An Robot Vision Grasping Network Based on Inception-Lite. Issue 2 (January 2021)

• Record Type: Journal Article
• Authors: Xu, Liang; Zhou, Ziwei; Wang, Chaoyang
• Is Part Of: Journal of physics. Volume 1748:Issue 2(2021)
• Publication Date: 2021-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1748/2/022041
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Using computer vision algorithms to find the grasping position of unknown objects is a key issue in the development of intelligent robot technology. In order to address the problem of robot grasping, firstly, based on the analysis of Inception-v1, a novel Inception-Lite convolution module is proposed, and then a grasping network GRASPNET-CLF is designed based on the convolution module. Finally, the grasping network GRASPNET-CLF is trained and verified by GPU on Cornell grasping dataset. The experimental results show that the GRASPNET-CLF has better performance in the same kind of network, and meets the real-time requirements.