Underwater applications of light emitting diodes

Underwater wet-mateable connectors have been widely used to reduce the cost and the time of installation, maintenance and reconfiguration in many fields, such as the oil and gas (O&G) industry, offshore renewable energy (ORE), and undersea observatories. In the past few years, the authors’ group has made some efforts in developing wet-mateable connectors. This paper presents a methodology for designing and testing a wet-mateable electrical connector. First, an innovative wet-mateable electrical connector with dual-bladder pressure-balanced oil-filled (PBOF) technology is proposed. Second, the generalized equations of differential pressure are derived. Then, a procedure of thermal-electric-structure (TES) coupling simulation is proposed, and a series of finite element analysis (FEA) involving coupled multi-field problems is conducted, including thermal-electric coupling analysis, static structural analysis, and dynamic analysis. Finally, a prototype of the proposed connector is developed successfully, and its electrical performance is verified by the online test in a hydrostatic pressure environment with an ocean depth of 3000 m, which has reached the leading level in China. This paper is the first discloser on wet-mateable connectors in the aspects of design, theory, simulation and testing, which might be helpful to many ocean scientists in developing countries who are technically blocked or could not afford the high cost.

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Robot Docking and Charging Techniques in Real Time Deep Learning Model

Alain,

Paul

2024

JRS

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This article describes various approaches that utilize computer vision and Lidar technology. These approaches include, but not limited to, vision-based algorithms such as the Faster RCNN model and AprilTag; and single shot detectors (SSD). In carrying out docking and recharging operations, the aforementioned approaches have shown varying degrees of success and accuracy. In order to make it easier for mobile robot systems to perform autonomous docking and recharging (ADaR) in industrial settings, this study presents a new method that employs vision and Lidar technology. In this study, we propose the YOLOv7 deep learning model to find charging stations. To further simplify docking with the specified wireless charging station, a Lidar-based approach is used to precisely modify the robot's position. An account of the assessment standards and training procedure used for the adjusted YOLOv7 model is provided in the results and discussion section. In this research, it was found that the model's 86.5% mean Average Precision (mAP) within the IoU range of 0.5 to 0.9 is evidence of its efficacy. In addition, the detection and identification of charging stations had an average accuracy rate of 95% in the studies conducted in real-world settings.

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Underwater applications of light emitting diodes

Cited by 3 publications

References 13 publications

On technical issues for underwater charging of robotic fish schools using ocean renewable energy

On technical issues for underwater charging of robotic fish schools using ocean renewable energy

An Underwater Wet-Mateable Electrical Connector with Dual-Bladder Pressure-Balanced Oil-Filled (PBOF) Technology

Robot Docking and Charging Techniques in Real Time Deep Learning Model

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