Automated Stratum Interface Detection Using the Optimized Drilling Specific Energy through Self-Adaptive Logistic Function.

The precise detection of stratum interfaces holds significant importance in geological discontinuity recognition and roadway support optimization. In this study, the model for locating rock interfaces through change point detection was proposed, and a drilling test on composite strength mortar specimens was conducted. With the logistic function and the particle swarm optimization algorithm, the drilling specific energy was modulated to detect the stratum interface. The results indicate that the drilling specific energy after the modulation of the logistic function showed a good anti-interference quality under stable drilling and sensitivity under interface drilling, and its average recognition error was 2.83 mm, which was lower than the error of 6.56 mm before modulation. The particle swarm optimization algorithm facilitated the adaptive matching of drive parameters to drilling data features, yielding a substantial 50.88% decrease in the recognition error rate. This study contributes to enhancing the perception accuracy of stratum interfaces and eliminating the potential danger of roof collapse.

3D LiDAR Point Cloud Registration Based on IMU Preintegration in Coal Mine Roadways.

Point cloud registration is the basis of real-time environment perception for robots using 3D LiDAR and is also the key to robust simultaneous localization and mapping (SLAM) for robots. Because LiDAR point clouds are characterized by local sparseness and motion distortion, the point cloud features of coal mine roadway environments show a weak texture and degradation. Therefore, for these environments, the traditional point cloud registration method to register directly will lead to problems, such as a decline in registration accuracy, z-axis drift, and map ghosting. To solve the above problems, we propose a point cloud registration method based on IMU preintegration with the sensor characteristics of LiDAR and IMU. The system framework of this method mainly consists of four modules: IMU preintegration, point cloud preprocessing, point cloud frame matching and point cloud registration. First, IMU sensor data are introduced, and IMU linear interpolation is used to correct the motion distortion in LiDAR scanning, and the IMU preintegration error function is constructed. Second, the point cloud segmentation is performed using the ground segmentation method of RANSAC to provide additional ground constraints for the z-axis displacement and to remove the unstable flawed points from the point cloud. On this basis, the LiDAR point cloud registration error function is constructed by extracting the feature corner points and feature plane points. Finally, the Gaussian Newton solution is used to optimize the constraint relationship between the LiDAR odometry frames to minimize the error function, complete the LiDAR point cloud registration and better estimate the position and pose of the mobile robot. The experimental results show that compared with the traditional point cloud registration method, the proposed method has a higher point cloud registration accuracy, success rate and computational efficiency. The LiDAR odometry constructed using this method can better reflect the authenticity of the robot trajectory and has higher trajectory accuracy and smaller absolute position and pose error.

Simulation Experiment Research of Mine Roadway Simulating Test Device with Adjustable Wind Velocity and Temperature and Humidity.

The design and development process of wind velocity sensors for mining has been a challenging task due to the complexity of a large number of field tests. To resolve this problem, this study aimed to provide a comprehensive test device for the design and development of high-precision wind velocities sensor for mining. Through a combination of experiments and computational fluid dynamics (CFD), a device that can simulate the mine roadway environment was developed. The device can control the temperature, humidity, and wind velocity parameters to fully replicate the mine roadway environment. It gives designers and developers of high-precision wind velocity sensors for mining a rational and scientific testing environment. In order to quantitatively define the uniformity of air flow in the mine highway section, the research introduced the non-uniformity determination method. The approach was expanded to assess the cross-sectional uniformity of temperature and humidity. The wind velocity within the machine can increase to 8.5 m/s by selecting the right kind of fan. The minimum wind velocity non-uniformity at this moment is 2.30%. The device's internal temperature can be raised to 38.23 °C and its humidity level can be increased to 95.09% by carefully crafting the rectifier orifice plate's structure. At this time, the lowest temperature non-uniformity is 2.22%, and the lowest humidity non-uniformity is 2.40%. The device's average wind velocity is 4.37 m/s, its average temperature is 37.7 °C, as well as its average humidity is 95%, per the emulate results. The device's non-uniformity in wind velocity, temperature, and humidity is 2.89%, 1.34%, and 2.23%, respectively. It is capable of simulating the mine roadway environment in its entirety.