Abstract:

In response to the lack of comprehensive functionality and limited application scenarios in the current field of industrial robot digital twin systems, which results in low versatility, a method for constructing a digital twin system for industrial robots with high versatility is proposed. A four-dimensional system architecture for the digital twin is designed, and the components and functions of the four-dimensional system are analyzed, based on the system level planning of the four-dimensional system, the concept of integrating reinforcement learning into the virtual replacement of real concept is defined. By constructing a multi-attribute virtual model and using TCP communication protocol to build a data communication system for virtual-real data interaction, combined with robot forward and inverse kinematic analysis, the virtual-real mapping and control functions are achieved. A reinforcement learning virtual scene is constructed, using a virtual robot model to replace the physical robot for reinforcement learning training, to achieve automatic path planning functionality. The experimental results verify the feasibility and reliability of the developed digital twin system, providing a new solution for further enriching the functionality of industrial robot digital twin systems.

Key words: digital twin, industrial robots, reinforcement learning, four-dimensional model, virtual-real mapping