Daily Arxiv

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An Exploratory Study on Crack Detection in Concrete through Human-Robot Collaboration

Created by
  • Haebom

Author

Junyeon Kim, Tianshu Ruan, Cesar Alan Contreras, Manolis Chiou

Outline

This paper investigates the effectiveness of human-robot collaboration (HRC), integrating AI-based visual crack detection with a mobile Jackal robot platform, for structural inspections of nuclear facilities. It highlights the safety risks, high cognitive load, and potential for human error associated with conventional manual inspection methods, and argues that advances in AI and robotics technology enable safer, more efficient, and more accurate inspection methods. Experimental results demonstrate that HRC outperforms conventional manual methods by increasing inspection accuracy and reducing operator burden.

Takeaways, Limitations

Takeaways:
The potential to improve the efficiency and safety of nuclear facility structural inspections using AI-based HRC has been presented.
Alternatives to overcome __T1914_____ of manual inspection
Confirming the potential for reducing worker burden and improving inspection accuracy through integration of robotic platforms and AI algorithms.
Limitations:
Limitations in generalizability due to limitations in the experimental environment
Further research is needed on the applicability to various types of cracks and complex structures.
Lack of considerations regarding the cost and maintenance of robotic platforms and AI algorithms.
Additional safety and reliability verification is needed for application to actual nuclear power facilities.
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