Pose Measurement System of Double Shield Universal Compact TBM

Wenhui, 杨文辉 Yang; Jiarui, 林嘉睿 Lin; Yang, 高扬 Gao; Jigui, 邾继贵 Zhu

doi:10.3788/aos201535.1112005

Cited by 3 publications

(1 citation statement)

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“…When a target with light sources is affixed to the measured object, the position of the moving object can be obtained from the target position using coordinate transformation. On the basis of back shield pose in real time, [81] measured the position and orientation between the front and back shields of a double-shield universal compact TBM by designing a spatial distribution model consisting of six crossing optical characteristic points (as shown in Fig. 7 (d)).…”

Section: ) Roadheader Body Visual Detectionmentioning

confidence: 99%

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

Du,

Zhang,

Liang

et al. 2023

IEEE Access

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The realization of intelligent mining is the only method for realizing high-quality development in the coal industry. As the forefront working link in mine production, achieving automatic roadway tunneling control is key to improving production efficiency, enhancing intelligence, and reducing accident rates at fully mechanized tunneling working faces. Among various detection techniques, machine vision technology stands out with advantages of non-contact measurement, rich information acquisition, and high detection accuracy. The detection and control of tunneling equipment groups based on machine vision has become a research hotspot in the intelligence process of coal mines. This study first introduces the key technologies of a visual detection system, including camera calibration, image preprocessing, feature extraction, visual matching, target segmentation and recognition, visual measurement, and 3D reconstruction. It then elaborates on detection principles, workflows, limitations, precautions, and development status of various vision detection systems in practical application scenarios at tunneling faces, such as tunneling equipments, anchoring systems, transportation systems, and safety auxiliary systems, which significantly improve production safety and efficiency. Finally, considering challenging work conditions and strong interference in mines, the successful adaptation of machine vision to excavation sites relies on addressing technical challenges related to poor environment adaptability, limited imaging field of view, and low intelligence level. Furthermore, according to existing research results and the current technical status, this paper forecasts key technologies that need to be developed in the future for coal mine intelligent equipment systems based on machine vision, including multi-sensor information fusion, equipment group collaborative control, and digital twin-driven remote monitoring.INDEX TERMS Machine vision, coal machinery equipment, detection and measurement, roadway fully mechanized tunneling faces, unmanned coal mine.

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Section: ) Roadheader Body Visual Detectionmentioning

confidence: 99%

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

Du,

Zhang,

Liang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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A Noncontact Cutterhead Dynamic Coordinate Measurement Method for Double-Shield TBM Guidance Based on Photographic Imaging

Huang

et al. 2022

IEEE Trans. Instrum. Meas.

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Edge detection based on Retinex theory and wavelet multiscale product for mine images

Tong

Zhou

et al. 2016

Appl. Opt.

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The application of visual technology to mine robots has become a hot topic in the development of coal mine automatic production. Key techniques of robot control are the feature recognition of sampled videos and the perception of complex surroundings. However, it is difficult for features in underground images with dark hue and low target discrimination to be recognized and extracted, especially for reasons of the nonuniform illumination and heavy dust concentration in mines. Hence, an edge detection algorithm based on the Retinex theory and wavelet multiscale product is proposed in this paper for low-light-level mine image feature extraction, which employs a modified multiscale Retinex method to deal with the low frequency subplot after the wavelet decomposition, an improved fuzzy enhancement approach to handle high frequency components, and finally a revised multiscale product edge detection algorithm to obtain the ultima edge image. Compared with a variety of algorithms by detecting edges of both normal illuminated and underground images, experimental results show that with characteristics of high real-time performance and detection accuracy, the proposed algorithm can exactly meet the needs of surrounding environment perception for mine robots, which applies well to image edge detection in low illumination mines.

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Pose Measurement System of Double Shield Universal Compact TBM

Cited by 3 publications

References 0 publications

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

Applications of Machine Vision in Coal Mine Fully Mechanized Tunneling Faces: A Review

A Noncontact Cutterhead Dynamic Coordinate Measurement Method for Double-Shield TBM Guidance Based on Photographic Imaging

Edge detection based on Retinex theory and wavelet multiscale product for mine images

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