An Exploratory Study on Crack Detection in Concrete through Human-Robot Collaboration
It addresses safety and efficiency problems in structural inspection for nuclear facilities, but appears incremental as it builds on existing AI and robotic technologies.
This study tackled crack detection in concrete for nuclear facility inspection by exploring AI-assisted visual detection on a mobile robot, finding that human-robot collaboration enhances accuracy and reduces operator workload compared to manual methods.
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.