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.     

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.     

Shared autonomy for low‐cost underwater vehicles

This field report presents an overview of the development and testing of a semi‐autonomous underwater vehicle (sAUV). The work presented here is aimed at bridging the gap between current remotely operated vehicles and autonomous research platforms by developing shared autonomy capabilities for low‐cost underwater vehicles. We use commercially available components and open‐source software interfaces to provide a wider range of capabilities for underwater autonomy research at a lower cost than previously available systems. We describe the overall structure of the system, discuss its capabilities, and provide results demonstrating system performance. We place particular emphasis on shared autonomy, where a human operator is assisted in controlling an underwater tethered vehicle. We present three capabilities developed for the sAUV: (a) an assisted control mode that provides a variable level of assistance using an on‐line estimate of user skill level, (b) a planner to generate paths that avoid tether entanglement, and (c) a sonar processing algorithm that identifies informative sonar images for selecting features for 3D scene reconstruction. The vehicle has been deployed on five off‐shore and near‐shore marine field deployments since 2015, and this report includes selected results from four of those trials to demonstrate the capabilities and limitations of the sAUV system.     

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.     

Toward Autonomous Exploration in Confined Underwater Environments

In this field note, we detail the operations and discuss the results of an experiment conducted in the unstructured environment of an underwater cave complex using an autonomous underwater vehicle (AUV). For this experiment, the AUV was equipped with two acoustic sonar sensors to simultaneously map the caves' horizontal and vertical surfaces. Although the caves' spatial complexity required AUV guidance by a diver, this field deployment successfully demonstrates a scan‐matching algorithm in a simultaneous localization and mapping framework that significantly reduces and bounds the localization error for fully autonomous navigation. These methods are generalizable for AUV exploration in confined underwater environments where surfacing or predeployment of localization equipment is not feasible, and they may provide a useful step toward AUV utilization as a response tool in confined underwater disaster areas.     

Scan matching SLAM in underwater environments

This paper proposes a pose-based algorithm to solve the full simultaneous localization and mapping problem for autonomous underwater vehicle (AUV) navigating in unknown and possibly unstructured environments. The proposed method first estimates the local path traveled by the robot while forming the acoustic image (scan) with range data coming from a mono-beam rotating sonar head, providing position estimates for correcting the distortions that the vehicle motion produces in the scans. Then, consecutive scans are cross-registered under a probabilistic scan matching technique for estimating the displacements of the vehicle including the uncertainty of the scan matching result. Finally, an augmented state extended Kalman filter estimates and keeps the registered scans poses. No prior structural information or initial pose are considered. The viability of the proposed approach has been tested reconstructing the trajectory of a guided AUV operating along a 600 m path within a marina environment.     

基于三维成像声纳技术的AUV前视避障方法

针对自主式水下航行器（ AUV）在水下自主航行过程中的避障问题,提出了一种基于三维成像声纳技术的前视避障方法。该方法使用三维成像声纳探测目标,通过声纳目标图像处理提取目标,完成目标的运动状态分析和轨迹预测。通过设置碰撞区域,建立避障模型和设计避障规则,实现了AUV的智能化避障。借助于其他传感器,这些关联模块构成了一个集目标探测、目标提取、轨迹预测、避障与路线回归等功能于一体的闭环系统。     

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.     

Experiments in feedback linearized iterative learning‐based path following for center‐articulated industrial vehicles

This paper describes the design, industrial application, and field testing of a technique for autonomous wheeled‐vehicle path following that uses iterative learning control (ILC) in a feedback linearized space. One advantage of this approach is that ILC is used without having to employ approximate linearization at every time step. The main contribution of this paper is the unique field experiments that used two large industrial‐scale center‐articulated underground mining vehicles. The described field work not only tested the underlying technique on commercial vehicles, but also presents a method for parallel speed learning, wherein the speed is adjusted over subsequent learning trials to improve cycle productivity. Finally, presented are field results for an approach to prelearning through simulation before deployment in the field to reduce the initial path‐following errors.     

