Mingyu Lei

A vast number of boreholes in underground mining operations are often plagued by deviation issues, which severely impact both production efficiency and safety. The accurate and rapid acquisition of borehole trajectories is fundamental for subsequent deviation control and correction. However, existing inclinometers are limited by their operational efficiency and estimation accuracy, making them inadequate for large-scale measurement demands. To address this, this paper proposes a novel method for the rapid and accurate reconstruction of underground mine borehole trajectories using a robotic system. We employ a custom-designed robot equipped with an Inertial Measurement Unit (IMU) and a displacement sensor, which travels stably while collecting real-time attitude and depth information. Algorithmically, a complementary filter is used to fuse data from the gyroscope with that from the accelerometer and magnetometer, overcoming both integration drift and environmental disturbances. A cubic spline interpolation algorithm is then utilized to time-register the low-sampling-rate displacement data with the high-frequency attitude data, creating a time-synchronized sequence of ‘attitude–displacement increment’ pairs. Finally, the 3D borehole trajectory is accurately reconstructed by mapping the attitude quaternions to direction vectors and recursively accumulating the displacement increments. Comparative experiments demonstrate that the proposed method significantly improves efficiency. On a complex trajectory, the maximum and mean errors were reduced to 0.38 m and 0.18 m, respectively. This level of accuracy is far superior to that of the conventional static point-by-point measurement mode and effectively suppresses the accumulation of dynamic errors. This work provides a new solution for routine borehole trajectory surveying in mining operations.

This study addresses collision detection in the unmanned loading of ore from load-haul-dump (LHD) machines into mining trucks in underground metal mines. Such environments present challenges like heavy dust, confined spaces, sensor occlusions, and poor lighting. This work identifies two primary collision risks and proposes corresponding detection strategies. First, for collisions between the bucket and tunnel walls, LiDAR is used to collect 3D point cloud data. The point cloud is processed through filtering, downsampling, clustering, and segmentation to isolate the bucket and tunnel wall. A KD-tree algorithm is then used to compute distances to assess collision risk. Second, for collisions between the bucket and the mining truck, a kinematic model of the LHD’s working device is established using the Denavit–Hartenberg (DH) method. Combined with inclination sensor data and geometric parameters, a formula is derived to calculate the pose of the bucket’s tip. Key points from the bucket and truck are then extracted to perform collision detection using the oriented bounding box (OBB) and the separating axis theorem (SAT). Simulation results confirm that the derived pose estimation formula yields a maximum error of 0.0252 m, and both collision detection algorithms demonstrate robust performance.