Evolving process maintenance through human-robot collaboration: An agent-based system performance analysis

Abstract

Periodic inspections of pressurized vessel systems are essential for maintaining safety through early fault detection. Traditional inspections often expose human operators to hazardous conditions within confined spaces. The advent of inspection robots has shifted the paradigm towards human-robot collaboration (HRC), which seeks to reduce risk while maintaining operational adaptability. This study compared the HRC and fully manual (FM) inspection processes, providing strategic insights for stakeholders. Historically, system performance evaluations have simplified or ignored dynamic human factors. To address this oversight, our research employs Agent-Based Models (ABMs) that encompass the evolving nature of human error, including the impact of fatigue and organizational factors, as well as the variability of human behavior and error recovery mechanisms. Our findings reveal that HRC significantly outperforms FM inspections, enhancing efficiency, accuracy, and safety. Notably, the study confirms that the miss rate of artificial intelligence (AI) for image identification within the HRC process is crucial for reliability and should not fall below the threshold of 0.04. This threshold is a benchmark for AI performance in HRC systems, ensuring that the balance between automated efficiency and human oversight is optimized. The research provides a comprehensive evaluation of HRC in pressurized vessel inspections. It offers a deeper understanding of the complex dynamics involved, advocating for integrating robust AI algorithms to support human operators in safety–critical tasks.