Under canopy light detection and ranging‐based autonomous navigation

This paper describes a light detection and ranging (LiDAR)‐based autonomous navigation system for an ultralightweight ground robot in agricultural fields. The system is designed for reliable navigation under cluttered canopies using only a 2D Hokuyo UTM‐30LX LiDAR sensor as the single source for perception. Its purpose is to ensure that the robot can navigate through rows of crops without damaging the plants in narrow row‐based and high‐leaf‐cover semistructured crop plantations, such as corn (Zea mays) and sorghum ( Sorghum bicolor). The key contribution of our work is a LiDAR‐based navigation algorithm capable of rejecting outlying measurements in the point cloud due to plants in adjacent rows, low‐hanging leaf cover or weeds. The algorithm addresses this challenge using a set of heuristics that are designed to filter out outlying measurements in a computationally efficient manner, and linear least squares are applied to estimate within‐row distance using the filtered data. Moreover, a crucial step is the estimate validation, which is achieved through a heuristic that grades and validates the fitted row‐lines based on current and previous information. The proposed LiDAR‐based perception subsystem has been extensively tested in production/breeding corn and sorghum fields. In such variety of highly cluttered real field environments, the robot logged more than 6 km of autonomous run in straight rows. These results demonstrate highly promising advances to LiDAR‐based navigation in realistic field environments for small under‐canopy robots.     

Magnetic survey and autonomous target reacquisition with a scalar magnetometer on a small AUV

A scalar magnetometer payload has been developed and integrated into a two‐man portable autonomous underwater vehicle (AUV) for geophysical and archeological surveys. The compact system collects data from a Geometrics microfabricated atomic magnetometer, a total‐field atomic magnetometer. Data from the sensor is both stored for post‐processing and made available to an onboard autonomy engine for real‐time sense and react behaviors. This system has been characterized both in controlled laboratory conditions and at sea to determine its performance limits. Methodologies for processing the magnetometer data to correct for interference and error introduced by the AUV platform were developed to improve sensing performance. When conducting seabed surveys, detection and characterization of targets of interest are performed in real‐time aboard the AUV. This system is used to drive both single‐ and multiple‐vehicle autonomous target reacquisition behaviors. The combination of on‐board target detection and autonomous reacquire capability is found to increase the effective survey coverage rate of the AUV‐based magnetic sensing system.     

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.     

An open‐source system for vision‐based micro‐aerial vehicle mapping,planning, and flight in cluttered environments

We present an open‐source system for Micro‐Aerial Vehicle (MAV) autonomous navigation from vision‐based sensing. Our system focuses on dense mapping, safe local planning, and global trajectory generation, especially when using narrow field‐of‐view sensors in very cluttered environments. In addition, details about other necessary parts of the system and special considerations for applications in real‐world scenarios are presented. We focus our experiments on evaluating global planning, path smoothing, and local planning methods on real maps made on MAVs in realistic search‐and‐rescue and industrial inspection scenarios. We also perform thousands of simulations in cluttered synthetic environments, and finally validate the complete system in real‐world experiments.     

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.     

In‐water visual ship hull inspection using a hover‐capable underwater vehicle with stereo vision

Underwater visual inspection is an important task for checking the structural integrity and biofouling of the ship hull surface to improve the operational safety and efficiency of ships and floating vessels. This paper describes the development of an autonomous in‐water visual inspection system and its application to visual hull inspection of a full‐scale ship. The developed system includes a hardware vehicle platform and software algorithms for autonomous operation of the vehicle. The algorithms for vehicle autonomy consist of the guidance, navigation, and control algorithms for real‐time and onboard operation of the vehicle around the hull surface. The environmental perception of the developed system is mainly based on optical camera images, and various computer vision and optimization algorithms are used for vision‐based navigation and visual mapping. In particular, a stereo camera is installed on the underwater vehicle to estimate instantaneous surface normal vectors, which enables high‐precision navigation and robust visual mapping, not only on flat areas but also over moderately curved hull surface areas. The development process of the vehicle platform and the implemented algorithms are described. The results of the field experiment with a full‐scale ship in a real sea environment are presented to demonstrate the feasibility and practical performance of the developed system.     

