Journal of Engineering, Project, and Production Management, 2026, 16(6), 2026-20
Parts Grasping by Intelligent Industrial Robots Based on Image Matching Algorithms and CNNs
Lecturer, School of
Equipment Engineering, Shanxi Vocational University of Engineering
Science and
Technology, Jinzhong, 030619, China, Email: taoli71@outlook.com
Production Management
Received January 7, 2026; revised March 19, 2026; August 2, 2026; accepted September 12, 2026
Available online September 20, 2026
Abstract: This study proposes an intelligent industrial robot part-grasping optimization model that integrates image-matching algorithms with convolutional neural networks. The model employs SuperPoint-based feature matching and introduces multi-scale convolution and a channel attention mechanism to enhance feature extraction. A generative convolutional neural network module further predicts the optimal grasping posture end-to-end. Experimental results show that the proposed image matching model achieves a matching accuracy of 97.8%, a repeatability of 92.5%, and a feature detection speed of 12.1ms per frame. The model maintains stable performance under complex lighting conditions and multi-view industrial environments, demonstrating strong robustness and real-time capability. These improvements enable more reliable perception and decision-making in robotic grasping tasks, thereby supporting flexible automation and the stable deployment of industrial robots in real manufacturing scenarios.
Keywords: Intelligent industrial robot; image matching algorithm; convolutional neural network; part grasping; feature fusion.
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Citation: Li, T. (2026). Parts Grasping by Intelligent Industrial Robots Based on Image Matching Algorithms and CNNs. Journal of Engineering, Project, and Production Management, 16(6), 2026-20.
