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.

在 COVID-19 之后，医疗设备的生产需求正在迅速增加。此类产品主要采用手工或固定程序组装，结构复杂灵活。然而，目前组装方式的低效率和适应性无法满足组装要求。因此，本文提出了一种基于数字孪生（DT）的人机协作（HRC）装配新框架。所提出框架的数据管理系统集成了来自数字孪生空间的各种数据。为了获得动态环境中的 HRC 策略和动作序列，在 DT 中应用双深度确定性策略梯度（D-DDPG）作为优化模型。在装配过程中，采用性能模型来评价回弹装配的质量。