Experimental Analysis of a Visual-Recognition Control for an Autonomous Underwater Vehicle in a Towing Tank

Yu, Chao Ming; Lin, Yu Hsien

doi:10.3390/app10072480

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…Finally, autonomous dynamic control on the rudders and the propeller has been successfully verified by testing the maneuverability of the AUV in the towing tank. Compared with our previous study [48], the advantage of this study is that the features of the object could be identified using color recognition and selection of the region of interest (ROI) area. Meanwhile, the FLC control of the AUV can be performed well by using the predicted data of the EKF.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, some processing methods are adopted to eliminate the influence of noise (attenuation and scattering of light by suspended particles) and improve detection. The image-processing methods of this study include image capture, color space conversion, image binarization, filter, and morphology operation [48]. The advantage of these image-processing methods is to obtain both the color and morphological information of the object in an efficient way for further processing.…”

Section: Recognition Of Image Featuresmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Recognition Of Image Featuresmentioning

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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“…The development of autonomous underwater vehicles (AUVs) over the past three decades has exhibited a high security, high controllability, and low cost [1]. AUVs are utilized extensively in hydrological surveys, seabed surveys, environmental assessments, and other fields today.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

Zhang

Zhang²,

Yan

et al. 2023

Machines

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Based on data-driven and mixed models, this study proposes a fault detection method for autonomous underwater vehicle (AUV) rudder systems. The proposed method can effectively detect faults in the absence of angle feedback from the rudder. Considering the parameter uncertainty of the AUV motion model resulting from the dynamics analysis method, we present a parameter identification method based on the recurrent neural network (RNN). Prior to identification, singular value decomposition (SVD) was chosen to denoise the original sensor data as the data pretreatment step. The proposed method provides more accurate predictions than recursive least squares (RLSs) and a single RNN. In order to reduce the influence of sensor parameter errors and prediction model errors, the adaptive threshold is mentioned as a method for analyzing prediction errors. In the meantime, the results of the threshold analysis were combined with the qualitative force analysis to determine the rudder system’s fault diagnosis and location. Experiments conducted at sea demonstrate the feasibility and effectiveness of the proposed method.

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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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Experimental Analysis of a Visual-Recognition Control for an Autonomous Underwater Vehicle in a Towing Tank

Cited by 8 publications

References 24 publications

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

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

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

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

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