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.     

Distributed finite‐time fault‐tolerant error constraint containment algorithm for multiple ocean bottom flying nodes with tan‐type barrier Lyapunov function

The ocean bottom flying node (OBFN) is a special autonomous underwater vehicle (AUV) for seabed resource exploration. In this article, unmodeled uncertainties, thruster faults, and ocean disturbances are considered. The trajectory errors are constrained. Based on the directed topology, the distributed finite‐time fault‐tolerant error constraint containment control problem for multiple OBFN systems is solved, while only a part of follower OBFNs can measure the state of leaders. By using the backstepping method and a tan‐type barrier lyapunov function (BLF), a novel form of virtual controller is constructed. Neural network is employed to approximate and compensate the general disturbances. And the upper bound of the estimation error is dealt with by proposing an adaptive law. Besides, the trajectory errors can be constrained to a small neighborhood of zero in finite time. In other words, follower OBFNs can reach the convex hull consisted of leaders in finite time. The effectiveness of the designed algorithm is shown by presenting numerical experiment.     

Detection of unanticipated faults for autonomous underwater vehicles using online topic models

For robots to succeed in complex missions, they must be reliable in the face of subsystem failures and environmental challenges. In this paper, we focus on autonomous underwater vehicle (AUV) autonomy as it pertains to self‐perception and health monitoring, and we argue that automatic classification of state‐sensor data represents an important enabling capability. We apply an online Bayesian nonparametric topic modeling technique to AUV sensor data in order to automatically characterize its performance patterns, then demonstrate how in combination with operator‐supplied semantic labels these patterns can be used for fault detection and diagnosis by means of a nearest‐neighbor classifier. The method is evaluated using data collected by the Monterey Bay Aquarium Research Institute's Tethys long‐range AUV in three separate field deployments. Our results show that the proposed method is able to accurately identify and characterize patterns that correspond to various states of the AUV, and classify faults at a high rate of correct detection with a very low false detection rate.     

A time differences of arrival‐based homing strategy for autonomous underwater vehicles

A new sensor‐based homing integrated guidance and control law is presented to drive an underactuated autonomous underwater vehicle (AUV) toward a fixed target, in 3‐D, using the information provided by an ultra‐short baseline (USBL) positioning system. The guidance and control law is first derived at a kinematic level, expressed on the space of the time differences of arrival (TDOAs), as directly measured by the USBL sensor, and assuming the plane wave approximation. Afterwards, the control law is extended for the dynamics of an underactuated AUV resorting to backstepping techniques. The proposed Lyapunov‐based control law yields almost global asymptotic stability (AGAS) in the absence of external disturbances and is further extended, keeping the same properties, to the case where known ocean currents affect the motion of the vehicle. Simulations are presented and discussed that illustrate the performance and behavior of the overall closed‐loop system in the presence of realistic sensor measurements and actuator saturation. Copyright © 2009 John Wiley & Sons, Ltd.     

Multirepresentation,Multiheuristic A* search‐based motion planning for a free‐floating underwater vehicle‐manipulator system in unknown environment

