Research on Robotic Arm Recognition Grasping Method Based on Improved GGCNN Algorithm Network Architecture: An Enhanced Approach for Object Grasping by Robotic Arms Using GGCNN Algorithm Network Architecture
Authors: 
Hui Qi, Shengsheng LiAuthors Info & Claims
Hui Qi, Shengsheng LiAuthors Info & ClaimsPages 1119 - 1124
Abstract
This paper describes the importance of robotic grasping in robotics applications and emphasizes the complexity and multidisciplinary cross-application of grasping tasks. The authors present some recent grasping algorithms and methods, including a planar grasping model based on RGB 2D image information, the application of depth cameras, and an end-to-end 6-DOF grasping bit-position detection network. The authors also propose an improved algorithm to generate reliable grasping schemes through specific target detection in depth images to meet the challenges of grasping in complex environments. The article also presents the overall design of a ROS-based robotic arm grasping system and experimentally verifies the feasibility and accuracy of the improved algorithm. The results show that the improved algorithm achieves 92% success rate in multiple grasping tasks with real-time performance. This research is of great significance to the development and application of robot grasping technology.
References
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Q. Yu, W. Shang, Z. Zhao, S. Cong and Z. Li, 2021. "Robotic Grasping of Unknown Objects Using Novel Multilevel Convolutional Neural Networks: From Parallel Gripper to Dexterous Hand," in IEEE Transactions on Automation Science and Engineering, vol. 18, no. 4, pp. 1730-1741, Oct. 2021.
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X. Zhou, X. Lan, H. Zhang, Z. Tian, Y. Zhang and N. Zheng, 2018. "Fully Convolutional Grasp Detection Network with Oriented Anchor Box," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 2018, pp. 7223-7230.
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Index Terms
- Research on Robotic Arm Recognition Grasping Method Based on Improved GGCNN Algorithm Network Architecture: An Enhanced Approach for Object Grasping by Robotic Arms Using GGCNN Algorithm Network Architecture
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Published In
October 2023
1809 pages
ISBN:9798400708305
DOI:10.1145/3650400
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Published: 17 April 2024
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EITCE 2023
EITCE 2023: 2023 7th International Conference on Electronic Information Technology and Computer Engineering
October 20 - 22, 2023
Xiamen, China
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