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.     

一种考虑洋流影响的AUV组合导航算法

针对由捷联惯导(SINS)、多普勒测速仪(DVL)以及深度传感器组成的自主水下航行器(AUV)组合导航系统,当DVL测量距离无法达到海底的情况下,洋流是该系统主要误差源之一的问题,在SINS/DVL组合导航算法的基础上,提出了一种在原算法中加入洋流信息提高系统导航定位精度的方法,并将以上两种导航算法解算出的AUV位置信息进行仿真对比,仿真结果表明:与未考虑洋流信息的算法相比,加入洋流信息的算法能够有效提高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.     

几种地形辅助导航方法的比较

地形辅助导航系统将飞行航迹下的地形高度信息与机载数字高程地图比较后得到导航定位结果，并以此对惯性导航主系统进行必要的修正。针对基于扩展Kalman滤波，基于关联算子和基于点群滤波的3种地形辅助导航方法，介绍其基本原理后。采用真实地形数据进行仿真，比较不同飞行情况下这些方法的性能。     

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.     

SINS/DVL水下组合导航技术发展综述

随着对海洋的探索和开发不断深入, 基于捷联惯性导航系统和多普勒计程仪相结合的水下组合导航技术, 近年来在水下无人航行器导航定位得到了广泛应用. 本文简要概述了捷联惯性导航系统/多普勒计程仪(SINS/DVL) 组合导航系统的基本架构, 列举了几种被广泛中应用于组合导航系统的信息融合技术. 通过对组合导航技术梳理分 析, 总结出近期研究的3个热点问题, 包括初始对准技术、标定技术、鲁棒性技术, 以技术的更新和优化为依托, 详细 阐述了3项技术的发展历程. 在总结归纳现有技术和研究成果的基础上, 展望并分析SINS/DVL组合系统将来的研究 方向及其面临的挑战. 本文可为高精度水下导航技术研究提供有益参考.     

微小型AUV模型辅助技术在惯性导航中的应用

针对微小型水下潜器在无法获得GPS信号的情况,设计了一种模型辅助的全自主惯性导航系统;由于体积和功耗的限制,本系统无法采用传统的多普勒测速仪作为外部传感器,而提出了以惯性导航系统为核心,运动模型辅助的方法;根据水下潜器的运动特性进行数学建模,与卡尔曼滤波构成回路,运动模型输出位置、速度等信息对捷联解算输出信息进行误差补偿;搭建了基于Simulink的实验仿真环境,验证了该方法的有效性,能够满足微小型AUV对于导航系统体积、成本和功耗的要求,一小时的定位误差小于500m。     

Mid-water current aided localization for autonomous underwater vehicles

Survey-class autonomous underwater vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the mid-water column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.     

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.     

水下自主导航系统算法设计

基于微小型水下机器人的导航需求,结合水下机器人应用环境的特点,在既定的元器件与处理芯片基础上,提出了水下组合导航算法的设计原则,选取合适的状态量和量测量,建立状态方程与量测方程,建立一套集中式Kalman滤波导航系统算法。算法保证导航系统能够满足长航时下高精度需求,具有较高的抗干扰能力,同时充分利用现有的各项传感器输出信息,保证系统的稳定性。并用实验小车进行陆上实验,验证算法的正确性。     

Single range observability for cooperative underactuated underwater vehicles

The paper addresses the single range observability analysis of a kinematics model of cooperating underactuated underwater vehicles. Teams of underwater vehicles that communicate with each other may be able to access and exchange their relative distances through, by example, acoustic signal time-of-flight measurements. Such relative distance measurements together with vehicle’s attitude and velocity information may be used onboard to implement a navigation filter to estimate the vehicle’s relative positions and orientations. A pre-requisite for successfully designing such navigation filters is to assess the systems observability properties. Contrary to the majority of existing studies on single range observability, the paper considers a more realistic underactuated kinematics model for slender body autonomous underwater vehicles rather than a simple point mass model. The paper extends previous results building on an augmented state technique allowing to reformulate the nonlinear observability problem in terms of a linear time varying one. As a result, all possible (globally) unobservable motions are characterized in terms of the systems’ initial conditions and velocity commands within the class of interest. The fundamental results reported are also illustrated by numerical simulations providing evidence of different motions generating the same output, namely lacking observability.     

基于PC104的超高速水下航行器测控程序设计与实现

超高速水下航行器自主航行试验需要雷载计算机提供时序控制、姿态自动控制等支持,在恶劣水下环境中测控系统的可靠性至关重要,它影响着实航数据的获取和航行控制研究的开展.以PC104嵌入式计算机为硬件平台,标准C语言为开发工具,针对超高速水下航行器航行控制和数据测量需求开发了测控程序.通过电平I/O进行时序及航行姿态控制,串口...     

Efficient View-Based SLAM Using Visual Loop Closures

This paper presents a simultaneous localization and mapping algorithm suitable for large-scale visual navigation. The estimation process is based on the viewpoint augmented navigation (VAN) framework using an extended information filter. Cholesky factorization modifications are used to maintain a factor of the VAN information matrix, enabling efficient recovery of state estimates and covariances. The algorithm is demonstrated using data acquired by an autonomous underwater vehicle performing a visual survey of sponge beds. Loop-closure observations produced by a stereo vision system are used to correct the estimated vehicle trajectory produced by dead reckoning sensors.      

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.     

