OpenIAI-SNIO: A Systematic AR-Based Assembly Guidance System for Small-Scale, High-Density Industrial Components
Abstract
This paper develops an AR-based assembly guidance system, OpenIAI-SNIO, for small-scale, high-density industrial components (SHIC), which addresses the challenge of existing AR technology's inability to achieve complete, accurate, and stable visual cognition and assembly operation guidance for SHIC. OpenIAI-SNIO combines artificial intelligence methods such as computer vision and deep learning with rule-based reasoning and augmented reality to achieve adaptive, whole process, and precise guidance of SHIC assembly in situations where visual information is insufficient. The application case shows that OpenIAI-SNIO can effectively improve the efficiency and quality of SHIC assembly, and reduce the workload of operators, realizing the systematic and practical application of AR technology in SHIC assembly.
Cite
Text
Wang et al. "OpenIAI-SNIO: A Systematic AR-Based Assembly Guidance System for Small-Scale, High-Density Industrial Components." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/1275Markdown
[Wang et al. "OpenIAI-SNIO: A Systematic AR-Based Assembly Guidance System for Small-Scale, High-Density Industrial Components." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/wang2025ijcai-openiai/) doi:10.24963/IJCAI.2025/1275BibTeX
@inproceedings{wang2025ijcai-openiai,
title = {{OpenIAI-SNIO: A Systematic AR-Based Assembly Guidance System for Small-Scale, High-Density Industrial Components}},
author = {Wang, Yuntao and Cheng, Yu and Geng, Junhao},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2025},
pages = {11119-11122},
doi = {10.24963/IJCAI.2025/1275},
url = {https://mlanthology.org/ijcai/2025/wang2025ijcai-openiai/}
}