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

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

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 coll...

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Bibliographic Details
Main Authors: Mingyu Lei, Pingan Peng, Liguan Wang, Yongchun Liu, Ru Lei, Chaowei Zhang, Yongqing Zhang, Ya Liu
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Mathematics
Subjects:
load-haul-dump machine
3D point cloud
inclination sensors
oriented bounding box
collision detection
Online Access:https://www.mdpi.com/2227-7390/13/15/2359
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Summary: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.
ISSN:2227-7390

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