Human-robot collaboration assembly personalised safety detection method based on digital twin

Abstract

Human-robot collaboration (HRC) in assembly is becoming increasingly popular in the field of intelligent manufacturing. However, safety concerns remain the most critical issue to be addressed. The complex assembly environment and the individual differences of workers make it challenging to ensure safety effectively. Furthermore, the concept of human-centric manufacturing also places higher requirements on safety and efficiency of HRC assembly. The application of digital twin (DT) technology has bolstered the cognitive and decision-making capabilities in the context of HRC assembly. This technological advancement provides essential support for addressing safety concerns in HRC assembly processes guided by a human-centric approach. A personalised safety detection method for HRC assembly based on DT technology is proposed. This approach involves personalised modelling and simulation of real objects within the HRC process, establishing a collision detection model based on skeleton points. Subsequently, human motion prediction methods drive safety monitoring between humans and robots. Ultimately, personalised safety warning strategies are applied to ensure the safety of the assembly process.