of particular importance for long‐endurance autonomous underwater vehicles (AUVs). This type of AUV requires simple and effective on‐board navigation solutions to undertake long‐range missions, operating for months rather than hours or days, without reliance on external support systems. Consequently, a suitable navigation solution has to fulfil two main requirements: (a) bounding the navigation error, and (b) conforming to energy constraints and conserving on‐board power. This study proposes a low‐complexity particle filter‐based TAN algorithm for Autosub Long Range, a long‐endurance deep‐rated AUV. This is a light and tractable filter that can be implemented on‐board in real time. The potential of the algorithm is investigated by evaluating its performance using field data from three deep (up to 3,700 m) and long‐range (up to 195 km in 77 hr) missions performed in the Southern Ocean during April 2017. The results obtained using TAN are compared to on‐board estimates, computed via dead reckoning, and ultrashort baseline (USBL) measurements, treated as baseline locations, sporadically recorded by a support ship. Results obtained through postprocessing demonstrate that TAN has the potential to prolong underwater missions to a range of hundreds of kilometres without the need for intermittent surfacing to obtain global positioning system fixes. During each of the missions, the system performed 20 Monte Carlo runs. Throughout each run, the algorithm maintained convergence and bounded error, with high estimation repeatability achieved between all runs, despite the limited suite of localisation sensors.     

Hierarchical inversion‐based output tracking control for uncertain autonomous underwater vehicles using extended Kalman filter

In this study, a hierarchical inversion‐based output tracking controller (HIOTC) is developed for an autonomous underwater vehicle (AUV) subject to random uncertainties (e.g., current disturbances, unmodeled dynamics, and parameter variations) and noises (e.g., process and measurement noises). The proposed HIOTC respectively utilizes a combination of feedforward and feedback controls in a hierarchical structure based on the kinematic and dynamic models of the system. Moreover, to obtain uncontaminated or unavailable states for implementing the proposed control law, the extended Kalman filter (EKF) is employed to estimate the system states. Then, the position outputs, orientation, and velocity of the AUV are reached with guaranteed asymptotic stability. The robustness of the proposed HIOTC is verified through injection of random uncertainties into the system model. The closed‐loop stability of the proposed individual subsystems is respectively guaranteed to have uniformly ultimately bounded (UUB) performance based on the Lyapunov stability criteria. In addition, the asymptotic tracking of the overall system is demonstrated using Barbalat's lemma. Finally, the feasibility and effectiveness of the proposed control scheme are evaluated through computer simulations and it is shown that the overall system achieves good asymptotic tracking performance.     

Estimating Vehicle State by GPS/IMU Fusion with Vehicle Dynamics

In this paper, a low-cost navigation system with high integrity and reliability is proposed. A high-integrity estimation filter is proposed to obtain a high-accuracy state estimate. The filter utilizes a vehicle velocity constraint measurement to enhance the accuracy of the estimate. Two estimation filters, the extended Kalman filter (EKF) and the extended information filter (EIF), are designed and compared to obtain the estimate of the vehicle state. An instrumentation system that consists of a microcontroller, GPS receiver, IMU, velocity encoder, and Zigbee transceiver is used. The microcontroller provides a vehicle navigation solution at 50 Hz by fusing the measurements of the IMU and GPS receiver using the proposed filter design. Extensive experimental tests are conducted to verify the accuracy of the proposed algorithm. These results are processed with and without the velocity constraints. The estimation accuracy improvement with the addition of the velocity constraints is shown. A more than 16 % reduction in the computational time is demonstrated when using the EIF in comparison to the EKF approach.     

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).     

A Multi‐Autonomous Underwater Vehicle System for Autonomous Tracking of Marine Life

This paper presents a multi‐autonomous underwater vehicle system capable of cooperatively and autonomously tracking and following marine targets (i.e., fish) tagged with an acoustic transmitter. The AUVs have been equipped with stereo‐hydrophones that receive signals broadcasted by the acoustic transmitter tags to enable real‐time calculation of bearing‐to‐tag and distance‐to‐tag measurements. These measurements are shared between AUVs via acoustic modem and fused within each AUV's particle filter for estimating the target's position. The AUVs use a leader/follower multi‐AUV control system to enable the AUVs to drive toward the estimated target state by following collision‐free paths. Once within the local area of the target, the AUVs circumnavigate the target state until it moves to another area. The system builds on previous work by incorporating a new SmartTag package that can be attached to an individual's dorsal fin. The SmartTag houses a full inertial measurement unit (INU), video logger, acoustic transmitter, and timed release mechanism. After real‐time AUV tracking experiments, the SmartTag is recovered. Logged IMU data are fused with logged AUV‐obtained acoustic tag measurements within a particle filter to improve state estimation accuracy. This improvement is validated through a series of multi‐AUV shark and boat tracking experiments conducted at Santa Catalina Island, California. When compared with previous work that did not use the SmartTag package, results demonstrated a decrease in mean position estimation error of 25–75%, tag orientation estimation errors dropped from 80° to 30° , the sensitivity of mean position error with respect to distance to the tag was less by a factor of 50, and the sensitivity of mean position error with respect to acoustic signal reception frequency to the tag was 25 times less. These statistics demonstrate a large improvement in the system's robustness when the SmartTag package is used.     

A novel anomaly detection approach to identify intentional AIS on-off switching

The Automatic Identification System (AIS) is a ship reporting system based on messages broadcast by vessels carrying an AIS transponder. The recent increase of terrestrial networks and satellite constellations of receivers is making AIS one of the main sources of information for Maritime Situational Awareness activities. Nevertheless, AIS is subject to reliability and manipulation issues; indeed, the received reports can be unintentionally incorrect, jammed or deliberately spoofed. Moreover, the system can be switched off to cover illicit operations, causing the interruption of AIS reception. This paper addresses the problem of detecting whether a shortage of AIS messages represents an alerting situation or not, by exploiting the Received Signal Strength Indicator available at the AIS Base Stations (BS). In designing such an anomaly detector, the electromagnetic propagation conditions that characterize the channel between ship AIS transponders and BS have to be taken into consideration. The first part of this work is thus focused on the experimental investigation and characterisation of coverage patterns extracted from the real historical AIS data. In addition, the paper proposes an anomaly detection algorithm to identify intentional AIS on-off switching. The presented methodology is then illustrated and assessed on a real-world dataset.     

多AUV协同导航问题的研究现状与进展

自主水下航行器(Autonomous underwater vehicle, AUV)协同导航是未来50年解决水下中间层区域AUV导航定位的重要方法. 本文针对多AUV协同导航, 对该领域相关问题的研究进展进行了综述, 包括: 1)论述多AUV协同导航领域的研究现状, 包括协同导航问题界定、特点综述与讨论; 2)分析多AUV协同导航系统模型及相关算法的研究进展, 包括基于优化的、 基于参数估计的和基于贝叶斯估计的滤波算法; 3)对协同导航网络下的误差建模与补偿方法的研究进展进行了综述, 包括未知洋流的影响、水声通信延迟补偿等; 4)从影响协同导航定位精度的角度出发, 对AUV协同导航的可观测性与编队最优构型设计的研究进展进行了一系列的分析; 5)陈述目前多AUV协同导航中存在的关键问题, 并讨论其发展趋势.