Underwater Pipeline Leakage Detection Using Vision Based Techniques: Semi-AUV (SAUV) Approach

This thesis intends to convert a Remote Operated Vehicle (ROV) to a Semi-Autonomous Underwater Vehicle (SAUV) using a vision-based control system. The SAUV was used for automatic underwater gas pipeline tracking and leakage detection. the leakages in the pipeline using Computer Vision. The SAUV was designed to operate both manually and automatically in underwater conditions. The proposed SAUV has 6 thrusters to achieve 4 degrees of freedom controlled by the controller unit and powered by LiPo battery packs. Our underwater vehicle is equipped with sensors providing continuous feedback signals to automatically control the vehicle to track predefined trajectories. The SAUV can be self-stabilized as the center of gravity and center of buoyancy of the vehicle is positioned in such a way in the predefined plan. The SAUV captures images to perform line tracking along with the pipeline and gas bubble images during its mission. The multi-core umbilical cable is used here for the video signal, the feedback signal, and battery charging lines. This will be used only for development and test purposes and will be removed during autonomous missions. For performing all operations, various control schemes such as computer vision algorithm for object detection using python programming, OpenCV, Hough Transform Theory, etc. are applied. The proposed SAUV is expected to pave the way for the development of advanced underwater oil and gas pipeline industrial applications by ocean scientists

A Study on the Optimum Design of Autonomous Undewater Vechicle by Fiber-reinforced Composites

This research investigated to find out the possibilities of applying FRPs to the AUVs. In this study, two kinds of metal materials, which is one of the popularly used materials for manufacturing AUVs, and 6 kinds of FRP materials were considered. Material properties of FRPs were derived by tensile strength tests and chemical analysis. Moreover, various types of AUVs were designed by 8 kinds of materials properties. From structural analysis, we can find out that the weights of AUV by CFRP-Autoclave could be reduced by 60% in comparison with the weights of AUV by Al 7075-T6. Also, 40% weight reduction could be expected compared to the AUV by Ti-6Al-4V. Moreover, two types of AUVs, which were designed by CFRP-Autocalve and CFRP- VaRTM, have sufficient mechanical properties comparing with prior metal AUV models. Lastly, manufacturing processes for two types of AUV models were designed, and environmental safety were clearly confirmed by the result of moisture absorption test. In this result, we could conclude that the AUVs of CFRP-Autoclave and CFRP-VaRTM have various merits and potentialities as one of the AUV models, and two types of models were manufactured by applying the materials and manufacturing processes near future.1. 서론 = 2 2. 설계 프로세스 = 7 3. 재료의 선정 = 8 3.1 재료 선정의 기준 = 8 3.2 금속 재료 = 9 3.3 섬유강화복합재료 = 12 3.3.1 섬유강화복합재료의 특징 = 12 3.3.2 항공산업에서의 섬유강화 복합재료 = 14 3.3.3 섬유강화 복합재료의 경제성 = 17 4. 공정의 선정 = 20 4.1 공정 선정의 기준 = 20 4.2 Hand Lay-up = 20 4.3 진공백 성형(Vacuum Bag Molding) = 21 4.4 VaRTM(Vacuum Assisted Resin Transfer Molding) = 24 5. 시험편 제작 및 물성치 산정 = 25 5.1 시험편 제작 = 25 5.2 물성치 산정 = 28 6. CFD 유동 해석을 통한 최적 선체 모델의 선정 = 31 7. 무인 잠수정 설계 및 해석 = 34 7.1 무인 잠수정의 선체 설계 = 34 7.2 각 재료 및 공정별 무게 비교 = 35 7.3 각 재료 및 공정별 기계적 특성 비교 = 36 7.4 재료별 특성비교 = 39 8. 흡습에 의한 영향 고찰 = 41 8.1 시험 조건 = 42 8.2 시험 결과 = 45 9. 성형 방식에 관한 고찰 = 47 10. 결론 = 52 참고문헌 = 5

Visual-Aided Shared Control of Semi-Autonomous Underwater Vehicle for Efficient Underwater Grasping

