An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN
Abstract For the blasting scenario, our research develops an emulsion explosive grasping and filling system suitable for tunnel robots. Firstly, we designed a system, YOLO-SimAM-GRCNN, which consists of an inference module and a control module. The inference module primarily consists of a blast hole...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-77034-0 |
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| _version_ | 1846158563245817856 |
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| author | Jiangang Yi Peng Liu Jun Gao Rui Yuan Jiajun Wu |
| author_facet | Jiangang Yi Peng Liu Jun Gao Rui Yuan Jiajun Wu |
| author_sort | Jiangang Yi |
| collection | DOAJ |
| description | Abstract For the blasting scenario, our research develops an emulsion explosive grasping and filling system suitable for tunnel robots. Firstly, we designed a system, YOLO-SimAM-GRCNN, which consists of an inference module and a control module. The inference module primarily consists of a blast hole position detection network based on YOLOv8 and an explosive grasping network based on SimAM-GRCNN. The control module plans and executes the robot’s motion control based on the output of the inference module to achieve symmetric grasping and filling operations. Meanwhile, The SimAM-GRCNN grasping network model is utilized to carry out comparative evaluated on the Cornell and Jacquard dataset, achieving a grasping detection accuracy of 98.8% and 95.2%, respectively. In addition, on a self-built emulsion explosive dataset, the grasping detection accuracy reaches 96.4%. The SimAM-GRCNN grasping network model outperforms the original GRCNN by an average of 1.7% in accuracy, achieving a balance between blast holes detection, grasping accuracy and filling speed. Finally, experiments are conducted on the Universal Robots 3 manipulator arm, using distributed deployment and manipulator arm motion control mode to achieve an end-to-end grasping and filling process. On the Jetson Xavier NX development board, the average time consumption is 119.67 s, with average success rates of 87.1% for grasping and 79.2% for filling emulsion explosives. |
| format | Article |
| id | doaj-art-edb792314682467db2a7c443da03ae3f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-edb792314682467db2a7c443da03ae3f2024-11-24T12:24:58ZengNature PortfolioScientific Reports2045-23222024-11-0114112110.1038/s41598-024-77034-0An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNNJiangang Yi0Peng Liu1Jun Gao2Rui Yuan3Jiajun Wu4State Key Laboratory of Precision Blasting, Jianghan UniversityState Key Laboratory of Precision Blasting, Jianghan UniversityState Key Laboratory of Precision Blasting, Jianghan UniversityState Key Laboratory of Precision Blasting, Jianghan UniversitySchool of Smart Manufacturing, Jianghan UniversityAbstract For the blasting scenario, our research develops an emulsion explosive grasping and filling system suitable for tunnel robots. Firstly, we designed a system, YOLO-SimAM-GRCNN, which consists of an inference module and a control module. The inference module primarily consists of a blast hole position detection network based on YOLOv8 and an explosive grasping network based on SimAM-GRCNN. The control module plans and executes the robot’s motion control based on the output of the inference module to achieve symmetric grasping and filling operations. Meanwhile, The SimAM-GRCNN grasping network model is utilized to carry out comparative evaluated on the Cornell and Jacquard dataset, achieving a grasping detection accuracy of 98.8% and 95.2%, respectively. In addition, on a self-built emulsion explosive dataset, the grasping detection accuracy reaches 96.4%. The SimAM-GRCNN grasping network model outperforms the original GRCNN by an average of 1.7% in accuracy, achieving a balance between blast holes detection, grasping accuracy and filling speed. Finally, experiments are conducted on the Universal Robots 3 manipulator arm, using distributed deployment and manipulator arm motion control mode to achieve an end-to-end grasping and filling process. On the Jetson Xavier NX development board, the average time consumption is 119.67 s, with average success rates of 87.1% for grasping and 79.2% for filling emulsion explosives.https://doi.org/10.1038/s41598-024-77034-0 |
| spellingShingle | Jiangang Yi Peng Liu Jun Gao Rui Yuan Jiajun Wu An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN Scientific Reports |
| title | An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN |
| title_full | An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN |
| title_fullStr | An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN |
| title_full_unstemmed | An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN |
| title_short | An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN |
| title_sort | intelligent emulsion explosive grasping and filling system based on yolo simam grcnn |
| url | https://doi.org/10.1038/s41598-024-77034-0 |
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