A Digital Twin–Based Environment-Adaptive Assignment Method for Human–Robot Collaboration

Abstract

Human–robot collaboration, which strives to combine the best skills of humans and robots, has shown board application prospects in meeting safe–effective–flexible requirements in various fields. The ideation of much closer interaction between humans and robots has greatly developed the exploration of digital twin to enhance collaboration. By offering high-fidelity models and real-time physical–virtual interaction, the digital twin enables to achieve an accurate reflection of the physical scenario, including not only human–robot conditions but also environmental changes. However, the appearance of unpredictable events may cause an inconsistency between the established schedule and actual execution. To cope with this issue, an environment-adaptive assignment method based on digital twin for human–robot collaboration is formed in this study. The proposed approach consists of a factor–event–act mechanism that analyzes the dynamic events and their impacts from both internal and external perspectives of digital twin and a genetic algorithm–based assignment algorithm to respond to them. Experiments are carried out in the last part, aiming to show the feasibility of the proposed method.