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Online First have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
Analysis of wireless transmission tests in mines and preferred working frequency bands for mining 5G
2024, 50(10): 1-11, 20.
doi: 10.13272/j.issn.1671-251x.18221
Abstract: The development and deployment of mobile communication systems, personnel and vehicle positioning systems in mines require an analysis of wireless transmission characteristics, the selection of preferred working frequency bands, and the optimization of wireless communication base stations and positioning substations. In this study, wireless transmission tests were conducted in a large frequency range from 350 MHz to 6 GHz in mine environments such as curved tunnels, branch tunnels, main transportation tunnels, excavation tunnels, and fully mechanized mining faces. The test results were analyzed, revealing the characteristics of wireless transmission in mines: ① In curved tunnels, the lower the wireless transmission frequency, the smaller the attenuation, with the least attenuation in the 350 MHz to 900 MHz frequency band. ② In branch tunnels, the lower the frequency, the smaller the attenuation, with the least attenuation in the 350 MHz to 900 MHz frequency band. ③ In main transportation tunnels, the least wireless transmission attenuation was found in the 700 MHz to 900 MHz frequency band. ④ In excavation tunnels, the least attenuation was in the 700 MHz to 900 MHz frequency band. ⑤ In fully mechanized mining faces, the least attenuation was observed in the 433 MHz to 1 300 MHz frequency band. ⑥ With the same cross-sectional area of the tunnels, wireless transmission attenuation in curved tunnels was smaller than in branch tunnels, and the attenuation in branch tunnels emitted from branch sources was smaller than that emitted from main tunnels. Curves and branches in tunnels increased wireless transmission attenuation. Furthermore, this paper proposed the preferred working frequency bands and the best arrangement of antennas for wireless communication systems in underground coal mines, specifically in curved and branch tunnels: ① The working frequency bands for underground wireless communication systems should preferably be in the 700 MHz to 900 MHz range. ② To minimize the impact of curves and branches in tunnels on wireless transmission, wireless communication base stations, positioning substations, and their antennas should be set at the turning points of curved tunnels and at the branch points of branch tunnels. The research results have been applied to the People's Republic of China energy industry standards NB/T 11546-2024 General specification of 5G communication system for coal mines, NB/T 11523-2024 5G communication base station for coal mines, and NB/T 11547-2024 5G communication baseband controller for coal mines.
2024, 50(10): 12-20.
doi: 10.13272/j.issn.1671-251x.2024070048
Abstract: To address the low path planning efficiency of mining vehicles in narrow, winding underground tunnels with unknown obstacles, a global path planning algorithm, DVGA*, was proposed, integrating simplified visibility graphs (SVG) and the A* algorithm. Based on the construction of a point cloud map of the real environment, the algorithm connected the vehicle's visual tangent points from different viewpoints to dynamically generate the SVG. The visual tangent points were sequentially stored in the OPEN list as nodes, and nodes were selected for the CLOSED list based on the A* algorithm's evaluation function to ensure the shortest path. This process continued until the endpoint appeared in the OPEN list, resulting in the optimal path points being stored while the remaining nodes in the OPEN list were deleted. Finally, a path smoothing algorithm was utilized to further reduce the number of path nodes, thereby enhancing path planning efficiency. Experimental results indicated that compared to the Complete Visibility Graph + A* algorithm, SVG + A* algorithm, and SVGCA* algorithm, the DVGA* algorithm had the shortest planning time for complex long-distance paths, with average path lengths reduced by 10.79%, 6.26%, and 2.86%, respectively, demonstrating stronger adaptability and higher planning success rates. Results from underground tests showed that in areas with variable tunnel widths and while avoiding static obstacles, the path planned by DVGA* was smoother compared to that of the SVGCA* algorithm. When avoiding dynamic obstacles, DVGA* was able to promptly correct the path, ensuring timely and stable path planning. In complex and variable tunnel environments, the planning time and path length of DVGA* were reduced by 11.51% and 1.54%, respectively, compared to SVGCA*, indicating higher environmental adaptability and stability.
2024, 50(10): 21-28.
doi: 10.13272/j.issn.1671-251x.18209
Abstract: Simultaneous localization and mapping (SLAM) is a critical technology for unmanned driving. Existing SLAM methods have the drawbacks of significant cumulative errors and drift in coal mine roadway environment. In this study, a roadway environment feature-assisted SLAM algorithm integrating inertial measurement unit (IMU) and LiDAR was proposed. IMU observation data was used to predict the motion state of point cloud and motion compensation was applied to reduce point cloud distortion caused by equipment movement. Pose transformation information from LiDAR odometry was obtained through point cloud registration, forming a LiDAR odometry constraint. Point clouds from roadway sidewalls and floor were extracted and fitted to planes, establishing environmental constraints. Using IMU pre-integration constraints, LiDAR odometry constraints, and environmental constraints, the algorithm applied factor graph optimization to achieve tight coupling between LiDAR and IMU, enabling high-precision 3D reconstruction of roadway scenes and accurate localization of autonomous vehicles. Simulation experiments showed that the absolute trajectory root mean square error (RMSE) of the roadway environment feature-assisted IMU-LiDAR integrated SLAM algorithm was 0.1162 m, and the relative trajectory RMSE was 0.0409 m, improving positioning accuracy compared to commonly used algorithms such as LeGO-LOAM and LIO-SAM. Based on the test results in a real environment, the algorithm provides excellent mapping performance with no drift or trailing, demonstrating strong environmental adaptability and robustness.
