Research on Digital Twin Modeling Method for Robotic Assembly Cell Based on Data Fusion and Knowledge Reasoning

doi:10.3901/JME.2024.05.036

Abstract

Abstract: At present, robotic task planning usually relies on manual teaching or offline programming, which often requires operators to spend a lot of time to define and program the operating process of robot. In order to further improve the autonomy and efficiency of robotic assembly task planning, a digital twin modeling method for robotic assembly cell based on data fusion and knowledge reasoning is proposed. The proposed method maps robotic assembly cell from physical space to virtual space, which lays the foundation for subsequent autonomous and dynamic assembly operations of robot in the digital twin environment. The robotic assembly cell is divided into three parts: robot & toolings, product, and robotic assembly process. The methods for constructing digital twin models of robot & toolings, product, and robotic assembly process are studies based on virtual-real mapping, fusion of multi-source heterogeneous data, and knowledge reasoning, respectively. The digital twin model of robotic assembly cell can realize fast planning and simulation verification for robotic assembly tasks. Finally, the proposed method is applied to the robotic assembly task planning for a type of stepping motor. The effectiveness of the proposed method in real application cases is verified.

Key words: digital twin, industrial robot, assembly, modeling, task planning

CLC Number:

TG156

LIU Daxin, WANG Ke, LIU Zhenyu, XU Jiatong, TAN Jianrong. Research on Digital Twin Modeling Method for Robotic Assembly Cell Based on Data Fusion and Knowledge Reasoning[J]. Journal of Mechanical Engineering, 2024, 60(5): 36-50.

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