Scalable underwater assembly with reconfigurable visual fiducials

We present a scalable combined localization infrastructure deployment and task planning algorithm for underwater assembly. Infrastructure is autonomously modified to suit the needs of manipulation tasks based on an uncertainty model on the infrastructure's positional accuracy. Our uncertainty model can be combined with the noise characteristics from multiple devices. For the task planning problem, we propose a layer-based clustering approach that completes the manipulation tasks one cluster at a time. We employ movable visual fiducial markers as infrastructure and an autonomous underwater vehicle (AUV) for manipulation tasks. The proposed task planning algorithm is computationally simple, and we implement it on AUV without any offline computation requirements. Combined hardware experiments and simulations over large datasets show that the proposed technique is scalable to large areas.Comment: Submitted to ICRA 202

Underwater Exploration and Mapping

This paper analyzes the open challenges of exploring and mapping in the underwater realm with the goal of identifying research opportunities that will enable an Autonomous Underwater Vehicle (AUV) to robustly explore different environments. A taxonomy of environments based on their 3D structure is presented together with an analysis on how that influences the camera placement. The difference between exploration and coverage is presented and how they dictate different motion strategies. Loop closure, while critical for the accuracy of the resulting map, proves to be particularly challenging due to the limited field of view and the sensitivity to viewing direction. Experimental results of enforcing loop closures in underwater caves demonstrate a novel navigation strategy. Dense 3D mapping, both online and offline, as well as other sensor configurations are discussed following the presented taxonomy. Experimental results from field trials illustrate the above analysis.acceptedVersio

Navigation of an underwater robotic vehicle in a structured environment based on monocular vision

Durante as últimas décadas, o desenvolvimento de veículos subaquáticos não tripulados (na sigla em inglês – UUV) permitiu a execução de atividades subaquáticas onde a presença humana não era possível. Para auxiliar a operação destes veículos e dotá-los de uma maior autonomia, existe a necessidade de determinar a sua localização no espaço 3-D. Entre as diferentes técnicas existentes para a localização subaquática, as técnicas baseadas em visão computacional são bastante atrativas pois as câmaras fazem parte do equipamento padrão de um veículo robótico subaquático, permitindo obter soluções de localizações de baixo custo. Nesta dissertação, é abordado o tema da localização subaquática com recurso a uma única câmara, a odometria visual monocular, e com recurso a uma câmara auxiliada por uma Unidade de Medição Inercial (na sigla em inglês - IMU), a odometria visual-inercial monocular. A IMU é um sensor composto por um acelerómetro, um giroscópio e um magnetómetro, os quais se encontram no veículo utilizado nesta dissertação, o Pro4 ROV da VideoRay. São explorados diferentes métodos de odometria visual-inercial e odometria visual, e a sua aplicação ao meio subaquático. Devido às condições difíceis do meio, são exploradas duas formas de melhorar a visibilidade das imagens adquiridas com o objetivo de melhorar o desempenho dos algoritmos avaliados. Como a aquisição de dados no ambiente subaquático não é trivial, não existe muita informação sobre o desempenho dos métodos utilizados no meio em estudo. Deste modo, antes de os aplicar no Pro4 ROV, juntamente com as melhorias propostas, os algoritmos foram aplicados sobre um dataset subaquático público. Devido a fatores que condicionaram a utilização do Pro4 num ambiente real e a dificuldades técnicas na leitura dos dados da IMU, foram definidos um conjunto de testes em ambiente terrestre utilizando apenas os métodos de odometria visual, com os objetivos de validar o processo de calibração efetuado e demonstrar a aplicação dos algoritmos no veículo utilizado. Os resultados obtidos com o dataset demonstram que a utilização de uma câmara e de uma IMU no meio subaquático permite obter uma solução de localização de baixo custo com uma precisão submétrica. Em particular, a visibilidade da imagem revela ser um fator determinante para o aumento dessa precisão. Relativamente ao resultados obtidos com o ROV, estes destacam a importância do aproveitamento do largo campo de visão da câmara para o desempenho da odometria visual.During the past few decades, the development of Unmanned Underwater Vehicles (UUV) has allowed underwater activities to be carried out where human presence was not possible. To assist the operation of these vehicles and provide them with greater autonomy, there is a need to determine their position in the 3-D space. Among the different existing techniques for underwater localization, techniques based on computer vision are quite attractive because cameras are part of the standard equipment of an underwater robotic vehicle, allowing to obtain low cost localization solutions. In this dissertation, the theme of underwater localization is addressed using a single camera, called monocular visual odometry, and using a camera aided by an Inertial Measurement Unit (IMU), called monocular visualinertial odometry. The IMU is a sensor composed of an accelerometer, a gyroscope and a magnetometer, which are part of the vehicle used in this dissertation, the VideoRay Pro4 ROV. Different methods of visual-inertial odometry and visual odometry are explored, including their application to the underwater environment. Due to the difficult conditions of the environment, two ways of improving the visibility of the acquired images are explored in order to improve the performance of the evaluated algorithms. As the acquisition of data in the underwater environment is not trivial, there is not much information on the performance of the methods used in the study environment. Thus, before applying them to the Pro4 ROV, as well as the proposed improvements, the algorithms were applied over a public underwater dataset. Due to factors that conditioned the use of Pro4 in a real environment and technical difficulties in reading the IMU data, a set of tests in terrestrial environment were defined using only the methods of visual odometry, with the purpose of validating the calibration process performed and demonstrate the application of the algorithms in the vehicle used. The results obtained with the dataset demonstrate that the use of a camera and an IMU in the underwater environment allows to obtain a low-cost localization solution with submetric precision. In particular, the visibility of the image proves to be a determinant factor for increasing this accuracy. Regarding the results obtained with the ROV, these highlight the importance of taking advantage of the wide field of view of the camera.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Uncrewed Aircraft Systems for Autonomous Infrastructure Inspection