2024, 50(10): 29-37.
doi: 10.13272/j.issn.1671-251x.2024050058
Abstract: The working environment of mining unmanned vehicles features complex lighting conditions, leading to frequent occurrences of missed detections in pedestrian detection, which undermines the reliability and safety of these vehicles. To address the challenges posed by intricate tunnel lighting conditions, a low-light image enhancement algorithm was proposed. This algorithm decomposed low-light images from the RGB color space into the HSV color space, applied a Logarithm function to enhance the V component, and employed a bilateral filter to reduce noise. Morphological operations were applied to the S component for closing, followed by Gaussian filtering to further eliminate noise. The enhanced image was then transformed back into the RGB color space and subjected to a semi-implicit ROF denoising model for additional noise reduction, resulting in an enhanced image. To tackle issues of missed detections and low accuracy in pedestrian detection, an improved YOLOv3-based pedestrian detection algorithm for mining unmanned vehicles was introduced. This approach replaced the Residual connections in YOLOv3 with densely connected modules to enhance feature map utilization. Additionally, a Slim-neck structure optimized the feature fusion architecture of YOLOv3, facilitating efficient information fusion between feature maps and further improving the detection accuracy for small-target pedestrians, while its unique lightweight convolutional structure enhanced detection speed. Finally, a lightweight convolutional block attention module (CBAM) was integrated to improve attention to object categories and locations, thereby enhancing pedestrian detection accuracy. Experimental results demonstrated that the proposed low-light image enhancement algorithm effectively improved image visibility, making pedestrian textures clearer and achieving better noise suppression. The average precision of the pedestrian detection algorithm for mining unmanned vehicles based on enhanced images reached 95.68%, representing improvements of 2.53%, 6.42%, and 11.77% over YOLOv5, YOLOv3, and a coal mine key position personnel unsafe behavior recognition method based on improved YOLOv7 and ByteTrack, respectively, with a runtime of 29.31 ms. YOLOv3 and a coal mine key position personnel unsafe behavior recognition method based on improved YOLOv7 and ByteTrack experienced missed detections and false positives based on enhanced images, while the proposed pedestrian detection algorithm effectively mitigated these issues.
2024, 50(10): 38-52.
doi: 10.13272/j.issn.1671-251x.2024070010
Abstract: Autonomous driving is identified as one of the key technologies for mining intelligence, with simultaneous localization and mapping (SLAM) technology serving as a key link to realize autonomous driving. To advance the development of SLAM technology in autonomous mining, this paper discusses the principles of SLAM technology, mature ground SLAM solutions, the current research status of mining SLAM, and future development trends. Based on the sensors employed in SLAM technology, the study analyzes the technical principles and corresponding frameworks from three aspects: vision, laser, and multi-sensor fusion. It is noted that visual and laser SLAM technologies, which utilize single cameras or LiDAR, are susceptible to environmental interference and cannot adapt to complex environments. Multi-sensor fusion SLAM emerges as the most effective solution. The research examines the status of mining SLAM technology, analyzing the applicability and research value of visual, laser, and multi-sensor fusion SLAM technologies in underground coal mines and open-pit mines. It concludes that multi-sensor fusion SLAM represents the optimal research approach for underground coal mines, while the research value of SLAM technology in open-pit mines is limited. Based on the challenges identified in underground SLAM technology, such as accumulated errors over time and activity range, adverse effects from various scenes, and the inadequacy of various sensors to meet the hardware requirements for high-precision SLAM algorithms, it is proposed that future developments in SLAM technology for autonomous mining should focus on multi-sensor fusion, solid-state solutions, and intelligent development.
