Research on Autonomous Cutting Method of Cantilever Roadheader

Xu, Ziyue; Liang, Minfu; Fang, Xiaohui; Wu, Gang; Chen, Ningning; Yang, Song

doi:10.3390/en15176190

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…It has been widely used and has shown promising application results. However, it is susceptible to device vibrations and other harsh underground working conditions, which can lead to data instability or sensor failure [85] [86] . Among noncontact measurement methods, visual systems are the most prominent.…”

Section: ) Cutting Head 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: ) Cutting Head 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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show abstract

“…Due to the complex excavation process of underground roadway excavation face, the intelligent level of excavation equipment is lower than that of coal mining equipment [5]. Pose measurement and precise positioning of tunneling equipment is the key to automation and intelligence, and it is also the basis of intelligent perception, automatic driving, autonomous navigation and trajectory correction [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Vision and Inertial Navigation Combined-Based Pose Measurement Method of Cantilever Roadheader

Wan

Zhang

et al. 2023

Sustainability

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Pose measurement of coal mine excavation equipment is an important part of roadway excavation. However, in the underground mining roadway of coal mine, there are some influencing factors such as low illumination, high dust and interference from multiple equipment, which lead to the difficulty in the position and pose measurement of roadheader with low measurement accuracy and poor stability. A combination positioning method based on machine vision and optical fiber inertial navigation is proposed to realize the position and pose measurement of roadheader and improve the accuracy and stability of the position and pose measurement. The visual measurement model of arm roadheader is established, and the optical fiber inertial navigation technology and the spatial coordinate transformation method are used. Finally, the Kalman filter fusion algorithm is used to fuse the two kinds of data to get the accurate roadheader pose data, and the inertia is compensated and corrected. Underground coal mine experiments are designed to verify the performance of the proposed method. The results show that the positioning error of the roadheader body using this method is within 40 mm, which meets the positioning accuracy requirements of roadway construction. This method compensates for the shortcomings of low accuracy and poor reliability of single vision measurement, single inertial navigation measurement and single odometer measurement.

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“…Hu et al simulated the change law of stress and displacement of the surrounding rock of a roadway under different cutting paths by using FLAC 3D, and determined the optimal cutting path in combination with the actual situation in the mine [17]. Xu et al established the spatial pose model of a cantilever roadheader by using the D-H parameter method, deduced the spatial pose matrix of roadheader and cutting head, determined the coordinates of the inflection point of the cutting path, and finally simulated it with MATLAB [18]. Zhang et al realized the adjustment of the pitching angle of the roadheader during tunneling by combining the particle swarm optimization algorithm and PID [19].…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning and Control Method for Secondary Autonomous Cutting of Cantilever Roadheader in a Large-Section Coal Roadway

Fang

et al. 2022

Sustainability

Self Cite

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A cantilever roadheader is the main tunneling equipment for underground coal mine roadways. The key to the safe, efficient and intelligent development of coal enterprises is to achieve the autonomous cutting and intelligent control of the cantilever roadheader. In order to realize the automatic cutting shaping control of a large-section coal roadway, the path planning and control method of secondary automatic cutting of a cantilever roadheader were studied. The Wangjialing 12307 belt roadway was used as the engineering background, the vertical displacement law of the roadway roof under different cutting paths was simulated with the FLAC 3D software, the reasonable cutting path was determined according to the actual situation, and the underground industrial test was carried out. The simplified model and spatial position and attitude coordinate system of the roadheader were established, the kinematics of the roadheader was analyzed, and the position and attitude expression of the cutting head center in the roadway coordinate system was obtained. The simplified model of the cutting head was established, the position expression of the pick in the roadway coordinate system was derived, the position coordinate of the inflection point and the cutting step distance were determined according to the relationship between the cutting head and the roadway boundary, and the cutting path control flow was designed. Finally, the reliability of the cutting path control method was verified with a MATLAB simulation. The research works provide a theoretical foundation for path planning and control to realize “secondary autonomous cutting of cantilever roadheader”.

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Research on Autonomous Cutting Method of Cantilever Roadheader

Cited by 4 publications

References 12 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

Vision and Inertial Navigation Combined-Based Pose Measurement Method of Cantilever Roadheader

Research on Path Planning and Control Method for Secondary Autonomous Cutting of Cantilever Roadheader in a Large-Section Coal Roadway

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