Human intelligence has the advantage for making high-level decisions in the remote control of underwater vehicles, while autonomous control is superior for accurate and fast close-range pose adjustment. Combining the advantages of both remote and autonomous control, this paper proposes a visual-aided shared-control method for a semi-autonomous underwater vehicle (sAUV) to conduct flexible, efficient and stable underwater grasping. The proposed method utilizes an arbitration mechanism to assign the authority weights of the human command and the automatic controller according to the attraction field (AF) generated by the target objects. The AF intensity is adjusted by understanding the human intention, and the remote-operation command is fused with a visual servo controller. The shared controller is designed based on the kinematic and dynamic models, and model parameter uncertainties are also addressed. Efficient and stable control performance is validated by both simulation and experiment. Faster and accurate dynamic positioning in front of the target object is achieved using the shared-control method. Compared to the pure remote operation mode, the shared-control mode significantly reduces the average time consumption on grasping tasks for both skilled and unskilled operators

Underwater robots provide similar fish biodiversity assessments as divers on coral reefs

Coral reefs are under increasing threat, and the loss of reef-associated fishes providing valuable ecosystem services is accelerating. The monitoring of such rapid changes has become a challenge for ecologists and ecosystems managers using traditional approaches like scuba divers performing underwater visual censuses (UVC) or diver operated video recording (DOV). However, the use of small, low-cost robots could help tackle the challenge of such monitoring, provided that they perform at least as well as diver-based methods. To address this question, tropical fish assemblages from 13 fringing reefs around Mayotte Island (Indian Ocean) were monitored along 50 m-long transects using stereo videos recorded by a semi-autonomous underwater vehicle (SAUV) and by a scuba diver (Diver Operated stereo Video system, DOV). Differences between the methods were tested for complementary fish assemblage metrics (species richness, total biomass, total density, Shannon diversity and Pielou evenness) and for the number and size of nine targeted species. SAUV recorded on average 35% higher biomass than DOV which in turn recorded on average 12% higher species richness. Biomass differences were found to be due to SAUV monitoring larger fishes than DOV, a potential marker of human-related fish avoidance behaviour. This study demonstrates that SAUV provides accurate metrics of coral reef fish biodiversity compared to diver-based procedures. Given their ability to conduct video transects at high frequency, 100 m depth range and at a moderate cost, SAUV is a promising tool for monitoring fish assemblages in coral reef ecosystems

Underwater robots provide similar fish biodiversity assessments as divers on coral reefs

International audienceCoral reefs are under increasing threat, and the loss of reef-associated fishes providing valuable ecosystem services is accelerating. The monitoring of such rapid changes has become a challenge for ecologists and ecosystems managers using traditional approaches like scuba divers performing underwater visual censuses (UVC) or diver operated video recording (DOV). However, the use of small, low-cost robots could help tackle the challenge of such monitoring, provided that they perform at least as well as diver-based methods. To address this question, tropical fish assemblages from 13 fringing reefs around Mayotte Island (Indian Ocean) were monitored along 50 m-long transects using stereo videos recorded by a semi-autonomous underwater vehicle (SAUV) and by a scuba diver (Diver Operated stereo Video system, DOV). Differences between the methods were tested for complementary fish assemblage metrics (species richness, total biomass, total density, Shannon diversity and Pielou evenness) and for the number and size of nine targeted species. SAUV recorded on average 35% higher biomass than DOV which in turn recorded on average 12% higher species richness. Biomass differences were found to be due to SAUV monitoring larger fishes than DOV, a potential marker of human-related fish avoidance behaviour. This study demonstrates that SAUV provides accurate metrics of coral reef fish biodiversity compared to diver-based procedures. Given their ability to conduct video transects at high frequency, 100 m depth range and at a moderate cost, SAUV is a promising tool for monitoring fish assemblages in coral reef ecosystems

Coupling underwater autonomous vehicles and automatic video analysis for efficient monitoring of coral reef ecosystems: promises and challenges

