Research on intelligent assembly experimental teaching platform based on stereo model stacking algorithm

Hongyi Wang; Kun Zhang; Yao Wang; Jiming Liu; Lijuan Yu; Haizhong Liu

doi:10.1117/12.3011262

8 November 2023 Research on intelligent assembly experimental teaching platform based on stereo model stacking algorithm

Hongyi Wang, Kun Zhang, Yao Wang, Jiming Liu, Lijuan Yu, Haizhong Liu

Author Affiliations +

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129230O (2023) https://doi.org/10.1117/12.3011262
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

The collision detection technology is an important basis for the reliability of artificial intelligence planning assembly process. In the past, collision detection technology has the problem of inaccurate detection of concave parts or high occupation of computing resources, which reduces the practicability of intelligent assembly in industrial field. This article proposes a collision detection method based on stereo model stacking algorithm, which can detect concave parts effectively and occupy low resources. Combined with this method, the intelligent assembly experimental teaching platform is built, which has multiple research and teaching function modules such as assembly sequence optimization and assembly demonstration. It has achieved good results in the research of intelligent assembly field and students' practical teaching.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongyi Wang, Kun Zhang, Yao Wang, Jiming Liu, Lijuan Yu, and Haizhong Liu "Research on intelligent assembly experimental teaching platform based on stereo model stacking algorithm", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129230O (8 November 2023); https://doi.org/10.1117/12.3011262

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