Autonomous Underwater Pipeline Tracking Control Based on Visual Images

Zhou, Zexing; Shen, Hai-Long; Huang, Hai; Zhou, Hao; Wan, Zhaoliang; Wang, Zhuo; Xu, Yang

doi:10.1109/robio.2018.8664853

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…To achieve real-time tracking of subsea pipelines, the AUV needs to be able to detect and locate subsea pipelines in real time and accurately. Lidar, underwater cameras, side-scan sonar and forward sonar can only detect exposed uncovered subsea pipelines and cannot detect buried subsea pipelines [6][7][8][9]. Sub-bottom profiling sonar can penetrate a certain depth of the seabed to detect buried pipelines, and when it moves perpendicular to the pipeline, it can detect the arc profile of the pipeline [10,11].…”

Section: Introductionmentioning

confidence: 99%

A localization method for subsea pipeline based on active magnetization

Huang¹,

Wang²,

Ma³

et al. 2022

Meas. Sci. Technol.

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Accurate localization of subsea pipelines is the prerequisite of real-time tracking and elaborate inspection by underwater robots. Magnetic anomaly generated by the ferromagnetic pipeline can be used to locate both uncover and buried pipelines. However, due to low signal ratio and model inconstancy under weak and variable ambient magnetization, there is still missing an intuitive and reliable pipeline localization method in order to realize pipeline tracking. This paper proposes a method capable of immediately and accurately locating pipelines via active magnetization and vertical magnetic measurement. Finite element simulation shows that magnet array can significantly enhance the magnetic anomaly, and the vertical magnetic component alone can accurately indicate pipeline position, avoiding the inconvenience of magnetic three-component alignment in field. It is experimentally demonstrated that magnetic detection SNR can be significantly increased by 5dB~20dB for a Φ219mm steel pipe by using the magnet array, and the max lateral positioning error is 0.03m and much smaller than that without magnet.

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Section: Introductionmentioning

confidence: 99%

A localization method for subsea pipeline based on active magnetization

Huang¹,

Wang²,

Ma³

et al. 2022

Meas. Sci. Technol.

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“…Machine vision processing technology mainly employs cameras and computers to analyze images and provide control information of the AUV drive system. There have been numerous research studies on the application of an AUV equipped with machine vision processing technology, such as the detection of underwater man-made structures and pipeline detection [3][4][5][6][7][8][9][10], auxiliary sonar image navigation [11,12], simultaneous localization and mapping (SLAM) [13][14][15][16][17], obstacle avoidance [18,19], identifying and tracking the habitats of sea animals [20], underwater docking systems [21][22][23][24][25][26][27], and object tracking [28][29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Towards the Design and Implementation of an Image-Based Navigation System of an Autonomous Underwater Vehicle Combining a Color Recognition Technique and a Fuzzy Logic Controller

Lin

2021

Sensors

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This study proposes the development of an underwater object-tracking control system through an image-processing technique. It is used for the close-range recognition and dynamic tracking of autonomous underwater vehicles (AUVs) with an auxiliary light source for image processing. The image-processing technique includes color space conversion, target and background separation with binarization, noise removal with image filters, and image morphology. The image-recognition results become more complete through the aforementioned process. After the image information is obtained for the underwater object, the image area and coordinates are further adopted as the input values of the fuzzy logic controller (FLC) to calculate the rudder angle of the servomotor, and the propeller revolution speed is defined using the image information. The aforementioned experiments were all conducted in a stability water tank. Subsequently, the FLC was combined with an extended Kalman filter (EKF) for further dynamic experiments in a towing tank. Specifically, the EKF predicts new coordinates according to the original coordinates of an object to resolve data insufficiency. Consequently, several tests with moving speeds from 0.2 m/s to 0.8 m/s were analyzed to observe the changes in the rudder angles and the sensitivity of the propeller revolution speed.

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The Fuzzy-Based Visual Intelligent Guidance System of an Autonomous Underwater Vehicle: Realization of Identifying and Tracking Underwater Target Objects

Lin

Huang

et al. 2022

Int. J. Fuzzy Syst.

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Autonomous Underwater Pipeline Tracking Control Based on Visual Images

Cited by 3 publications

References 5 publications

A localization method for subsea pipeline based on active magnetization

A localization method for subsea pipeline based on active magnetization

Towards the Design and Implementation of an Image-Based Navigation System of an Autonomous Underwater Vehicle Combining a Color Recognition Technique and a Fuzzy Logic Controller

The Fuzzy-Based Visual Intelligent Guidance System of an Autonomous Underwater Vehicle: Realization of Identifying and Tracking Underwater Target Objects

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