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

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.