International audienceCoral reefs are facing increasing effects of global changes. Yet, diver-based surveys do not allow assessing the subsequent changes of reef fish assemblages over large areas and at high frequency. Development of Semi-Autonomous Underwater Vehicle (SAUV) and Deep Learning technologies could help to tackle this challenging task. To this end, we developed a small-size underwater vehicle carrying stereo cameras and environmental sensors, capable of automatically perform standardized trajectories in a complex environment. Our robot can, for example, dynamically verify mission-dependent properties (remaining to a constant depth or altitude, moving below a maximal speed, following a virtual transect line, etc.) and automatically transition among missions. We are actually working toward full autonomous functionalities in order to remove the umbilical cable presently necessary for human operators to visually detect dangerous obstacles. The umbilical cable limits drastically the operational range of the system, increases the logistical burden and induces huge disturbances on the system (it can cling on coral, creates overwhelming drag, limits the action range of the robot, etc.). But, removing the umbilical cable implies to improve autonomous system reaction to specific troubling events (obstacle detection and avoidance, energy management, essential sensors failure, etc.). In parallel, we developed Deep Learning based computer vision algorithms capable of automatically, locating and identifying fishes in videos for post treatment of recordings. Tests of these novel tools revealed that diver and SAUV-operated video recordings showed little differences in describing overall structure of fish assemblages (both set of video were treated manually by the same operator). SAUV even appeared to be more appropriate to survey commercial species, which are probably more scared by the potential "human predators". Similarly, Deep Learning algorithms were as good as humans to identify fish species but at a higher rate. Full autonomy is still a hard point to reach for mobile robotics in harsh environment such as oceans, but combined with automatic video analysis, such tools are now necessary to succeed in ecosystems monitoring. While necessary hardware are now becoming available, greater effort needs to be made in algorithmics

Optimal control system for a semi-autonomous underwater vehicle.

Este trabalho apresenta aspectos teóricos e práticos relevantes do desenvolvimento do Sistema de Navegação e Controle (SNC) a ser implementado em um Veículo Submarino Semi-Autônomo (VSSA), tipo não carenado e auto propelido, que está em desenvolvimento e construção na Escola Politécnica da USP, para a Petrobrás. Os três graus de liberdade horizontais são controlados para seguirem trajetórias pré-definidas, enviadas como sinais de referência para navegação por uma estação de apoio localizada na superfície, responsável pela guiagem do veículo. Os sinais de referência enviados são acústicos propagados pela água. A implementação física do SNC e o controle dos três graus de liberdade verticais não fazem parte do escopo deste trabalho. O SNC consiste em um controlador determinístico, um seguidor de trajetórias linear quadrático alimentado por um vetor de estados estimado assintoticamente. Por segurança, em caso de falha de algum sensor, e para filtrar ruídos nos sinais medidos, um estimador de estados de ordem plena é utilizado conjuntamente. Pela simplicidade de síntese e implementação, esta arquitetura de controle é considerada a melhor alternativa para capacitar o VSSA a executar os movimentos semi-autônomos desejados. As técnicas de controle utilizadas requerem a linearização do modelo matemático não-linear que descreve o comportamento dinâmico do veículo. O modelo é obtido de maneira simplificada. Os resultados são gerados por simulações com o modelo não-linear.This work presents theoretical and practical aspects of the development of the Navigation and Control System (NCS) to be implemented into a Petrobras\' Semi-Autonomous Underwater Vehicle (SAUV), an open-frame and self-propelled type, which is being developed and built at Escola Politécnica of the University of São Paulo (EPUSP). The three horizontal Degrees-of-Freedom (DoF) are controlled so that they can follow a pre-defined trajectory sent as navigation reference signals to the NCS by a support ship, responsible for the guidance of the vehicle and placed on the ocean surface. Reference signals are sent as acoustic signals through the ocean water. The implementation and the control of the three vertical DoF are not in the scope of the present work. The NCS is based upon a deterministic controller, a Linear Quadratic (LQ) trajectory follower fed by an asymptotically estimated state vector, even though all the state variables are available by direct measurents. For safety, if some sensor fails, and for filtering noise on measured signals, a full-order state estimator is also designed. Since the LQ controller architecture is rather simple to design and implement, it was elected to control the SAUV manoeuvers. The control techniques require a linear model of the dynamics of the vehicle. Hence, a linearization procedure is applied to the system of nonlinear differential equations that describe the dynamic behavior of the SAUV. The results presented are provided by computer-aided simulations with the nonlinear model of the plant