A grasping posture estimation method based on 3D detection network

Ge, Junyan; Shi, Jinlong; Zhou, Zhiqiang; Wang, Zhi; Qian, Qiang

doi:10.1016/j.compeleceng.2022.107896

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…These algorithms can provide real-time feedback on the quality of exercise execution by analyzing the posture and movements of athletes. These algorithms can help coaches and athletes optimize training methods and improve athletic performance [1]. Traditional analysis of sports movements mainly relies on observing coaches and the subjective feelings of athletes.…”

Section: Introductionmentioning

confidence: 99%

Sports Action Detection and Counting Algorithm Based on Pose Estimation and Its Application in Physical Education Teaching

Song,

Chen

2024

IJCAI

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Section: Introductionmentioning

confidence: 99%

Sports Action Detection and Counting Algorithm Based on Pose Estimation and Its Application in Physical Education Teaching

Song,

Chen

2024

IJCAI

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An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN

Yi,

Liu,

Gao

et al. 2024

Sci Rep

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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.

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EPM-Net: Efficient Feature Extraction, Point-Pair Feature Matching for Robust 6-D Pose Estimation

Zhang,

Shi,

Zou

et al. 2024

IEEE Trans. Multimedia

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A grasping posture estimation method based on 3D detection network

Cited by 4 publications

References 9 publications

Sports Action Detection and Counting Algorithm Based on Pose Estimation and Its Application in Physical Education Teaching

Sports Action Detection and Counting Algorithm Based on Pose Estimation and Its Application in Physical Education Teaching

An intelligent emulsion explosive grasping and filling system based on YOLO-SimAM-GRCNN

EPM-Net: Efficient Feature Extraction, Point-Pair Feature Matching for Robust 6-D Pose Estimation

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