Abstract
In the process of complex products assembly-commissioning, manual operation is the main reason for low efficiency. The human-robot cooperation (HRC) technology combines the advantages of human and robot, and makes it complete the task in the shared space. It is an effective way to solve the problem by introducing the HRC technology into the complex products of assembly-commissioning. However, the current HRC technology has insufficient perception and cognitive ability of tasks. Therefore, this paper presents a digital twin-driven HRC assembly-commissioning framework. In this framework, a virtual-real mapping environment for HRC is constructed. In order to improve the cognitive ability of robot units to tasks, this paper proposes a method of intention recognition that integrates the features of parts into human joint sequences. In order to improve the adaptability of robot unit to task, the assembly-commissioning task knowledge graph is constructed to quickly extract the implement sequence of robot unit. At the same time, the deep deterministic policy gradient (DDPG) is used to adaptively adjust the robot unit implement action 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.
A digital twin-driven human–robot collaborative assembly-commissioning method for complex products
