A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19. (July 2021) - Lv, Qibing; Zhang, Rong; Sun, Xuemin; Lu, Yuqian; Bao, Jinsong
Highlights: The proposed DT-based HRC assembly framework improves the overall assembly efficiency and reduces the workload of human. The proposed double deep deterministic policy gradient (D-DDPG) model reduces the time consumption in picking or fixing the part. The proposed process model of DT optimizes the trajectory of robot and ensures the safety of HRC assembly. The proposed evaluation model of DT comprehensively assesses the performance of HRC assembly. Abstract: 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.