Fault‐tolerant path‐following control for in‐wheel‐motor‐driven autonomous ground vehicles with differential steering

Over the past several decades, the automobile industry has denoted significant research efforts to developing in‐wheel‐motor‐driven autonomous ground vehicles (IWM‐AGVs) with active front‐wheel steering. One of the most fundamental issues for IWM‐AGVs is path following, which is important for automated driving to ensure that the vehicle can track a target‐planned path during local navigation. However, the path‐following task may fail if the system experiences a stuck fault in the active front‐wheel steering. In this paper, a fault‐tolerant control (FTC) strategy is presented for the path following of IWM‐AGVs in the presence of a stuck fault in the active front‐wheel steering. For this purpose, differential steering is used to generate differential torque between the left and right wheels in IWM‐AGVs, and an adaptive triple‐step control approach is applied to realize coordinated lateral and longitudinal path‐following maneuvering. The parameter uncertainties for the cornering stiffness and external disturbances are considered to make the vehicles robust to different driving environments. The effectiveness of the proposed scheme is evaluated with a high‐fidelity and full‐car model based on the veDYNA‐Simulink joint platform.     

基于多波束前视声呐的水下障碍物检测及避障算法

近年来,人们开始不断地开发海洋资源和空间,如在海底铺设大量的天然气管道以便于运输,因此,利用自主式水下航行器去探测海底天然气管道是否泄漏的技术,就具有重大的战略意义。基于自主式水下航行器搭载的多波束前视声呐采集的数据,进行声呐图像中的障碍物检测,提出了一种基于类间方差及小区域抑制的障碍物检测算法。然后,利用声呐图像的障碍物检测结果,设计了基于障碍物轮廓的避障算法,来估计合理的避障角度,传送给水下航行器的主控来控制航行器避开障碍物。     

TrackerBots: Autonomous unmanned aerial vehicle for real‐time localization and tracking of multiple radio‐tagged animals

Autonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods. We present a novel autonomous aerial vehicle system—TrackerBots—to track and localize multiple radio‐tagged animals. The simplicity of measuring the received signal strength indicator (RSSI) values of very high frequency (VHF) radio‐collars commonly used in the field is exploited to realize a low‐cost and lightweight tracking platform suitable for integration with unmanned aerial vehicles (UAVs). Due to uncertainty and the nonlinearity of the system based on RSSI measurements, our tracking and planning approaches integrate a particle filter for tracking and localizing and a partially observable Markov decision process for dynamic path planning. This approach allows autonomous navigation of a UAV in a direction of maximum information gain to locate multiple mobile animals and reduce exploration time and, consequently, conserve on‐board battery power. We also employ the concept of search termination criteria to maximize the number of located animals within power constraints of the aerial system. We validated our real‐time and online approach through both extensive simulations and field experiments with five VHF radio‐tags on a grassland plain.     

AUV-enabled adaptive underwater surveying for optimal data collection

A new adaptive strategy for performing data collection with a sonar-equipped autonomous underwater vehicle (AUV) is proposed. The approach is general in the sense that it is applicable to a wide range of underwater tasks that rely on subsequent processing of side-looking sonar imagery. By intelligently allocating resources and immediately reacting to the data collected in-mission, the proposed approach simultaneously maximizes the information content in the data and decreases overall survey time. These improvements are achieved by adapting the AUV route to prevent portions of the mission area from being either characterized by poor image quality or obscured by shadows caused by sand ripples. The peak correlation of consecutive sonar returns is used as a measure for image quality. To detect the presence of and estimate the orientation of sand ripples, a new innovative algorithm is developed. The components of the overall data-driven path-planning algorithm are purposely constructed to permit fast real-time execution with only minimal AUV onboard processing capabilities. Experimental results based on real sonar data collected at sea are used to demonstrate the promise of the proposed approach.     

自主式水下航行器三维路径跟踪的神经网络H∞鲁棒自适应控制方法

本文研究了存在模型不确定以及外界未知扰动情况下的自主式水下航行器(AUV)的三维路径跟踪控制问题. 针对此问题, 首先利用时标分离原理及正交投影Serret-Frenet坐标系建立了描述AUV质心运动及姿态运动的的仿射非线性数学模型. 其次, 在控制器设计中运用神经网络H∞鲁棒自适应算法克服了模型的不确定性及扰动, 同时在控制器设计中利用了主导输入的思想, 降低了闭环系统的复杂度, 减少了实时计算工作量, 便于工程应用. 基于Lyapunov理论的分析保证了系统的稳定性. 仿真结果表明, 路径跟踪控制律可以保证AUV沿期望路径运动, 并且具有良好的动态性能.