An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN DOI Creative Commons
Jiangang Yi, Peng Liu, Jun Gao

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 18, 2024

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 inference module control module. The primarily blast hole position detection network based on YOLOv8 SimAM-GRCNN. plans executes robot's motion output to achieve symmetric operations. Meanwhile, SimAM-GRCNN model is utilized carry out comparative evaluated Cornell Jacquard dataset, achieving accuracy 98.8% 95.2%, respectively. In addition, self-built reaches 96.4%. outperforms original GRCNN by average 1.7% in accuracy, balance between holes detection, speed. Finally, experiments are conducted Universal Robots 3 manipulator arm, using distributed deployment arm mode end-to-end process. On Jetson Xavier NX development board, time consumption 119.67 s, with success rates 87.1% 79.2% explosives.

Language: Английский

Parallel RepConv network: Efficient vineyard obstacle detection with adaptability to multi-illumination conditions DOI

Xuezhi Cui,

Licheng Zhu, Bo Zhao

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 230, P. 109901 - 109901

Published: Jan. 8, 2025

Language: Английский

Citations

0
Research status of apple picking robotic arm picking strategy and end-effector DOI
Chun‐Lin Chen, Z. Song, Xiangdong Li

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 235, P. 110349 - 110349

Published: April 5, 2025

Language: Английский

Citations

0
RGB-D Camera and Fractal-Geometry-Based Maximum Diameter Estimation Method of Apples for Robot Intelligent Selective Graded Harvesting DOI Creative Commons
Bin Yan,

Xiameng Li

Fractal and Fractional, Journal Year: 2024, Volume and Issue: 8(11), P. 649 - 649

Published: Nov. 7, 2024

Realizing the integration of intelligent fruit picking and grading for apple harvesting robots is an inevitable requirement future development smart agriculture precision agriculture. Therefore, maximum diameter estimation model based on RGB-D camera fusion depth information was proposed in study. Firstly, parameters Red Fuji apples were collected, results statistically analyzed. Then, Intel RealSense D435 LabelImg software, two-dimensional size images obtained. Furthermore, relationship between information, images, explored. Based Origin multiple regression analysis nonlinear surface fitting used to analyze correlation depth, diagonal length bounding rectangle, diameter. A estimating constructed. Finally, constructed experimentally validated evaluated imitation laboratory fruits trees modern orchards. The experimental showed that average relative error validation set ±4.1%, coefficient (R2) estimated 0.98613, root mean square (RMSE) 3.21 mm. orchard ±3.77%, 0.84, 3.95 can provide theoretical basis technical support selective apple-picking operation grading.

Language: Английский

Citations

1
An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN DOI Creative Commons
Jiangang Yi, Peng Liu, Jun Gao

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 18, 2024

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 inference module control module. The primarily blast hole position detection network based on YOLOv8 SimAM-GRCNN. plans executes robot's motion output to achieve symmetric operations. Meanwhile, SimAM-GRCNN model is utilized carry out comparative evaluated Cornell Jacquard dataset, achieving accuracy 98.8% 95.2%, respectively. In addition, self-built reaches 96.4%. outperforms original GRCNN by average 1.7% in accuracy, balance between holes detection, speed. Finally, experiments are conducted Universal Robots 3 manipulator arm, using distributed deployment arm mode end-to-end process. On Jetson Xavier NX development board, time consumption 119.67 s, with success rates 87.1% 79.2% explosives.

Language: Английский

Citations

0