OpenIAI-SNIO: A Systematic AR-Based Assembly Guidance System for Small-Scale, High-Density Industrial Components

Wang, Yuntao; Cheng, Yu; Geng, Junhao

doi:10.24963/ijcai.2025/1275

OpenIAI-SNIO: A Systematic AR-Based Assembly Guidance System for Small-Scale, High-Density Industrial Components

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Demo Track. Pages 11119-11122.

https://doi.org/10.24963/ijcai.2025/1275

Keywords

Computer Vision: CV: Applications and Systems
Humans and AI: HAI: Applications
Humans and AI: HAI: Human-AI collaboration
Computer Vision: CV: Recognition (object detection, categorization)

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.

Yuntao Wang, Yu Cheng, and Junhao Geng. 2025. OpenIAI-SNIO: A Systematic AR-Based Assembly Guidance System for Small-Scale, High-Density Industrial Components. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 11119–11122. https://doi.org/10.24963/ijcai.2025/1275

@inproceedings{ijcai2025p1275,
  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 = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {11119--11122},
  year      = {2025},
  month     = {8},
  note      = {Demo Track},
  doi       = {10.24963/ijcai.2025/1275},
  url       = {https://doi.org/10.24963/ijcai.2025/1275},
}

TY  - CPAPER
TI  - OpenIAI-SNIO: A Systematic AR-Based Assembly Guidance System for Small-Scale, High-Density Industrial Components
AU  - Wang, Yuntao
AU  - Cheng, Yu
AU  - Geng, Junhao
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 11119
EP  - 11122
DO  - 10.24963/ijcai.2025/1275
UR  - https://doi.org/10.24963/ijcai.2025/1275
ER  -