Digital Twin and Deep Reinforcement Learning-Driven Robotic Automation System for Confined Workspaces: A Nozzle Dam Replacement Case Study in Nuclear Power Plants
Su-Young Park,
Cheonghwa Lee,
Suhwan Jeong
et al.
Abstract:Robotic automation has emerged as a leading solution for replacing human workers in dirty, dangerous, and demanding industries to ensure the safety of human workers. However, practical implementation of this technology remains limited, requiring substantial effort and costs. This study addresses the challenges specific to nuclear power plants, characterized by hazardous environments and physically demanding tasks such as nozzle dam replacement in confined workspaces. We propose a digital twin and deep-reinforc… Show more
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