A key challenge in autonomous mobile manipulation is the ability to determine, in real time, how to safely execute complex tasks when placed in unknown or changing world. Addressing this issue for Intervention Autonomous Underwater Vehicles (I‐AUVs), operating in potentially unstructured environment is becoming essential. Our research focuses on using motion planning to increase the I‐AUVs autonomy, and on addressing three major challenges: (a) producing consistent deterministic trajectories, (b) addressing the high dimensionality of the system and its impact on the real‐time response, and (c) coordinating the motion between the floating vehicle and the arm. The latter challenge is of high importance to achieve the accuracy required for manipulation, especially considering the floating nature of the AUV and the control challenges that come with it. In this study, for the first time, we demonstrate experimental results performing manipulation in unknown environment. The Multirepresentation, Multiheuristic A* (MR‐MHA*) search‐based planner, previously tested only in simulation and in a known a priori environment, is now extended to control Girona500 I‐AUV performing a Valve‐Turning intervention in a water tank. To this aim, the AUV was upgraded with an in‐house‐developed laser scanner to gather three‐dimensional (3D) point clouds for building, in real time, an occupancy grid map (octomap) of the environment. The MR‐MHA* motion planner used this octomap to plan, in real time, collision‐free trajectories. To achieve the accuracy required to complete the task, a vision‐based navigation method was employed. In addition, to reinforce the safety, accounting for the localization uncertainty, a cost function was introduced to keep minimum clearance in the planning. Moreover a visual‐servoing method had to be implemented to complete the last step of the manipulation with the desired accuracy. Lastly, we further analyzed the approach performance from both loose‐coupling and clearance perspectives. Our results show the success and efficiency of the approach to meet the desired behavior, as well as the ability to adapt to unknown environments.     

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.     

Simplex sliding mode control for autonomous six‐DOF vehicles with mono‐directional actuators: Robustness,stability, and implementation issues

The position and attitude control of a six‐degree‐of‐freedom vehicle is dealt with in this paper. The actuation system is assumed to consist of a set of mono‐directional devices suitably located and directed. The model of the system is characterized by a large amount of uncertainties, disturbances and measurement errors. The state is assumed fully available and the navigation problems generating smooth references for the state trajectories are supposed already solved. A new attitude guidance algorithm has been developed to enhance robustness with respect to a class of nonsmooth measurement errors. The use of the simplex‐based sliding mode methodology reveals to be simultaneously suitable for the design of the actuation system (position and orientation of the actuators) and the implementation of the control strategy. The chattering phenomenon is strongly attenuated by the introduction of integrators in the input channel and, consequently to this choice, a suitable mechanism to avoid the unbounded growth of the individual thrust of the actuators is designed, while at the same time, achieving a direct control of power losses. The performances of the proposed control scheme are demonstrated by simulation by using mathematical models available in the literature.     

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.     

Multi‐AUV motion planning for archeological site mapping and photogrammetric reconstruction

This paper presents coupled and decoupled multi‐autonomous underwater vehicle (AUV) motion planning approaches for maximizing information gain. The work is motivated by applications in which multiple AUVs are tasked with obtaining video footage for the photogrammetric reconstruction of underwater archeological sites. Each AUV is equipped with a video camera and side‐scan sonar. The side‐scan sonar is used to initially collect low‐resolution data to construct an information map of the site. Coupled and decoupled motion planning approaches with respect to this map are presented. Both planning methods seek to generate multi‐AUV trajectories that capture close‐up video footage of a site from a variety of different viewpoints, building on prior work in single‐AUV rapidly exploring random tree (RRT) motion planning. The coupled and decoupled planners are compared in simulation. In addition, the multiple AUV trajectories constructed by each planner were executed at archeological sites located off the coast of Malta, albeit by a single‐AUV due to limited resources. Specifically, each AUV trajectory for a plan was executed in sequence instead of simultaneously. Modifications are also made by both planners to a baseline RRT algorithm. The results of the paper present a number of trade‐offs between the two planning approaches and demonstrate a large improvement in map coverage efficiency and runtime.     

基于贝叶斯网络的水下AUV健康管理方法

随着自主式水下航行器(AUV)系统不断向自动化、智能化方向发展,系统结构和功能也越来越复杂。针对AUV健康管理系统中未能充分挖掘多源数据中隐含的有效信息的缺点,提出了基于朴素贝叶斯网络的AUV健康管理方法。首先,采用朴素贝叶斯网络与数据结合的方式构建AUV的健康管理系统;其次,计算该系统各部件和子系统的健康指标,并对整体健康情况进行评估;最终,输出AUV系统的健康状态和寿命指标结果,得到AUV系统的健康指标。利用Visual Studio和MATLAB作为仿真平台实现该方法并对结果进行验证,实验结果表明,该方法可以实现对不同健康程度的量化分析和表达,可确保AUV健康长久地运行。     

