scholarly journals Autonomous underwater vehicle based marine multi-component self-potential method: observation scheme and navigational correction

2020 ◽  
Author(s):  
Zhongmin Zhu ◽  
Jinsong Shen ◽  
Chunhui Tao ◽  
Xianming Deng ◽  
Tao Wu ◽  
...  

Abstract. Marine self-potential (SP) investigation is an effective method to study deep-sea hydrothermal vents and seafloor sulfide deposits. At present, the commonly used marine self-potential instrument is a towed electrode array, large noise involves when the seafloor topography is complex, causing the greatly change of electrode distance and array attitude. In this paper, a new multi-component electric field observation system based on underwater autonomous underwater vehicle (AUV) was introduced for the measurement of seafloor self-potential. The system was tested in a lake and the multi-component self-potential data were collected. Observed data involve the navigational information of AUV, which could be corrected using a rotation transform. After navigational correction, measured data can recover the location of the artificial source well using self-potential tomography. The experimental results showed that the new SP system can be applied to marine SP observations, providing an efficient and low-noise SP acquisition method for marine resources and environmental investigations.

2021 ◽  
Vol 10 (1) ◽  
pp. 35-43
Author(s):  
Zhongmin Zhu ◽  
Jinsong Shen ◽  
Chunhui Tao ◽  
Xianming Deng ◽  
Tao Wu ◽  
...  

Abstract. Marine self-potential (SP) investigation is an effective method to study deep-sea hydrothermal vents and seafloor sulfide deposits. At present, one of the commonly used marine self-potential systems is a towed array of electrodes. Large noises are recorded when great changes in electrode distance and array attitude occur due to the complex seafloor topography. In this paper, a new multicomponent electrical field observation system based on an autonomous underwater vehicle (AUV) was introduced for the measurement of seafloor self-potential signals. The system was tested in a lake, and the multicomponent self-potential data were collected from there. Observed data involve the navigational information of the AUV, which could be corrected using a rotation transform. After navigational correction, measured data can recover the location of the artificial source using self-potential tomography. The experimental results showed that the new SP system can be applied to marine SP observations, providing an efficient and low-noise SP acquisition method for marine resources and environmental investigations.


2019 ◽  
Vol 9 (21) ◽  
pp. 4614
Author(s):  
Lingyan Dong ◽  
Hongli Xu ◽  
Xisheng Feng ◽  
Xiaojun Han ◽  
Chuang Yu

We propose an acoustic-based framework for automatically homing an Autonomous Underwater Vehicle (AUV) to the fixed docking station (F-DS) and mobile docking station (M-DS). The proposed framework contains a simultaneous localization method of AUV and docking station (DS) and a guidance method based on the position information. The Simultaneous localization and mapping (SLAM) algorithm is not available as the statistical characteristics of the measurement error of the observation system are unknown. To solve this problem, we propose a data pre-processing method. Firstly, the measurement error data of acoustic sensor are collected. Then, We propose a Variational Auto-Encoder (VAE) based Gaussian mixture model (GMM) for estimating the statistical characteristics of measurement error. Finally, we propose a support vector regression (SVR) algorithm to fit the non-linear relationship between the statistical characteristics of measurement error and its corresponding working distance. We adopt a guidance method based on line-of-sight (LOS) and path tracking method for homing an AUV to the fixed docking station (F-DS) and mobile docking station (M-DS). The lake experimental data are used to verify the performance of the localization with the estimated statistical characteristics of measurement error.


2006 ◽  
Vol 2006 (0) ◽  
pp. _1P1-E34_1-_1P1-E34_4
Author(s):  
Takeshi NAKATANI ◽  
Tamaki URA ◽  
Yoshiaki NOSE ◽  
Takashi SAKAMAKI ◽  
Yuzuru ITO ◽  
...  

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