Positioning of underground mining equipment in coal mines is a primary challenge in the intelligent development of tunnel excavation. Accurate and reliable position measurement plays a crucial role in improving excavation efficiency. However, the stability and accuracy of traditional measurement methods are difficult to guarantee due to factors such as vibration, magnetic interference, and the absence of GPS signals in coal mine environments. To address the problem of unstable measurement of the boom-type roadheader‘s position, this paper proposes a binocular vision-based measurement technique that utilizes four light spots as characteristic points. By processing the target images captured by the binocular camera and combining the minimum bounding rectangle and ellipse fitting of the spot regions, the method successfully obtains the four light spot characteristics. Subsequently, precise matching and stereo distance measurement of the target in the left and right images enable the determination of the boom-type roadheader’s posi-tion information. A positioning platform based on binocular vision is built and experimentally evaluated. The results demonstrate that this method can achieve accurate spot extraction and stable measurement of the machine’s position, even in complex backgrounds such as mixed lighting and low illumination. The planar measurement errors within a distance range of 50 m are all within ±25 mm, which basically meets the required construction precision for tunnel excavation.

The stable and accurate measurement of the position of tunneling machine in coal mines is crucial for enhancing intelligence and efficiency. To address the issue of inaccurate position measurements of binocular vision, an analysis was conducted to examine the accuracy of position measurements made by binocular vision, specifically by analyzing the imaging phenomena in visual sensors and the structural parameters of binocular vision. The study examines how the attitude parameters of a spatial circle impact the vision sensor’s plane projection error, establishes a circular feature projection error model, and conducts simulation analysis. Furthermore, a structural parameter analysis model for binocular vision sensors is proposed based on error analysis theory, and a simulated position measurement accuracy curve is provided along with criteria for selecting structural parameters. Additionally, a model is developed for measuring the accuracy of a vision system on a boom-type roadheader. The model comprehensively analyzes the impact of plane imaging errors and structural parameters on the accuracy of the body position visual measurement system. Finally, experiments are carried out to verify the accuracy and reliability of the proposed system and methods for measuring body position accuracy.

This paper proposes a temporary support system for improving the efficiency and safety of underground roadway excavation in coal mines. Firstly, this study establishes a calculation model for the initial supporting force of the excavation of roadway temporary support and a gray system-based automatic prediction model for the initial supporting force level, based on the mechanism of temporary support controlling the roof. These models enable the prediction of the required initial supporting force at different locations along the roadway’s temporary support area, thereby providing a basis for controlling the initial supporting force of the temporary support system. To achieve efficient and adaptive control of the initial supporting force of temporary supports at different locations, this study designs a support force controller based on Simulated Annealing Particle Swarm Optimization Proportional-Integral-Derivative (SAPSO-PID). This study establishes a mathematical model for the hydraulic cylinder pressure system controlled by the temporary support overflow valve and conducts a stability analysis and model verification. The study constructs a simulation control system for the initial supporting force based on SAPSO-PID using the combined simulation platform of AMESim and Matlab. The simulation results demonstrate that the proposed support force control system efficiently achieves adaptive control of the initial supporting force of temporary supports. An experimental system in the underground roadway of a coal mine is constructed to validate the results of the simulation analysis.

The coal mine’s underground heading roadway suffers from a lack of effective temporary support, which contributes to low efficiency and inadequate safety in excavation operations. This paper presents a novel approach for roadway excavation, support, and anchoring, utilizing the twin-link stepping temporary support method. To address the stability requirements of the roadway roof in this support method, a numerical model of the excavation face was developed using the BBM (Bonded Block Model) method in 3DEC 7.0 software. Through numerical simulation, the repeatability of the twin-link stepping temporary support process was assessed for various roadway roof rock types, thicknesses, and burial depths. By considering aspects such as roof fragmentation, roof convergence, and relative displacement between the direct roof and the bedrock, the range of roadway roof repeatability was determined. The proposed twin-link stepping temporary support process, along with its applicable roof conditions, establishes a theoretical foundation for implementing the parallel operation mode in coal mine underground heading roadways, thereby significantly enhancing safety and efficiency.

A positioning method for a roadheader based on fiber-optic strap-down inertial navigation and binocular vision is proposed to address the issue of low measurement accuracy of the mining machine position caused by single-sensor methods in underground coal mines. A vision system for the mining machine position is constructed based on the four-point target fixed on the body of the roadheader, and the position and attitude information of the roadheader are obtained by combining the inertial navigation on the body. To deal with the problem of position detection inaccuracies caused by the accumulation of errors in inertial navigation measurements over time and disturbances from body vibrations to the combined positioning system, an Adaptive Derivative Unscented Kalman Filtering (ADUKF) algorithm is proposed, which can suppress the impact of process variance uncertainties on the filtering. The simulation results demonstrate that, compared to the Unscented Kalman Filtering algorithm, the position errors in the three directions are reduced by 20%, 20.68%, and 28.57%, respectively. Experiments demonstrate that the method can compensate for the limitations of single-measurement methods and meet the positioning accuracy requirements for underground mining standards.

This paper introduces a control method tailored for the speed regulation of monorail cranes in coal mines. Initially, an analysis of the structure and load conditions of the monorail crane drive components is conducted to calculate the traction force, clamping force, and target travel speed across varying operational scenarios. Subsequently, the hydraulic system schematic of the monorail crane is analyzed to develop a mathematical model for speed control, enabling the assessment of system stability using transfer functions. A simulation model of the monorail crane speed control loop is then created in AMESim, where fuzzy adaptive PID controllers and MPC controllers are optimized in a collaborative simulation with Simulink. Experimental findings reveal that in a single acceleration condition, both controllers demonstrate superior dynamic response compared to a traditional PID controller, with the MPC controller exhibiting an overshoot of merely 8.9%. In speed variation conditions, the MPC controller achieved a settling time in the range of 0.26–0.3 s. Notably, the MPC controller displays a maximum overshoot of 11%, substantially enhancing the dynamic response performance of speed regulation in monorail cranes.