Uncrewed Aircraft Systems (UAS) are becoming increasingly popular for infrastructure inspections since they offer increased safety, decreased costs and consistent results, compared to traditional methods. However, there are still many open challenges before fully autonomous, reliable, and repeatable UAS inspections. While a UAS platform has increased mobility and can easily approach hard to reach areas, it has limited range and payload capacity and is susceptible to environmental disturbances. Therefore, current operations are limited to Visual Line of Sight (VLOS) manual inspections that usually result in just a qualitative (visual) assessment of the structure. The objective of this work is to propose solutions to these limitations in an effort to improve the effectiveness of UAS as an autonomous inspection platform. First, a heterogeneous marsupial robotic system is proposed as a solution to the limited range and flight time of UAS. The proposed system uses an Autonomous Surface Vehicle (ASV) to ferry the UAS close to the area of interest, where the latter can perform an inspection. Combining these two different platforms in a single system takes advantage of the individual strengths resulting on a platform that has the reach and high point of view of a UAS but has the range and operation time of the ASV. The proposed system was extensively tested over a six-month period in field deployments at Lake Murray and at the Congaree River, SC, USA, to validate its capabilities. As a solution to go beyond visual UAS inspections, a UAS equipped with a Stereo Digital Image Correlation (StereoDIC) system is proposed. StereoDIC is a non-contact non-destructive evaluation method that can accurately measure displacements, strains, strain rates, and geometry profiles. StereoDIC has become a method of choice in experimental mechanics with most studies performed in controlled lab environments with controlled lighting and stationary cameras positioned in the appropriate distance from the measured object. A prototype is built and tested in a lab setting to investigate its effectiveness and understand the challenges that might arise from the deployment of such a system. A comparative study using a stationary StereoDIC system validates the accuracy of the measurements while the acquisition of measurements showing the onset and evolution of defects and the dynamic response of the structure during a harmonic oscillation verifies the ability of the system to produce a quantitative assessment. Finally, using the lessons learned from the lab experiments, a new, upgraded, StereoDIC enabled UAS is developed for outdoor deployment and infrastructure inspection. To allow safe field deployments, the new system features a state estimation framework enabling operation in GNSS degraded environments while also estimating any external disturbances acting on the platform. These disturbances are utilized by the controller to make the platform adaptable to challenging weather conditions. The new system was deployed over an eight-month period at a railroad bridge in Columbia, SC. Initial data were collected that guided the investigations of effective speckle pattern applications. Experimental results from bridge measurements, while loaded from crossing trains, are presented and discussed