基于传感器信息的水下机器人动态避障研究

提出一种新的基于传感器信息的自治式水下机器人(AUV)动态避障方法。介绍了传感器的工作原理。通过栅格法把传感器采集的AUV运行环境障碍信息进行合理描述,并预测动态障碍物的速度,保证AUV能够根据传感器信息躲避障碍物,达到航行要求。最后,通过仿真实验对机器人自主避障能力进行了验证。     

基于信息增益率的WNB水下机器人故障分类

提高故障诊断能力对于确保水下机器人系统的稳定运行具有重要意义,故障分类是目前水下机器人故障诊断所面临的一个重要问题。针对水下机器人推进器系统数据特征,提出一种基于信息增益率的加权朴素贝叶斯故障分类算法。首先,计算故障训练样本的先验概率,将各属性的信息增益率作为权值;其次,构建基于增益率加权的朴素贝叶斯分类模型;然后,对检测的故障数据利用分类模型获取具有最大后验概率的故障模式,实现故障分类。与朴素贝叶斯算法和决策树算法相比,仿真实验结果表明基于信息增益率加权的朴素贝叶斯算法的分类成功率更高,能够有效地实现水下机器人的故障分类。     

Experimental analysis of low‐altitude terrain following for hover‐capable flight‐style autonomous underwater vehicles

Operating an autonomous underwater vehicle (AUV) in close proximity to terrain typically relies solely on the vehicle sensors for terrain detection, and challenges the manoeuvrability of energy efficient flight‐style AUVs. This paper gives new results on altitude tracking limits of such vehicles by using the fully understood environment of a lake to perform repeated experiments while varying the altitude demand, obstacle detection and actuator use of a hover‐capable flight‐style AUV. The results are analysed for mission success, vehicle risk and repeatability, demonstrating the terrain following capabilities of the overactuated AUV over a range of altitude tracking strategies and how these measures better inform vehicle operators. A major conclusion is that the effects of range limits, bias and false detections of the sensors used for altitude tracking must be fully accounted for to enable mission success. Furthermore it was found that switching between hover‐ and flight‐style actuations based on speed, whilst varying the operation speed, has advantages for performance improvement over combining hover‐ and flight‐style actuators at high speeds.     

基于灰色预测和KID离群点检测的AUV故障诊断

提高故障诊断能力对于确保水下机器人AUV系统的稳定运行具有重要意义。针对水下机器人推进器系统,提出一种基于离群点检测的AUV故障检测方法。首先,将传感器采集的数据进行灰色预测处理;然后,提出了一种结合K-mean和DBSCAN的改进迭代聚类(Iterative K-mean DBSCAN,IKD)算法进行离群点检测;最后,与K-mean和DBSCAN算法相比,仿真实验结果表明基于灰色预测和KID离群点检测算法的故障检测准确率高,能够有效地实现水下机器人AUV的无监督故障诊断。     

Pipeline following by visual servoing for Autonomous Underwater Vehicles

A nonlinear image-based visual servo control approach for pipeline following of fully-actuated Autonomous Underwater Vehicles (AUV) is proposed. It makes use of the binormalized Plücker coordinates of the pipeline borders detected in the image plane as feedback information while the system dynamics are exploited in a cascade manner in the control design. Unlike conventional solutions that consider only the system kinematics, the proposed control scheme accounts for the full system dynamics in order to obtain an enlarged provable stability domain. Control robustness with respect to model uncertainties and external disturbances is reinforced using integral corrections. Robustness and efficiency of the proposed approach are illustrated via both realistic simulations and experimental results on a real AUV.     

