Research on Body Positioning Measurement Method of Bolting Robot based on Vision Theory

Hao, Xuedi; Yang, Xueqiang; Zhang, Jinglin; Ding, Yaotian; Wu, Miao

doi:10.21203/rs.3.rs-501934/v1

Cited by 2 publications

(4 citation statements)

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“…Meanwhile, Xu et al [8] opted to deploy multiple ArUco markers on the seabed, thereby advancing underwater visual navigation. Wu et al [9,10] designed a monocular visual positioning system for manned submersibles reaching depths of up to 7000 m based on cooperative markers. However, the aforementioned visual positioning techniques, employing optical, cooperative, and AR markers, exhibit notable constraints when applied to underwater cluster positioning, as detailed in the following discussion.…”

Section: Related Workmentioning

confidence: 99%

“…Sub-problem (10) seeks to tackle the reduction in contrast and clarity of underwater images, a challenge predominantly caused by light attenuation and forward scattering.…”

Section: Model Buildingmentioning

confidence: 99%

“…In the resolution of Sub-problem (10), the focus is directed towards issues related to light attenuation and forward scattering. The formula t(x) = e −βd(x) , as delineated in Equation (8b), serves to quantify the attenuation of light throughout its underwater transmission, a phenomenon that considerably reduces image contrast.…”

Section: Hoirm-based Image Recoverymentioning

confidence: 99%

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Localization Method for Underwater Robot Swarms Based on Enhanced Visual Markers

Wei,

Yang,

Zhou

et al. 2023

Electronics

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In challenging tasks such as large-scale resource detection, deep-sea exploration, prolonged cruising, extensive topographical mapping, and operations within intricate current regions, AUV swarm technologies play a pivotal role. A core technical challenge within this realm is the precise determination of relative positions among AUVs within the cluster. Given the complexity of underwater environments, this study introduces an integrated and high-precision underwater cluster positioning method, incorporating advanced image restoration algorithms and enhanced underwater visual markers. Utilizing the Hydro-Optical Image Restoration Model (HOIRM) developed in this research, image clarity in underwater settings is significantly improved, thereby expanding the attenuation coefficient range for marker identification and enhancing it by at least 20%. Compared to other markers, the novel underwater visual marker designed in this research elevates positioning accuracy by 1.5 times under optimal water conditions and twice as much under adverse conditions. By synthesizing the aforementioned techniques, this study has successfully developed a comprehensive underwater visual positioning algorithm, amalgamating image restoration, feature detection, geometric code value analysis, and pose resolution. The efficacy of the method has been validated through real-world underwater swarm experiments, providing crucial navigational and operational assurance for AUV clusters.

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Section: Related Workmentioning

confidence: 99%

“…Sub-problem (10) seeks to tackle the reduction in contrast and clarity of underwater images, a challenge predominantly caused by light attenuation and forward scattering.…”

Section: Model Buildingmentioning

confidence: 99%

Section: Hoirm-based Image Recoverymentioning

confidence: 99%

See 1 more Smart Citation

Localization Method for Underwater Robot Swarms Based on Enhanced Visual Markers

Wei,

Yang,

Zhou

et al. 2023

Electronics

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show abstract

“…Employing a laser radar sensor and camera, Professor Ma's team [111] designed a gantry drill-anchor robot to calculate the motion control quantity of each part of the robot to avoid collisions between the boring crown and the anchor network and ensure the accurate alignment of the drilling rig. Based on the principle of monocular vision, [112] proposed a method of body positioning measurement of a bolting robot, laying the foundation for the localization control of the mining face and the automation and unmanned bolting. In the process of "mine equipment robotization", the anchoring operation robot still has plenty of room for further development.…”

Section: B Anchoring and Supporting Systemmentioning

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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Research on Body Positioning Measurement Method of Bolting Robot based on Vision Theory

Cited by 2 publications

References 0 publications

Localization Method for Underwater Robot Swarms Based on Enhanced Visual Markers

Localization Method for Underwater Robot Swarms Based on Enhanced Visual Markers

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

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