装配机器人的数字孪生虚实同步及抓取方法

doi:10.16182/j.issn1004731x.joss.23-0575

摘要/Abstract

摘要：

针对工业机器人进行装配任务时对物料智能抓取精度差和数据处理难等问题，提出一种基于数字孪生的装配机器人虚实同步及抓取方法。以数字孪生为基础，设计了数字孪生装配机器人虚实同步及抓取架构。通过对多源异构数据的分类实现OPC UA服务器和客户端的信息模型搭建，并以OPC UA通信协议为桥梁进行装配机器人的数据通信，实现虚实同步。通过虚拟机器人对卷积神经网络进行进一步训练，提高对物料的抓取精度。在所开发的机器人数字孪生原型系统中验证所提方法的正确性和有效性，为实现装配机器人的数字孪生虚实同步和精确抓取提供新方案。

关键词: 数字孪生, 装配装机器人, 实时数据, 抓取, OPC UA, 虚实同步

Abstract:

A method based on digital twin for assembly robot virtual-real synchronization and grasping is proposed to address the issues of poor intelligent grasping accuracy and difficult data processing in assembly tasks for industrial robots. Based on the digital twin, a digital twin assembly robot virtual-real synchronization and grasping architecture is designed. The OPC UA information model is built by classifying multi-source heterogeneous data, and the OPC UA communication protocol is used as a bridge for data communication of the assembly robot, achieving virtual-real synchronization. The convolutional neural network is further trained using the virtual robot to improve the grasping accuracy of the materials. The correctness and effectiveness of the proposed method are validated in the developed robot digital twin prototype system, providing a new solution for achieving digital twin virtual-real synchronization and precise grasping for assembly robots.

Key words: digital twin, assembly robot, real-time data, grab, OPC UA, virtual and real synchronization

中图分类号:

TP391.9

徐健,刘高峰,赵一剑等 . 装配机器人的数字孪生虚实同步及抓取方法[J]. 系统仿真学报, 2024, 36(9): 2181-2192.

Xu Jian,Liu Gaofeng,Zhao Yijian,et al . The Synchronous Grasping Method of Virtual-real Assembly Robot Based on Digital Twin[J]. Journal of System Simulation, 2024, 36(9): 2181-2192.

图/表 16

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

图13

表1

关节角度数据 (°)

点位	J1	J2	J3	J4	J5	J6
$A 1$	-70.21	91.32	345.16	112.58	132.98	16.51
$A 2$	-70.12	91.27	345.08	112.47	132.84	16.42
$Δ A$	0.09	0.05	0.08	0.11	0.14	0.09
$B 1$	72.18	121.21	112.93	109.28	39.98	-72.56
$B 2$	72.09	121.08	112.87	109.19	40.05	-72.49
$Δ B$	0.09	0.13	0.06	0.09	0.07	0.07
$C 1$	140.31	-48.68	37.12	-65.89	48.75	94.87
$C 2$	140.23	-48.56	37.19	-65.74	48.63	94.93
$Δ C$	0.08	0.12	0.07	0.15	0.12	0.06

表1

图14

表2

参考文献 20

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抓取	原始CNN	数字孪生优化CNN
虚拟	0.58	0.63
真实	0.48	0.56

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