A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19

In the wake of COVID-19, the production demand of medical equipment is increasing rapidly. This type of products is mainly assembled by hand or fixed program with complex and flexible structure. However, the low efficiency and adaptability in current assembly mode are unable to meet the assembly requirements. So in this paper, a new framework of human-robot collaborative (HRC) assembly based on digital twin (DT) is proposed. The data management system of proposed framework integrates all kinds of data from digital twin spaces. In order to obtain the HRC strategy and action sequence in dynamic environment, the double deep deterministic policy gradient (D-DDPG) is applied as optimization model in DT. During assembly, the performance model is adopted to evaluate the quality of resilience assembly. The proposed framework is finally validated by an alternator assembly case, which proves that DT-based HRC assembly has a significant effect on improving assembly efficiency and safety.

Automated summary

In response to the increasing demand for medical equipment production due to COVID-19, a new framework of human-robot collaborative assembly based on digital twin is proposed in this paper. By integrating data from digital twin spaces and using optimization models, the proposed framework improves assembly efficiency and safety.