Home Journals Articles Updates About
CN
image
收藏切换
Research on robotic arm grasping detection technology based on target recognition and FC-GQCNN network
收藏切换
PDF
Yangfan BAI1, Yongming BIAN1, Jixiang YANG2, Meng YANG1
Chinese Journal of Construction Machinery | 2025, 23(2) : 227 - 232
Less
收藏切换
Chinese Journal of Construction Machinery | 2025, 23(2): 227-232
• Basic Theory and Key Technique •
Research on robotic arm grasping detection technology based on target recognition and FC-GQCNN network
Full
Yangfan BAI1, Yongming BIAN1, Jixiang YANG2, Meng YANG1
Affiliations
  • 1. School of Mechanical Engineering, Tongji University, Shanghai 201804, China
  • 2. School of Robotics, Ningbo University of Technology, Ningbo 315211, Zhejiang, China
Outline
收藏切换

This paper proposes a robotic grasping technique based on object recognition and fully convolutional grasp quality convolutional neural network (FC-GQCNN). To address the limitations of traditional GQCNN, such as low computational efficiency and redundant feature calculations, an improved FC-GQCNN is developed. By replacing the fully connected layers in GQCNN with 1×1 convolutional layers, the proposed network can handle input images of arbitrary sizes. Furthermore, the integration of FC-GQCNN with the YOLOv8 object detection algorithm forms a YOLOv8-FCGQCNN cascade structure, effectively solving the challenges of object recognition and localization in complex environments. Experimental results demonstrate that this method achieves an 86% grasp success rate across 10 different objects, with an average detection time of 0.09 s per frame, which is 22 times faster than traditional GQCNN, significantly improving system efficiency. This method can accurately detect the grasping position of the object of interest and has higher reliability than the baseline method.

object recognition  /  grasp pose estimation  /  robotic arm grasping system  /  algorithm integration
Yangfan BAI, Yongming BIAN, Jixiang YANG, Meng YANG. Research on robotic arm grasping detection technology based on target recognition and FC-GQCNN network[J]. Chinese Journal of Construction Machinery, 2025 , 23 (2) : 227 -232 .
Appendix
Less
Year 2025 volume 23 Issue 2
PDF
101
56
Cite this Article
BibTeX
Article Info
  • Online Date：2025-12-16
Article Data
Affiliations
History
Funding
Affiliations
    1. School of Mechanical Engineering, Tongji University, Shanghai 201804, China
    2. School of Robotics, Ningbo University of Technology, Ningbo 315211, Zhejiang, China
References
Share
https://castjournals.cast.org.cn/joweb/zggcjxxb/EN/
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House