2024, 50(10): 53-61, 89.
doi: 10.13272/j.issn.1671-251x.2024070056
Abstract: The realization of autonomous driving for mining vehicles relies on accurate environmental perception, and the combination of LiDAR and cameras can provide richer and more accurate environmental information. To ensure effective fusion of LiDAR and cameras, external parameter calibration is necessary. Currently, most mining intrinsically safe onboard LiDARs are 16-line LiDARs, which generate relatively sparse point clouds. To address this issue, this paper proposed a targetless automatic calibration method for mining LiDAR and camera. Multi-frame point cloud fusion was utilized to obtain fused frame point clouds, increasing point cloud density and enriching point cloud information. Then, effective targets such as vehicles and traffic signs in the scene were extracted using panoramic segmentation. By establishing a corresponding relationship between the centroids of 2D and 3D effective targets, a coarse calibration was completed. In the fine calibration process, the effective target point clouds were projected onto the segmentation mask after inverse distance transformation using the coarse-calibrated external parameters, constructing an objective function based on the matching degree of effective target panoramic information. The optimal external parameters were obtained by maximizing the objective function using a particle swarm algorithm. The effectiveness of the method was validated from three aspects: quantitative, qualitative, and ablation experiments. ① In the quantitative experiments, the translation error was 0.055 m, and the rotation error was 0.394°. Compared with the method based on semantic segmentation technology, the translation error was reduced by 43.88%, and the rotation error was reduced by 48.63%. ② The qualitative results showed that the projection effects in the garage and mining area scenes were highly consistent with the true values of the external parameters, demonstrating the stability of the method. ③ Ablation experiments indicated that multi-frame point cloud fusion and the weight coefficients of the objective function significantly improved calibration accuracy. When using fused frame point clouds as input compared to single-frame point clouds, the translation error was reduced by 50.89%, and the rotation error was reduced by 53.76%. Considering the weight coefficients, the translation error was reduced by 36.05%, and the rotation error was reduced by 37.87%.
2024, 50(10): 62-67, 79.
doi: 10.13272/j.issn.1671-251x.2024050067
Abstract: Identifying passable areas is a crucial aspect of autonomous driving technology in mining. Open-pit mining road scenes are characterized by unclear road boundaries and varying surface flatness. When using traditional concentric circle ground segmentation models for fitting mining road planes, misclassification issues often arise, such as disconnection between passable areas and vehicles, and inconsistencies in passable area recognition results across frames. This paper proposed a method for identifying passable areas in mining roads based on spatiotemporal continuous compensation. First, the mining road was modeled using a concentric circle model, and principal component analysis was applied for multi-plane fitting to obtain the initial segmentation results of passable areas. Next, based on spatial connectivity, regional connectivity filtering and point connectivity filtering were performed on the initial passable areas using the region-growing algorithm and density-based spatial clustering of applications with noise algorithm, respectively, to obtain passable areas that meet spatial connectivity criteria. Finally, to eliminate unstable regions with inconsistent passability across different point cloud frames, a grid map was constructed based on a normal distribution transformation algorithm, and temporal stability weights were used to assess grid stability, ultimately filtering out unstable regions through regional grid projection. Test results in mining indicated that the proposed method for identifying passable areas achieved an accuracy of 93.44%, representing a 2.27% improvement over existing mainstream algorithms; the recall rate was 99.14%, reflecting an 8.26% enhancement compared to current mainstream algorithms. The proposed method not only exhibits good spatial connectivity in disconnected areas but also demonstrates strong temporal stability in rugged regions.
2024, 50(10): 68-79.
doi: 10.13272/j.issn.1671-251x.2024070017
Abstract: Open-pit mine unmanned dump trucks face harsh transportation conditions, such as low-grade roads with numerous ramps and curves, as well as heavy and highly variable loads. Most existing vehicle motion control strategies are designed for conventional road environments, making them unsuitable for direct application to mine dump trucks. To address these issues, a lateral-longitudinal coordinated control system based on preview error and layered feedback was proposed for unmanned open-pit mine dump trucks. The lateral control was based on a linear quadratic regulator (LQR) and employed a feedforward controller to reduce steady-state errors, while a fuzzy controller was used to adaptively adjust the preview distance, thereby improving path tracking accuracy. The longitudinal control established a layered feedback longitudinal speed controller, which used model predictive control and fuzzy proportional-integral-differential (PID) feedback control. In addition, an inverse model for vehicle driving and braking was established to minimize the impact of load and road gradient changes on longitudinal speed tracking. Simulation results indicated that: ① The error between the actual speed and the desired speed was within 2%, demonstrating that the speed tracking performance of the dump truck could meet requirements under both empty downhill and fully loaded uphill conditions. ② Due to the lateral-longitudinal coordinated control’s ability to adjust vehicle speed in real time based on varying road curvature, the coordinated controller achieved higher path tracking accuracy compared to single lateral control in both operating conditions, while also enhancing vehicle maneuverability and stability. Laboratory test results showed that: ① The peak lateral error during empty downhill runs was 0.0199 m, and the peak direction error was 0.1840 rad. Both errors increased at curves, but their fluctuations were minimal, ensuring that the test vehicle effectively tracked the desired path. ② During loaded uphill runs, the peak lateral error was 0.0168 m, and the peak direction error was 0.0714 rad. The error trends were opposite to those observed in empty downhill tests, but the errors remained within acceptable limits, resulting in good path tracking performance. ③ Both peak errors were lower compared to those in empty downhill tests, which validated the effect of varying speeds on lateral control accuracy.
