Digital Twin-Based Task Rescheduling for Robotic Assembly Line

Abstract

With the development of smart manufacturing, robotic assembly technology in automatic production line has greatly improved production efficiency and product quality. In assembly process, dynamic disturbances such as advance delivery and changes in processing time may occur, which requires rescheduling of robotic assembly line (RAL). Traditional scheduling methods are not sufficient to meet the real-time and adaptive requirements. Due to digital twin’s characteristics of virtual reality interaction, real-time mapping and iterative optimization, the digital twin-based assembly planning (DTAP) model is established and the task rescheduling strategy for RAL is proposed. The virtual RAL is the real reflection of the physical RAL. When the RAL is dynamically disturbed, based on the virtual entity of RAL and the adaptive discrete bees algorithm (ADBA), the assembly task assignment is optimized and fed back to the physical RAL. Mapping and interaction between the physical and virtual RAL achieves the precise scheduling and execution of assembly tasks and improves the assembly efficiency. Finally, the case study based on bearing fitting is implemented to verify the effectiveness of the proposed method.