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

TL;DR

This paper investigates how combining AI and robotic systems in human-robot collaboration improves crack detection accuracy and reduces workload during concrete inspections in nuclear facilities.

Contribution

It introduces an AI-assisted visual crack detection system integrated into a mobile robot, demonstrating improved inspection performance over manual methods.

Findings

HRC enhances inspection accuracy

Reduces operator workload

Potential for safer, more efficient inspections

Abstract

Structural inspection in nuclear facilities is vital for maintaining operational safety and integrity. Traditional methods of manual inspection pose significant challenges, including safety risks, high cognitive demands, and potential inaccuracies due to human limitations. Recent advancements in Artificial Intelligence (AI) and robotic technologies have opened new possibilities for safer, more efficient, and accurate inspection methodologies. Specifically, Human-Robot Collaboration (HRC), leveraging robotic platforms equipped with advanced detection algorithms, promises significant improvements in inspection outcomes and reductions in human workload. This study explores the effectiveness of AI-assisted visual crack detection integrated into a mobile Jackal robot platform. The experiment results indicate that HRC enhances inspection accuracy and reduces operator workload, resulting in potential superior performance outcomes compared to traditional manual methods.