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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/23/11036 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850061412319100928 |
|---|---|
| author | Hanwen Zhang Sun Jin Bing Li Bo Xu Yuanbin Xiao Weixin Zhou |
| author_facet | Hanwen Zhang Sun Jin Bing Li Bo Xu Yuanbin Xiao Weixin Zhou |
| author_sort | Hanwen Zhang |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-c51475403d7249fa950768b7889206aa |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c51475403d7249fa950768b7889206aa2025-08-20T02:50:15ZengMDPI AGApplied Sciences2076-34172024-11-0114231103610.3390/app142311036Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel PilesHanwen Zhang0Sun Jin1Bing Li2Bo Xu3Yuanbin Xiao4Weixin Zhou5School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaGravel 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%.https://www.mdpi.com/2076-3417/14/23/11036gravel pileunmanned loaderdiscrete element co-simulationshovel positionlightweight network model |
| spellingShingle | Hanwen Zhang Sun Jin Bing Li Bo Xu Yuanbin Xiao Weixin Zhou Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles Applied Sciences gravel pile unmanned loader discrete element co-simulation shovel position lightweight network model |
| title | Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles |
| title_full | Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles |
| title_fullStr | Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles |
| title_full_unstemmed | Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles |
| title_short | Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles |
| title_sort | research on shoveling position analysis and recognition of unmanned loaders for gravel piles |
| topic | gravel pile unmanned loader discrete element co-simulation shovel position lightweight network model |
| url | https://www.mdpi.com/2076-3417/14/23/11036 |
| work_keys_str_mv | AT hanwenzhang researchonshovelingpositionanalysisandrecognitionofunmannedloadersforgravelpiles AT sunjin researchonshovelingpositionanalysisandrecognitionofunmannedloadersforgravelpiles AT bingli researchonshovelingpositionanalysisandrecognitionofunmannedloadersforgravelpiles AT boxu researchonshovelingpositionanalysisandrecognitionofunmannedloadersforgravelpiles AT yuanbinxiao researchonshovelingpositionanalysisandrecognitionofunmannedloadersforgravelpiles AT weixinzhou researchonshovelingpositionanalysisandrecognitionofunmannedloadersforgravelpiles |