Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles

Gravel is the most frequently used material in infrastructure construction. However, the irregular shape of the gravel pile makes it challenging for the loader to predict a stable shoveling position, which can easily result in partial collapse or a complete landslide, thereby posing a serious threat...

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Bibliographic Details
Published in:Applied sciences 2024-12, Vol.14 (23), p.11036
Main Authors: Zhang, Hanwen, Jin, Sun, Li, Bing, Xu, Bo, Xiao, Yuanbin, Zhou, Weixin
Format: Article
Language:English
Subjects:
Deep learning
discrete element co-simulation
Efficiency
Energy consumption
Engineering
gravel pile
Kinematics
Lasers
lightweight network model
Radar systems
Sensors
shovel position
Simulation
unmanned loader
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Summary:Gravel is the most frequently used material in infrastructure construction. However, the irregular shape of the gravel pile makes it challenging for the loader to predict a stable shoveling position, which can easily result in partial collapse or a complete landslide, thereby posing a serious threat to the equipment. In view of the imperfect method of determining the shoveling position of the pile by the current unmanned loader and the high hardware requirements for the deployment of the identification model, this paper first establishes a mathematical model of the loader, and preliminarily determines the influence of the concave and convex edges of the gravel pile on the shoveling position selection through discrete element joint simulation; secondly, the influence of the pile with different edge curvatures on the loader operation process is analyzed in the simulation software, and the radar map is used to further identify the superior position features; finally, Ghost Net is used as the backbone network, the RFB module is introduced into the Backbone, and the CBAM attention mechanism is integrated into the C3 module to identify the lightweight YOLOv5s shoveling position. Discrete element analysis and a lightweight network model were used in the above study to find the safest and most effective shoveling positions. During the test that mimicked how the loader would actually shovel, the number of parameters in the improved model was cut down to 32.5% of the original, the number of calculations was cut down to about 55.2% of the original, and the average accuracy of finding the shoveling position of the gravel pile reached 98%.
ISSN:2076-3417
2076-3417
DOI:10.3390/app142311036