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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

自动化学报2025,Vol.51Issue(7):1463-1479,17.
自动化学报2025,Vol.51Issue(7):1463-1479,17.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

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

冯运 1童翊轩 1王耀南 1唐永鹏 1吴昊天 2谭浩然 1江一鸣 1朴玄斌3

作者信息

  • 1. 湖南大学人工智能与机器人学院 长沙 410082||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
  • 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||长沙理工大学人工智能学院 长沙 410114
  • 3. 北京科技大学智能科学与技术学院 北京 100083
  • 折叠

摘要

Abstract

Major equipment manufacturing in fields such as aerospace,marine vessels,and rail transportation plays a crucial role in driving economic development and ensuring national defense security.Traditional manufacturing,reliant on manual processing and special-purpose machines,lacks flexibility and intelligence,making it inadequate for large-scale,multi-variety flexible manufacturing.Cluster robots,leveraging collaborative mechanisms of biologic-al clusters,can continuously expand and optimize execution capabilities in complex scenarios for efficient collabora-tion and intelligent manufacturing.As a cutting-edge manufacturing technology,digital twin offers integrated solu-tions and toolchain support for the construction,virtual debugging,scheduling,and collaborative control of such cluster robot collaborative manufacturing systems,significantly improving system efficiency and safety.This review presents the research background,research status,key technologies,and development trends of digital twin techno-logy,and analyzes a case study of a self-developed cluster robot digital twin system for aircraft wall panel assembly.It offers valuable insight into the application of digital twins to collaborative manufacturing of major equipment by cluster robots.

关键词

数字孪生/智能制造/集群机器人/协同制造

Key words

Digital twin/intelligent manufacturing/cluster robot/collaborative manufacturing

引用本文复制引用

冯运,童翊轩,王耀南,唐永鹏,吴昊天,谭浩然,江一鸣,朴玄斌..重大装备集群机器人协同制造数字孪生技术综述[J].自动化学报,2025,51(7):1463-1479,17.

基金项目

国家自然科学基金(62293510/62293515,62203161,62473143,62303172),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(2024JJ5087,2023JJ40179),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)资助Supported by National Natural Science Foundation of China(62293510/62293515,62203161,62473143,62303172),National Key Research and Development Program of China(2023YFB4706400),Open Project of Xiangjiang Laboratory(23XJ03012),Natural Science Foundation of Hunan Province(2024JJ5087,2023JJ40179),Natural Science Foundation of Guangdong Province(2025A1515011482),Natural Science Foundation of Ji-angxi Province(20232BAB212024),Open Project Program of State Key Laboratory of Virtual Reality Technology and Sys-tems,Beihang University(VRLAB2025B04) (62293510/62293515,62203161,62473143,62303172)

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