Coverage Path Planning with Real‐time Replanning and Surface Reconstruction for Inspection of Three‐dimensional Underwater Structures using Autonomous Underwater Vehicles

We present a novel method for planning coverage paths for inspecting complex structures on the ocean floor using an autonomous underwater vehicle (AUV). Our method initially uses a 2.5‐dimensional (2.5D) prior bathymetric map to plan a nominal coverage path that allows the AUV to pass its sensors over all points on the target area. The nominal path uses a standard mowing‐the‐lawn pattern in effectively planar regions, while in regions with substantial 3D relief it follows horizontal contours of the terrain at a given offset distance. We then go beyond previous approaches in the literature by considering the vehicle's state uncertainty rather than relying on the unrealistic assumption of an idealized path execution. Toward that end, we present a replanning algorithm based on a stochastic trajectory optimization that reshapes the nominal path to cope with the actual target structure perceived in situ. The replanning algorithm runs onboard the AUV in real time during the inspection mission, adapting the path according to the measurements provided by the vehicle's range‐sensing sonars. Furthermore, we propose a pipeline of state‐of‐the‐art surface reconstruction techniques we apply to the data acquired by the AUV to obtain 3D models of the inspected structures that show the benefits of our planning method for 3D mapping. We demonstrate the efficacy of our method in experiments at sea using the GIRONA 500 AUV, where we cover part of a breakwater structure in a harbor and an underwater boulder rising from 40 m up to 27 m depth.     

基于PeliCAN协议的AUV总线系统设计

设计了基于PeliCAN协议的AUV总线系统;文中介绍了系统总体设计,系统工作流程和基于报文滤波的数据通讯过程;重点介绍了在报文标志符中加入子设备地址的设计方法,在CAN总线原有的开放性基础上,使AUV系统的设备构成更加灵活自由,系统采用双冗余方法,备用总线使系统具有传输容错能力,监测记录系统作为主控计算机的备份,可代替发生故障的主机维持系统正常运转,提高了AUV的可靠性。     

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.     

稀疏水下传感网中AUV数据移动收集技术研究

由于水下传感器节点的水声通信距离有限、价格昂贵,水下传感器网络中一般采用稀疏方式部署,因此很难保证整体网络的连通性及数据采集效率。自主水下航行器AUV（Autonomous Underwater Vehicle）作为天然的移动数据采集平台,可以弥补固定Sink节点数据采集方式的缺陷。提出了一种基于移动AUV的水下传感网移动数据收集机制。以AUV覆盖区域内的传感器节点作为临时Sink节点,其他传感器节点以临时Sink节点为根节点,采用最小生成树MST（Minimum Spanning Tree）方法将传感数据传输到这些临时Sink节点,然后通过临时Sink节点将汇聚数据传输给AUV。随着AUV的自主移动轨迹,水下传感网的传感数据都能简单高效地被收集起来。仿真结果验证了该方法在保证网络能耗的前提下提高了数据采集效率。     

基于自适应阈值与扩张状态滑模观测器的AUV执行机构故障检测与估计

作为自主式水下机器人(AUV)的重要组成部分,执行机构的可靠性对系统的安全运行具有重要意义.本文以AUV六自由度模型为基础,提出了一种基于自适应阈值与扩张状态滑模观测器相结合的故障检测与估计机制.首先,本文将模型中除去控制输入的部分扩张成新的系统状态,得到估计值和实际值之间的残差;其次,针对执行机构的未知扰动,文章设计了一种改进的自适应阈值以监测残差的变化,进一步降低了误诊率与漏诊率;随后,文章在扩张状态的结构基础上设计滑模观测器,将观测器的增益求解转化为线性矩阵不等式(LMI)约束优化问题;最后,通过动态滑模面的设计实现了抖振的抑制并论证了该动态滑模面的收敛性,同时引入等效控制输出误差注入原理,实现了AUV执行机构的故障重构.仿真结果表明,本文所提方法对AUV执行机构的故障具有较好的检测灵敏度和估计精度.