重大装备集群机器人协同制造数字孪生技术综述

冯运; 童翊轩; 王耀南; 唐永鹏; 吴昊天; 谭浩然; 江一鸣; 朴玄斌

doi:10.16383/j.aas.c240707

重大装备集群机器人协同制造数字孪生技术综述

doi: 10.16383/j.aas.c240707 cstr: 32138.14.j.aas.c240707

冯运^{1, 2,},
童翊轩^{1, 2,},
王耀南^{1, 2,},
唐永鹏^{1, 2,},
吴昊天^{1, 2, 3,},
谭浩然^{1, 2,},
江一鸣^{2, 4,},
朴玄斌^5,

1.
湖南大学电气与信息工程学院长沙 410082
2.
湖南大学机器人视觉感知与控制技术国家工程研究中心长沙 410082
3.
长沙理工大学电气与信息工程学院长沙 410114
4.
湖南大学机器人学院长沙 410082
5.
北京科技大学智能科学与技术学院北京 100083

基金项目: 国家自然科学基金(62293510/62293515, 62203161, 62473143), 国家重点研发计划(2023YFB4706400), 湘江实验室开放基金(23XJ03012), 湖南省自然科学基金(2024JJ5087), 广东省自然科学基金(2025A1515011482), 江西省自然科学基金(20232BAB212024), 虚拟现实技术与系统全国重点实验室(北京航空航天大学)开放课题基金(VRLAB2025B04)

详细信息

作者简介:
冯运：湖南大学电气与信息工程学院副教授, 机器人视觉感知与控制技术国家工程研究中心研究员. 主要研究方向为机器人数字孪生, 系统建模与运维技术. E-mail: fyrobot@hnu.edu.cn

童翊轩：湖南大学电气与信息工程学院博士研究生. 2024年获得江南大学学士学位. 主要研究方向为机器人数字孪生, 系统建模和控制理论. E-mail: b2409z0455@hnu.edu.cn

王耀南：中国工程院院士, 湖南大学电气与信息工程学院教授. 1995年获得湖南大学博士学位. 主要研究方向为机器人学, 智能控制和图像处理. 本文通信作者. E-mail: yaonan@hnu.edu.cn

唐永鹏：湖南大学电气与信息工程学院博士研究生. 2018年获得湖南大学学士学位. 主要研究方向为机器人测量技术, 数字孪生. E-mail: bylh_ieee@hnu.edu.cn

吴昊天：长沙理工大学电气与信息工程学院副教授, 湖南大学电气与信息工程学院博士后, 机器人视觉感知与控制技术国家工程研究中心研究员. 主要研究方向为三维测量, 点云配准, 三维重建和数字孪生. E-mail: wuhaotian@csust.edu.cn

谭浩然：湖南大学电气与信息工程学院副教授, 机器人视觉感知与控制技术国家工程研究中心研究员. 主要研究方向为机器人智能控制与网络化控制系统. E-mail: tanhaoran@hnu.edu.cn

江一鸣：湖南大学机器人学院副教授, 机器人视觉感知与控制技术国家工程研究中心副研究员. 主要研究方向为多机器人协同控制及应用. E-mail: ymjiang@hnu.edu.cn

朴玄斌：北京科技大学智能科学与技术学院硕士研究生. 主要研究方向为集群机器人协同作业的数字孪生. E-mail: m202310575@xs.ustb.edu.cn

计量
- 文章访问数: 63
- HTML全文浏览量: 36
- 被引次数: 0
出版历程
- 收稿日期: 2024-11-01
- 录用日期: 2025-03-23
- 网络出版日期: 2025-05-27

Digital Twin Technology for Collaborative Manufacturing of Major Equipment by Cluster Robots: A Review

FENG Yun^{1, 2
,},
TONG Yi-Xuan^{1, 2
,},
WANG Yao-Nan^{1, 2
,},
TANG Yong-Peng^{1, 2
,},
WU Hao-Tian^{1, 2, 3
,},
TAN Hao-Ran^{1, 2
,},
JIANG Yi-Ming^{2, 4
,},
PIAO Xuan-Bin^5
,

1.
College of Electrical and Information Engineering, Hunan University, Changsha 410082
2.
National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha 410082
3.
School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114
4.
School of Robotics, Hunan University, Changsha 410082
5.
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083

Funds: Supported by National Natural Science Foundation of China (62293510/62293515, 62203161, 62473143), National Key Research and Development Program of China (2023YFB4706400), Open Project of Xiangjiang Laboratory (23XJ03012), Natural Science Foundation of Hunan Province (2024JJ5087), Natural Science Foundation of Guangdong Province (2025A1515011482), Natural Science Foundation of Jiangxi Province (20232BAB212024), Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (VRLAB2025B04)

More Information

Author Bio:
FENG Yun　Associate professor at the College of Electrical and Information Engineering, Hunan University, researcher at the National Engineering Research Center of Robot Visual Perception and Control Technology. His main research interest covers robot digital twins, system modeling and operation and maintenance technology

TONG Yi-Xuan　Ph. D. candidate at the College of Electrical and Information Engineering, Hunan University. He received his bachelor degree from Jiangnan University in 2024. His main research interest covers robot digital twins, system modeling and control theory

WANG Yao-Nan　Academician at Chinese Academy of Engineering, professor at the College of Electrical and Information Engineering, Hunan University. He received his Ph. D. degree from Hunan University in 1995. His main research interest covers robotics, intelligent control, and image processing. Corresponding author of this paper

TANG Yong-Peng　Ph.D candidate at the College of Electrical and Information Engineering, Hunan University. He received the bachelor degree from Hunan University in 2018. His main research interest covers robotic measurement technology and digital twins

