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

IF 1.3 Q3 ENGINEERING, MULTIDISCIPLINARY International Journal of Engineering and Technology Innovation Pub Date : 2020-01-01 DOI:10.46604/ijeti.2020.4158
T. Nhat, Hyeung-Sik Choi, J. Sur, Jin-Il Kang, Hyun-joong Son
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引用次数: 4

Abstract

Detecting the relative position of the docking station is a very important issue for the homing of AUVs (Autonomous Unmanned Vehicles). To detect the position of the light source, a pinhole camera model structure was proposed like the camera model. However, due to the sensor resolution and the distortion errors of the pinhole camera system, the application of the camera of docking the under turbid sea environments is almost impossible. In this paper, a new method 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. In addition, to improve the system, a neural network (NN) algorithm constructing a model relating the light inputs and optical sensor which are developed in this study is proposed. To evaluate the performance of the NN algorithm, the experiments were performed in the air beforehand. The result shows that the NN algorithm with AUV docking system using the NN model is better than the pinhole camera model.
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基于光学传感器和神经网络的水下机器人对接位置确定研究
对接站的相对位置检测是自动无人驾驶飞行器(auv)寻的一个重要问题。为了检测光源的位置,提出了一种类似相机模型的针孔相机模型结构。然而,由于传感器分辨率和针孔相机系统的畸变误差,相机在浑浊海洋环境下的对接应用几乎是不可能的。本文提出了一种利用光源检测对接站位置的新方法。此外,一种新开发的光学传感器,使其更容易感知光源比相机系统的水下航行器的导引。此外,为了改进系统,提出了一种神经网络算法,该算法构建了光输入和光传感器之间的模型。为了评估神经网络算法的性能,事先在空中进行了实验。结果表明,基于神经网络模型的AUV对接系统神经网络算法优于针孔摄像机模型。
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来源期刊
CiteScore
2.80
自引率
0.00%
发文量
18
审稿时长
12 weeks
期刊介绍: The IJETI journal focus on the field of engineering and technology Innovation. And it publishes original papers including but not limited to the following fields: Automation Engineering Civil Engineering Control Engineering Electric Engineering Electronic Engineering Green Technology Information Engineering Mechanical Engineering Material Engineering Mechatronics and Robotics Engineering Nanotechnology Optic Engineering Sport Science and Technology Innovation Management Other Engineering and Technology Related Topics.
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