Collision Detection Algorithms for Autonomous Loading Operations of LHD-Truck Systems in Unstructured Underground Mining Environments

Mingyu Lei, Pingan Peng (), Liguan Wang, Yongchun Liu, Ru Lei, Chaowei Zhang, Yongqing Zhang and Ya Liu
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Mingyu Lei: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Pingan Peng: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Liguan Wang: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Yongchun Liu: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Ru Lei: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Chaowei Zhang: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Yongqing Zhang: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Ya Liu: School of Resources and Safety Engineering, Central South University, Changsha 410083, China

Mathematics, 2025, vol. 13, issue 15, 1-28

Abstract: 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.

Keywords: load-haul-dump machine; 3D point cloud; inclination sensors; oriented bounding box; collision detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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