Robot navigation in an unexplored terrain

Navigation planning is one of the most vital aspects of an autonomous mobile robot. Robot navigation for completely known terrain has been solved in many cases. Comparatively less research dealing with robot navigation in unexplored obstacle terrain has been reported in the literature. In recent times this problem has been addressed by adding learning capability to a robot. The robot explores terrain using sensors as it navigates, and builds a terrain model in an incremental manner. In this article we present concurrent algorithms for robot navigation in unexplored terrain. The performance of the concurrent algorithms is analyzed in terms of planning time, travel time, scanning time, and update time. The analysis reveals the need for an efficient data structure to store an obstacle terrain model in order to reduce traversal time, and also to incorporate learning. A modified adjacency list is proposed as a data structure for storing a spatial graph that represents an obstacle terrain. The time complexities of the algorithms that access, maintain, and update the spatial graph are estimated, and the effectiveness of the implementation is illustrated.     

Visual SLAM for underwater vehicles: A survey

Underwater scene is highly unstructured, full of various noise interferences. Moreover, GPS information is not available in the underwater environment, which thus brings huge challenges to the navigation of autonomous underwater vehicle. As an autonomous navigation technology, Simultaneous Localization and Mapping (SLAM) can deliver reliable localization to vehicles in unknown environment and generate models about their surrounding environment. With the development and utilization of marine and other underwater resources, underwater SLAM has become a hot research topic. By focusing on underwater visual SLAM, this paper reviews the basic theories and research progress regarding underwater visual SLAM modules, such as sensors, visual odometry, state optimization and loop closure detection, discusses the challenges faced by underwater visual SLAM, and shares the prospects of underwater visual SLAM. It is found that the traditional underwater visual SLAM based on filtering methods is gradually developing towards optimization-based methods. Underwater visual SLAM presents a diversified trend, and various new methods have emerged. This paper aims to provide researchers and practitioners with a better understanding of the current status and development trend of underwater visual SLAM, while offering help for collecting underwater vehicles intelligence.     

Mobility characterization for autonomous mobile robots using machine learning

This paper presents a supervised learning approach to improving the autonomous mobility of wheeled robots through sensing the robot’s interaction with terrain ‘underfoot.’ Mobility characterization is cast as a hierarchical task, in which pre-immobilization detection is achieved using support vector machines in time to prevent full immobilization, and if a pre-immobilization condition is detected, the associated terrain feature affecting mobility is identified using a Hidden Markov model. These methods are implemented using a hierarchical, layered control scheme developed for the Yeti robot, a 73-kg, four-wheeled robot designed to perform autonomous medium-range missions in polar terrain. The methodology is motivated by the difficultly of visually recognizing terrain features that impact mobility in low contrast terrain. The efficacy of the approach is evaluated using data from a suite of proprioceptive sensors. Real-time implementation shows that Yeti can consistently detect pre-immobilization conditions, stop in time to avoid unrecoverable immobilization, identify the terrain feature presenting the mobility challenge, and execute an escape sequence to retreat from the condition.     

一种基于学习向量量化神经网络的图象分割方法

基于视觉传感器实现道路信息的理解是目前移动机器人自主导航的重要研究方向,其中道路图象的正确分割是提取有效路径信息的关键。该文针对复杂、干扰因素多的室外环境下传统方法难以实现道路图象正确分割的问题,提出了一种基于LV Q神经网络的道路图象分割方法。该方法通过选取道路图象的归一化色彩分量为特征向量,应用基于LV Q学习算法的神经网络分类器进行道路与非道路识别;为解决环境噪声对神经网络输出的影响,本文设计了串行级联式四阶形态滤波器实现对神经网络输出的分割图象的滤波处理。通过对实测图象进行分割处理验证了该方法的有效性和鲁棒性,可用于室外环境下机器人的实时视觉导航控制。     

基于粒子滤波的无人机自主轨迹视觉导航控制方法研究

针对现有无人机导航控制方法存在的控制效果不佳的问题,本文提出一种基于粒子滤波的无人机自主轨迹视觉导航控制方法研究。利用粒子滤波算法,实现对无人机自主轨迹视觉导航控制方法的优化设计。采用栅格法构建无人机飞行环境地图,根据无人机的机械组成结构和工作原理,构建运动状态模型。利用内置的摄像机设备采集视觉图像,执行图像灰度转换、几何校正、滤波等预处理步骤。通过对视觉图像的特征提取,判断当前环境是否存在障碍物。利用粒子滤波算法确定无人机位姿,结合障碍物识别结果规划无人机的自主飞行轨迹。将位置、速度和姿态角的控制量计算结果,输入到安装的导航控制器中,完成无人机的自主轨迹视觉导航控制任务。通过实测分析得出结论：应用设计的导航控制方法,其位置误差、速度误差以及姿态角误差均维持在预设值以下,即设计的导航控制方法具有良好的控制效果。     

基于粒子滤波的重力梯度与地形信息融合辅助导航方法

基于粒子滤波（PF）的重力梯度与地形信息融合辅助导航方法充分利用了重力梯度特征与地形特征融合的优点,可提高舰艇导航系统信息的利用程度。仿真比较了基于重力梯度的扩展卡尔曼滤波、基于重力梯度的粒子滤波和重力梯度与地形多特征融合粒子滤波算法得到的位置均方根误差,分析了基于EKF的重力梯度匹配辅助导航系统的稳定性和状态能观性。仿真结果表明,提出的融合算法既能加快粒子滤波的收敛速度,又能提高粒子滤波算法的估计精度。