WU Hao-Tian　Associate professor at the School of Electrical and Information Engineering, Changsha University of Science and Technology, postdoctor at the College of Electrical and Information Engineering, Hunan University, researcher at the National Engineering Research Center of Robot Visual Perception and Control Technology. His main research interest covers 3D measurement, point cloud registration, 3D reconstruction and digital twins

TAN Hao-Ran　Associate professor at the College of Electrical and Information Engineering, Hunan University, researcher at the National Engineering Research Center of Robot Visual Perception and Control Technology. His main research interest covers robot intelligent control and networked control systems

JIANG Yi-Ming　Associate professor at the School of Robotics, Hunan University, associate researcher at the National Engineering Research Center of Robot Visual Perception and Control Technology. His main research interest covers multi-robot cooperative control and their applications

PIAO Xuan-Bin　Master student at the School of Intelligence Science and Technology, University of Science and Technology Beijing. His main research interest is digital twins for cluster robots co-operation

摘要

摘要: 航空航天、海洋舰船、轨道交通等领域的重大装备制造在服务于国家重大需求、引领国民经济发展与保障国防安全中占据举足轻重的作用. 传统人工加工一致性差、效率低, 专机加工工作空间受限、柔性不足, 车间加工协同性弱、智能化程度低, 这些都难以满足大型化、多品种的柔性制造需求. 集群机器人协同制造利用生物集群的智能协作机理, 能在大型复杂场景中不断拓展并优化各类作业的执行能力, 克服传统制造模式中的不足, 实现制造过程的高效协同和智能制造. 数字孪生技术作为新兴制造技术, 为集群机器人协同制造系统的构建部署、虚拟调试、智能感知、通信协作、调度规划和协同控制等方面提供技术集成方案和工具链支撑, 能有效提升制造系统的效率, 保障制造过程安全性. 本文详细介绍数字孪生技术的研究背景与意义、国内外研究现状、关键技术及应用和发展趋势, 并以自主研发的一套面向飞机壁板装配的集群机器人数字孪生系统为例进行具体分析. 本文对于了解数字孪生在制造领域的技术方案和工具体系以及数字孪生在重大装备集群机器人协同制造方面的应用研究具有参考价值.
- 数字孪生 /
- 智能制造 /
- 集群机器人 /
- 协同制造
Abstract: Aerospace, marine vessels, rail transportation and other fields of major equipment manufacturing in the service of the country's major needs, leading the development of the national economy and safeguarding national defense and security occupy a pivotal role. Traditional manual processing has poor consistency and low efficiency. Special-purpose machine processing is limited by workspace constraints and lacks flexibility. Workshop processing collaboration is weak and the degree of intelligence is low. All these factors make it difficult to meet the needs of large-scale, multi-variety flexible manufacturing. Cluster robot collaborative manufacturing, based on the intelligent collaboration mechanisms of biological clusters, can continuously expand and optimize the execution capabilities of various operations in large-scale complex scenarios, overcoming the limitations of traditional manufacturing models and enabling efficient collaboration and intelligent manufacturing in the production process. As an emerging manufacturing technology, digital twins provide technical integration solutions and tool chain support for the construction and deployment, virtual debugging, intelligent perception, communication and collaboration, scheduling planning and collaborative control of cluster robots collaborative manufacturing systems, which can effectively improve the efficiency of the manufacturing system and ensure the safety of the manufacturing process. This paper introduces in detail the research background and significance of digital twins technology, the current status of research at home and abroad, key technologies and applications and development trends, and analyzes the self-developed set of cluster robot digital twins system for the assembly of aircraft wall panels as an example. This paper is of reference value for understanding the technical solutions and tool systems of digital twins in the manufacturing field as well as the application research of digital twins in the collaborative manufacturing of cluster robots for major equipment.
- Digital twins /
- intelligent manufacturing /
- cluster robots /
- collaborative manufacturing

HTML全文

图 1 重大装备对于国民经济和国防安全十分重要

Fig. 1 Major equipment is important for national economy and national defense security

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图 2 国际研究机构在集群机器人协同制造开展的相关应用研究

Fig. 2 Applied research related to collaborative manufacturing of cluster robots by international research institutes

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图 3 数字孪生结构由三维到五维模型的完善

Fig. 3 Refinement of digital twin structures from 3D to 5D models

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图 4 亚马逊机器人公司建造数字孪生仓库

Fig. 4 Amazon robotics builds digital twin warehouse

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图 5 BMW首家虚拟工厂在Omniverse中启用

Fig. 5 BMW's first virtual factory in Omniverse

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图 6 海尔与Unity开发基于数字孪生技术的智能柔性产线

Fig. 6 Haier and Unity develop intelligent flexible production line based on digital twin technology

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图 7 英伟达与西门子公司联合开发多机器人制造数字孪生系统

Fig. 7 NVIDIA and Siemens co-develop digital twins system for muti-robot manufacturing

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图 8 多智能体数字孪生元模型^[38]

Fig. 8 A metamodel of digital twins for muti-agents^[38]

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图 9 数字孪生数据的组成^[80]

Fig. 9 Components of digital twin data^[80]

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图 10 集群机器人协同制造数字孪生系统框架

Fig. 10 A framework of digital twins system for collaborative manufacturing of cluster robots

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图 11 集群机器人协同制造数字孪生系统用户界面

Fig. 11 User interface of digital twins system for collaborative manufacturing of cluster robots

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图 12 机械臂虚实同步实验测试

Fig. 12 Mechanical arm virtual-physical synchronization experiment test

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图 13 多机器人协同测量虚实同步实验测试

Fig. 13 Multi-robot collaborative measurement of virtual-physical synchronization experiment test

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