A digital twin-driven human–robot collaborative assembly-commissioning method for complex products

The process of complex product assembly-commissioning has the characteristics of high flexibility and firm dynamics. To overcome the drawbacks of manual assembly, deploying automated and intelligent techniques can greatly boost efficiency, improve flexibility, and enhance the quality control. The human–robot collaborative (HRC) technology combines the advantages of human capabilities and the efficiency and precision of robots. However, current HRC technology lacks of perception and cognitive ability, especially in dynamic environments. Therefore, this paper proposed a digital twin-driven HRC assembly-commissioning framework. In this framework, a virtual-physical mapping environment for HRC is constructed. In order to improve the cognitive ability of robot units to tasks, a motion intention recognition approach is proposed which integrates the feature of part into human joint sequences. To improve the adaptability of the robot unit to tasks, the assembly-commissioning task knowledge graph is developed to extract the action sequence of the robot unit in a timely manner. Moreover, the deep deterministic policy gradient (DDPG) is used to adaptively adjust the robot unit movement path in the process of assembly-commissioning. Finally, the effectiveness of the proposed method is verified by taking a particular type of automobile generator as a case study product.