TY - JOUR
T1 - Truck loading state estimation for autonomous operation of mining excavator
AU - Yang, Xu
AU - Li, Yunhua
AU - Yao, Yu
AU - Qin, Tao
AU - Yang, Liman
AU - Sheng, Zhexuan
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/9
Y1 - 2025/9
N2 - The truck loading plays an important role in the open-pit mining industry, underload can reduce work efficiency, overload can affect truck life and even cause some safety issues. Therefore, how to accurately estimate the loading state (loading volume) of the dump truck has become an urgent problem to be solved. An intelligent truck loading volume estimation scheme is proposed to estimate the loading volume, which includes the point cloud extraction of ore pile in the truck and the its surface reconstruction. Firstly, the Euclidean clustering extraction method and color-based region growth segmentation algorithm are used to obtain a single and complete ore pile point cloud. Secondly, the Kriging interpolation method is adopted to reconstruct the ore pile surface, and then the loading volume is estimated based on the reconstructed surface. Finally, the experiments are conducted on the scale model of the electric shovel and dump truck, and experimental results show that the proposed scheme can accurately estimate the volume of truck loading, which contributes to improve efficiency and safety of the truck loading.
AB - The truck loading plays an important role in the open-pit mining industry, underload can reduce work efficiency, overload can affect truck life and even cause some safety issues. Therefore, how to accurately estimate the loading state (loading volume) of the dump truck has become an urgent problem to be solved. An intelligent truck loading volume estimation scheme is proposed to estimate the loading volume, which includes the point cloud extraction of ore pile in the truck and the its surface reconstruction. Firstly, the Euclidean clustering extraction method and color-based region growth segmentation algorithm are used to obtain a single and complete ore pile point cloud. Secondly, the Kriging interpolation method is adopted to reconstruct the ore pile surface, and then the loading volume is estimated based on the reconstructed surface. Finally, the experiments are conducted on the scale model of the electric shovel and dump truck, and experimental results show that the proposed scheme can accurately estimate the volume of truck loading, which contributes to improve efficiency and safety of the truck loading.
KW - Mining dump truck
KW - Mining excavator
KW - Point cloud segmentation
KW - Surface reconstruction
KW - Unloading and loading operations
UR - https://www.scopus.com/pages/publications/85218822968
U2 - 10.1007/s41315-024-00414-2
DO - 10.1007/s41315-024-00414-2
M3 - 文章
AN - SCOPUS:85218822968
SN - 2366-5971
VL - 9
SP - 1097
EP - 1108
JO - International Journal of Intelligent Robotics and Applications
JF - International Journal of Intelligent Robotics and Applications
IS - 3
ER -