Two AUVs Guidance Method for Self-Reconfiguration Mission Based on Monocular Vision

Ren, Ranzhen; Zhang, Lichuan; Liu, Lu; Yuan, Yijie

doi:10.1109/jsen.2020.3042306

Cited by 27 publications

(14 citation statements)

References 25 publications

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“…Based on the visual alarm data of the transmission line channel, the maximum longitude lng max , minimum longitude lng min , maximum latitude lat max and minimum latitude lat min in the alarm data coordinate set are calculated respectively. The longitude difference as shown in the formula (1), and the latitude difference as shown in the formula (2). Where the number of alarm areas K, when calculating the number of latitude and longitude grid ranks N, if the square root of K is an integer, namely SQRT (K) is an integer, as shown in the formula (3); if the square root of K is not an integer, then set INT (SQRT (K)), set as shown in the formula (4).…”

Section: The Latitude and Longitude Grid For The Datamentioning

confidence: 99%

“…For the cluster order K to be generated, K objects will be randomly selected as the initial points, and the clustering results are sensitive to the initial points. As for the application scenarios of the identification of the high incidence of alarm areas, the division and clustering results of the above different initial points are somewhat different, resulting in the density and drift in the high incidence of alarm areas [1] . not drift.…”

Section: Introductionmentioning

confidence: 99%

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High-risk areas identification of transmission lines based on historical warning information

Wang¹,

Kong²

2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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With the upgrade of transmission line maintenance technology, the visual remote inspection of transmission line channel is widely used. However, the application of these marked data is currently in the stage of statistical analysis and report form, a deeper data mining work has not been carried out, such as the identification of areas with high incidence of alarm. This paper presents a method of analysis of high risk area of transmission line channel based on historical early warning data, which can be applied to the field of transmission line maintenance. By preprocessing the transmission line visual alarm data, using the improved k-means algorithm, analyze the clustering results, and finding out the series of modeling and data analysis. In addition, when determining the initial points, we propose the maximum and minimum longitude and latitude coordinates of the alarm data set, divide a certain number of longitude and latitude grid, obtain the candidate data within each grid, and the method of obtaining the initial point set after screening. Based on the initial point set, the alarm area distribution is reasonable, and the regional stability does not drift. This paper can identify the visual alarm data of transmission lines with high alarm incidence areas, and provide effective data support for maintenance personnel to guide the deployment of human resources and ensure the safe operation of transmission lines.

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Section: The Latitude and Longitude Grid For The Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-risk areas identification of transmission lines based on historical warning information

Wang¹,

Kong²

2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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show abstract

“…By definition, a system is observable if the output can fully reflect the properties of the system state [27,28]. Next, the rank criterion is used to analyze the observability of the system.…”

Section: Observability Analysismentioning

confidence: 99%

Multi-AUV Cooperative Navigation Algorithm Based on Temporal Difference Method

Ren

Zhang

Liu

et al. 2022

JMSE

Self Cite

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To reduce the cooperative positioning error and improve the navigation accuracy, a single master–slave AUV cooperative navigation method is proposed in this paper, which mainly focuses on planning the optimal path of the master AUV by the time difference (TD) method, under the premise that the path of the slave AUV has been planned. First, the model of multi-AUV cooperative navigation is established, and the observable problem of the system is analyzed. Second, for the single master–slave AUV cooperative navigation system, a Markov decision process (MDP)-based multi-AUV cooperative navigation model is established, and the master AUV path planning method is designed based on the TD method. Finally, the extended Kalman filter (EKF) and unscented Kalman filter (UKF) nonlinear filtering algorithms are applied to simulate and verify the algorithm that is proposed in this paper. The results show that the theoretical positioning error of the slave AUV can be controlled to about 3.2m by planning the path of the master AUV using the TD method. This method can not only reduce the observation error and positioning error of the slave AUV during the whole cooperative navigation process, but also keep the relative measurement distance between the master AUV and the slave AUV within an appropriate range.

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“…The detection range of the docking interface is increased to 6.5 m, and under the condition of strong noise, the average detection error of the docking distance is only 9.432 mm, and the average detection error of the angle is 2.353 degrees [ 16 ]. Ranzhen Ren et al used the combination of YOLO v3 and the P4P algorithm to locate an optical beacon with a length of 28 cm at a long distance of 3 m to 15 m and used Aruco markers for positioning within a range of 3 m, realizing the visual docking of the combination of far and near [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…The underwater ranging algorithm combined with deep learning has been proven to have the advantages of better real-time performance and higher detection accuracy [ 17 , 18 , 19 ]. However, the following problems need to be solved: The water pressure increases as the AUV’s navigation depth and volume increase.…”

Section: Introductionmentioning

confidence: 99%

Fast Underwater Optical Beacon Finding and High Accuracy Visual Ranging Method Based on Deep Learning

Zhang

Zhong

et al. 2022

Sensors

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Visual recognition and localization of underwater optical beacons is an important step in autonomous underwater vehicle (AUV) docking. The main issues that restrict the use of underwater monocular vision range are the attenuation of light in water, the mirror image between the water surface and the light source, and the small size of the optical beacon. In this study, a fast monocular camera localization method for small 4-light beacons is proposed. A YOLO V5 (You Only Look Once) model with coordinated attention (CA) mechanisms is constructed. Compared with the original model and the model with convolutional block attention mechanisms (CBAM), and our model improves the prediction accuracy to 96.1% and the recall to 95.1%. A sub-pixel light source centroid localization method combining super-resolution generative adversarial networks (SRGAN) image enhancement and Zernike moments is proposed. The detection range of small optical beacons is increased from 7 m to 10 m. In the laboratory self-made pool and anechoic pool experiments, the average relative distance error of our method is 1.04 percent, and the average detection speed is 0.088 s (11.36 FPS). This study offers a solution for the long-distance fast and accurate positioning of underwater small optical beacons due to their fast recognition, accurate ranging, and wide detection range characteristics.

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Two AUVs Guidance Method for Self-Reconfiguration Mission Based on Monocular Vision

Cited by 27 publications

References 25 publications

High-risk areas identification of transmission lines based on historical warning information

High-risk areas identification of transmission lines based on historical warning information

Multi-AUV Cooperative Navigation Algorithm Based on Temporal Difference Method

Fast Underwater Optical Beacon Finding and High Accuracy Visual Ranging Method Based on Deep Learning

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