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. 

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

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.

/pdf/vision-and-inertial-navigation-combined-based-pose-1ie8jcid.pdf

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

Accurately mastering the power transmission characteristics of the cutting arm transmission shaft system is key to improving the reliability and working capacity of the cantilever roadheader. Based on the rigid–flexible coupling vibration characteristic modeling of the roadheader cutting arm, the vibration characteristics of different substructures in the transmission shaft system of the roadheader cutting arm were considered, the dynamic characteristic model was comprehensively constructed, and the numerical analysis was carried out with the parameters of the XTR260 tunnel hard rock roadheader to compare the vibration characteristics of the cutting head under different cutting conditions. The experiment was carried out by using an artificial concrete wall, and the measurement results verify the established dynamic model that lays the foundation for the dynamic design of a high-performance roadheader.

/pdf/cutting-mechanism-rotor-system-dynamic-characteristics-of-3j159ulq.pdf

Cutting Mechanism Rotor System Dynamic Characteristics of Cantilever Roadheader under Random Hard Rock Load

In this paper, a position and orientation measurement method for bolter-miner based on double-screen visual target is proposed, in which the target is composed of two industrial cameras and photosensitive surfaces. The point-cloud data of 2D–3D coordinates is established by using the method of grid calibration and index. Consequently, the posture parameters of bolter-miner and the horizontal/vertical offset distance of machine body key-points relative to the laneway tunneling axis (LTA) can be finally calculated with the prior information of the indicating laser. The experimental results show that the static repeatability measurement accuracy of laser-spot 3D coordinate is 0.07 mm, the repeatability measurement accuracy of yaw angle is better than 0.01°, the absolute measurement error of yaw angle is less than 0.05°, and the measurement error of horizontal/vertical offset distance are less than 5 mm. The experiment results proved that this method is practicable for application of the guidance and localization for bolter-miner. 

A high-accuracy position and orientation measurement method for bolter-miner based on double-screen visual target

Subpixel spot localization using multiscale anisotropic Gaussian tensor

Accuracy improvement for linear array photocell sensor

Multi-sensor fusion method for roadheader pose detection

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Accuracy improvement for linear array photocell sensor

Multi-sensor fusion method for roadheader pose detection