Computer-vision-based separation methods for coal gangue face challenges due to the harsh environmental conditions in the mines, leading to the reduction of separation accuracy. So, rather than purely depending on the image features to distinguish the coal gangue, it is meaningful to utilize fixed coal characteristics like density. This study achieves the classification of coal and gangue based on their mass, volume, and weight. A dataset of volume, weight and 3_side images is collected. By using 3_side images of coal gangue, the visual perception value of the volume is extracted ($ExM$) to represent the volume of the object. A Support Vector Machine (SVM) classifier receives ($ExM$) and the weight to perform the coal gangue classification. The proposed system eliminates computer vision problems like light intensity, dust, and heterogeneous coal sources. The proposed model was tested with a collected dataset and achieved high recognition accuracy (KNN 100%, Linear SVM 100%, RBF SVM 100%, Gaussian Process 100%, Decision Tree 98%, Random Forest 100%, MLP 100%, AdaBosst 100%, Naive Bayes 98%, and QDA 99%). A cross-validation test has been done to verify the generalization ability. The results also demonstrate high classification accuracy (KNN 96%, Linear SVM 100%, RBF SVM 96%, Gaussian Process 96%, Decision Tree 99%, Random Forest 99%, MLP 100%, AdaBosst 99%, Naive Bayes 99%, and QDA 99%). The results show the high ability of the proposed technique $ExM$-SVM in coal gangue classification tasks.

Mobile robots in complex underground coal mine environments are still unable to achieve accurate pose estimation and the real-time reconstruction of scenes with absolute geographic information. Integrated terrestrial-underground localization and mapping technologies have still not been effectively developed. This paper proposes a multimodal robust SLAM method based on wireless beacon-assisted geographic information transmission and lidar-IMU-UWB elastic fusion mechanism (LIU-SLAM). In order to obtain the pose estimation and scene models consistent with the geographic information, the construction of two kinds of absolute geographic information constraints based on UWB beacons is proposed. An elastic multimodal fusion state estimation mechanism is designed based on incremental factor graph optimization. A tightly coupled lidar-inertial odometry is firstly designed to construct the lidar-inertial local transformation constraints, which are further integrated with the absolute geographic constraints by UWB anchors through a loosely coupled approach. Extensive field tests based on coal mine robots have been conducted in scenarios such as underground garages and underground coal mine laneways. The results show that the proposed geodesic-coordinate driven multimode robust SLAM method can obtain absolute localization accuracy within 25 cm with practical robustness and real-time performance in different underground application scenarios. The wireless beacon-assisted geodesic-coordinate transmission strategy can provide a plug-and-play customized solution for precise positioning and scene modeling in complex scenarios of various coal mine robot application.

In order to find out the main causes of coal mine safety accidents and improve the pertinence of coal mine safety risk management and control, the identification and analysis of coal mine safety risks and hidden dangers are carried out based on the analysis of coal mine accident reports. Combing the complex network theory, a complex network model for the evolution of coal mine safety risks is constructed. The key elements that affect coal mine safety risk accidents are obtained through quantitative research on the characteristic indicators of the complex network model of coal mine safety risks. And the key nodes of coal mine safety risk spread network are obtained through network interference to the overall efficiency. The research results show that the complex network of coal mine safety risks illustrate the characteristics of a small-world network, and the spread of a certain risk is likely to cause coal mine safety accidents. Strengthening the risk management and control of hidden dangers with higher intermediate centrality can isolate the spread of coal mine safety risks and reduce the possibility of coal mine accidents.

In order to realize the scientific evaluation of coal mine safety risks, the coal mine safety accident report is used as the data sample, and grounded theory is used to explore the causes of coal mine safety risks. At the same time, combined with the existing research results, a coal mine safety risk evaluation index system is constructed. Monte Carlo Simulation (MCS) is used to optimize the analytic hierarchy process (AHP) to determine the index weights. Then, a coal mine safety risk assessment model based on the AHP-MCS coupling technique for order preference by similarity to ideal solution (TOPSIS) is established by combining TOPSIS. Taking Lingquan Coal Mine as an example, the applicability of the model is verified. The research results show that the coal mine safety risk level is orange risk, which is consistent with the actual situation. It also shows the applicability of the model in the field of coal mine safety evaluation and expands new theories for coal mine safety risk evaluation research.

Coal Mine Mobile Robots (CMRRs) are generally large in size and inertia, while narrow laneway space and bumpy terrain pose great challenges to CMRR’s planning and control. Aiming at the trajectory planning and tracking problems of CMRR, a new trajectory class MINCO is derived in detail based on the properties of differential flat systems. A trajectory planning method based on MINCO trajectory and safety corridor constraints constructed with underground environmental constraints is further proposed. A trajectory tracking method based on model predictive control (MPC) is further proposed. The prediction model of MPC is constructed by a kinematics model and transformed into a standard quadratic programming problem according to the cost function of a trajectory tracking target. Finally, large quantities of field tests were carried out for the proposed approaches. The results show that the proposed planning algorithm based on MINCO trajectory can achieve good avoidance effects within 10 planning attempts in different obstacle scenarios, and the trajectory is smoother compared to the Fast-Planner algorithm, with shorter trajectory length and less planning time. The tracking error of MPC is always less than 0.05 m in different underground scenarios, having a more adaptable trajectory tracking effect than PID.

Understanding the evolution of coal pore–fracture structures under coupled stress paths and creep deformation is critical for enhancing coalbed methane extraction and preventing coal and gas outbursts. In this study, coal samples from the Ningtiaota Mine were investigated using online Nuclear Magnetic Resonance (NMR) technology combined with triaxial loading–creep coupled experiments. The dynamic evolution of pore–fracture structures (PFSs) under different deviatoric stress levels was characterized and visualized in real time and across multiple scales. The results reveal a pronounced stress-dependent pore evolution during creep. Under low-stress conditions, seepage pores were compressed and gradually transformed into adsorption pores, whereas under high-stress conditions, seepage pores expanded and interconnected, dominating deformation and failure. Fractal theory was employed to quantify pore structure complexity, and repeated experiments demonstrated a significant positive correlation between the fractal dimension and the fractional order. Based on these findings, a fractal-dimension-based fractional creep model was developed by introducing a Riemann–Liouville fractional dashpot. The proposed model accurately captures the nonlinear creep behavior of coal and provides a microstructural interpretation of the fractional order. This study provides theoretical and experimental support for long-term stability assessment of deep coal–rock masses and prediction of coalbed methane migration.