On the Position Determination of Docking Station for AUVs Using Optical Sensor and Neural Network

Nhat, Thieu Quang Minh; Choi, Hyeung-Sik; Vu, Mai The; Sur, Joono; Kang, Jin-Il; Son, Hyun-Joong

doi:10.46604/ijeti.2020.4158

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…A new method of detecting the position of the docking station using a light source is presented. Also, a newly developed optical sensor which makes it much easier to sense the light source than the camera system for homing of the AUV under the water is performed [14].…”

Section: Introductionmentioning

confidence: 99%

Simulation Study on a New Hybrid Autonomous Underwater Vehicle with Elevators

Jiafeng Huang,

Hyeung-Sik Choi,

Dong-Wook Jung

et al. 2023

Proc. eng. technol. innov.

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This study aims to design a new hybrid twin autonomous underwater vehicle (HTAUV) consisting of dual cylinder hulls and analyze its pitching motion. The kinematic model for the HTAUV is established, followed by the execution of hydrodynamic simulation CFD of the HTAUV using Ansys Fluent. These simulations are conducted to obtain the hydrodynamic force equation of the HTAUV, which relates to the deflection angle of the elevator. Through the motion simulation of the HTAUV, under the same net buoyancy condition, notable differences emerge when the elevator is deflected. Specifically, parameters such as gliding speed, gliding angle, and pitch angle of the HTAUV are larger when the elevator is deflected, as compared to cases where no deflection is applied.

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Section: Introductionmentioning

confidence: 99%

Simulation Study on a New Hybrid Autonomous Underwater Vehicle with Elevators

Jiafeng Huang,

Hyeung-Sik Choi,

Dong-Wook Jung

et al. 2023

Proc. eng. technol. innov.

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“…With the rapid and versatile development of UAVs, they can be applied to people's lives to reduce labor costs and improve efficiency. Unmanned vehicles have been utilized in many applications, 11 – 17 such as failed solar panel detection, octopus drift fishing, geological and environmental applications, parcel delivery, and pollution monitoring. In many applications, the drone flies to a location to collect data, and the navigation is achieved either through human operators or autonomously.…”

Section: Introductionmentioning

confidence: 99%

Application of image identification to UAV control for cage culture

Liang

Juang

2022

Science Progress

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The purpose of this study was to save manpower and reduce costs on water quality measurement in cage culture. An unmanned aerial vehicle system was applied to locate the target net cage and detect the water quality and temperature in the desired cage automatically. This paper presents the use of image recognition and deep learning to find a predefined target location of cage aquaculture. The whole drone control and image recognition process was based on an onboard computer and was successfully realized in an actual environment. When the drone approached the net cage, image recognition was utilized to fix the position of the unmanned aerial vehicle on the net cage and drop a sensor to check the water quality. The proposed system could improve conventional manned measurement methods and reduce the costs of cage culture.

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Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

Jeong

et al. 2021

IFS

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In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9].

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On the Position Determination of Docking Station for AUVs Using Optical Sensor and Neural Network

Cited by 3 publications

References 14 publications

Simulation Study on a New Hybrid Autonomous Underwater Vehicle with Elevators

Simulation Study on a New Hybrid Autonomous Underwater Vehicle with Elevators

Application of image identification to UAV control for cage culture

Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

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