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.
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.
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.
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