多传感融合的深腔体结构装配过程实时感知与精准控制技术

doi:10.3901/JME.260364

摘要/Abstract

摘要： 针对深腔体结构装配中可视化程度较低，导致其装配质量难以保证等问题，提出一种多传感融合的深腔体结构装配过程实时感知与精准控制方案。首先，分析深腔体结构的装配特点，其几何特征与装配载荷是影响装配质量的关键因素，结合数字孪生理论介绍了该研究所构建的装配系统的功能模块及逻辑关系；其次，开展深腔体装配过程感知研究，分别采用激光传感与力传感装置实现对深腔体几何特征与装配载荷特征的有效获取；最后，以深腔体装配特征信息为输入，开展虚拟装配和误差补偿研究，通过装配路径规划与机器人末端柔顺控制，实现了深腔体装配过程的精准调控，并以数字孪生技术为框架，构建了装配感知与调控系统。最终以某型航空器深腔体结构为对象开展关键技术验证，结果表明该技术可实现深腔体装配多阶段的特征信息感知与调控，为其高性能装配提供了有效的理论架构与技术方案。

关键词: 深腔体结构, 多传感融合, 装配过程感知, 虚拟装配, 装配调控

Abstract: To address the low visualization in deep cavity structure assembly, which affects assembly quality, a multi-sensor fusion approach for real-time sensing and precise control is proposed. First, the assembly characteristics of deep cavity structures are analyzed, with geometric features and assembly loads identified as key factors affecting assembly quality. The functional modules and logical relationships of the assembly system are introduced, combining digital twin theory. Next, a sensing study of the assembly process is conducted, utilizing laser and force sensors to effectively capture the geometric and load features of the deep cavity structure. Finally, using assembly feature information as input, virtual assembly and error compensation research is carried out. Through assembly path planning and robot end-effector compliance control, precise regulation of the assembly process is achieved. A sensing and control system is developed based on a digital twin framework. Key technology validation on a specific aircraft’s deep cavity structure demonstrates that this method can effectively sense and control multi-stage feature information, providing an effective theoretical framework and technical solution for high-performance assembly.

Key words: deep cavity structure, multi-sensor fusion, assembly process sensing, virtual assembly, assembly control

中图分类号:

TP272
TG95

魏朝辉, 王子航, 叶祥瑞, 张开富, 程晖, 郭留明, 梁德建. 多传感融合的深腔体结构装配过程实时感知与精准控制技术[J]. 机械工程学报, 2026, 62(7): 52-66.

WEI Zhaohui, WANG Zihang, YE Xiangrui, ZHANG Kaifu, CHENG Hui, GUO Liuming, LIANG Dejian. Real-time Perception and Precision Control Technology for Deep Cavity Structure Assembly Process Based on Multi-sensor Fusion[J]. Journal of Mechanical Engineering, 2026, 62(7): 52-66.

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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260364

http://www.cjmenet.com.cn/CN/Y2026/V62/I7/52

参考文献

[1] 吴晓苏,周智敏. 薄壁矩形深腔体零件的数控加工工艺[J]. 机械工程师,2007,1:123-125. WU Xiaosu,ZHOU Zhimin. NC machining technology of thin-walled rectangular deep cavity parts[J]. Mechanical Engineer,2007,1:123-125.
[2] 孟松鹤,叶雨玫,杨强,等. 数字孪生及其在航空航天中的应用[J]. 航空学报,2020,41(9):6-17. MENG Songhe,YE Yumei,YANG Qiang,et al. digital twinning and its application in aerospace[J]. Journal of aeronautics,2020,41(9):6-17.
[3] BLASCH E. Assembling a distributed fused information-based human-computer cognitive decision making tool[J]. IEEE Aerospace and Electronic Systems Magazine,2000,15(5):11-17.
[4] 卢远,国凯,孙杰. 工业机器人轨迹精度力-位置复合补偿方法[J]. 机械工程学报,2022,58(14):181-189. LU Yuan,GUO Kai,SUN Jie. Force-position compound compensation method for industrial robot trajectory accuracy[J]. Journal of Mechanical Engineering,2022,58(14):181-189.
[5] 周健,郑联语,樊伟,等. 工业机器人定位误差在线自适应补偿[J]. 机械工程学报,2023,59(5):53-66. ZHOU Jian,ZHENG Lianyu,FAN Wei,et al. On-line adaptive compensation for positioning errors of industrial robots[J]. Journal of Mechanical Engineering,2023,59(5):53-66.
[6] 史晓佳,张福民,曲兴华,等. KUKA工业机器人位姿测量与在线误差补偿[J]. 机械工程学报,2017,53(8):1-7. SHI Xiaojia,ZHANG Fumin,QU Xinghua,et al. pose measurement and online error compensation of KUKA industrial robot[J]. Journal of Mechanical Engineering,2017,53(8):1-7.
[7] MENG F,YANG G,YANG J,et al. Error analysis of normal surface measurements based on multiple laser displacement sensors[J]. Sensors,2024,24(7):2059.
[8] GUO Z,LU,JIAO T,et al. Error analysis and modeling of a large component assembly monitoring system based on multi-sensor fusion[J]. Sensors,2024,24(18):1-9.
[9] XIE S. Research on the industrial robot grasping method based on multisensor data fusion and binocular vision[J]. Computational Intelligence and Neuroscience,2022,2022:1-7.
[10] 朱永国. 基于三维力传感器的飞机翼身对合干涉检测与位置修正[D]. 南昌:南昌航空大学,2022. ZHU Yongguo. Interference detection and position correction of aircraft wing-body engagement based on three-dimensional force sensor[D]. Nanchang:Nanchang Hangkong University,2022.
[11] 黄翔. 基于激光测距和机器视觉的飞机大部件对接技术研究[D]. 南京:南京航空航天大学,2019. HUANG Xiang. Research on aircraft large parts docking technology based on laser ranging and machine vision[D]. Nanjing:Nanjing University of Aeronautics and Astronautics,2019.
[12] WANG Z,YAN J. Multi-sensor fusion based industrial action recognition method under the environment of intelligent manufacturing[J]. Journal of Manufacturing Systems,2024,74:575-586.
[13] RANI K,KUMAR N. Intelligent controller for hybrid force and position control of robot manipulators using RBF neural network[J]. International Journal of Dynamics and Control,2019,7(2):767-775.
[14] LIU H,ZHU W,KE Y. Pose alignment of aircraft structures with distance sensors and CCD cameras[J]. Robotics and Computer-Integrated Manufacturing,2017,48:30-38.
[15] ZHA Q,ZHU Y,ZHANG W. Visual and automatic wing-fuselage docking based on data fusion of heterogeneous measuring equipments[J]. Journal of the Chinese Institute of Engineers,2021,44(8):792-802.
[16] JIANG Z,WEI P,DU Y,et al. A virtual assembly technology for virtual-real fusion interaction of ship structure based on three-level collision detection[J]. Journal of Marine Science and Engineering,2024,12(11):10-19.
[17] 王南山,蔡娇坤,乔嘉璐,等. 基于RobotStudio的齿轮减速器虚拟装配工作站仿真研究[J]. 机电工程技术,2024,53(10):167-171. WANG Nanshan,CAI Jiaokun,QIAO Jialu,et al. Simulation research on virtual assembly workstation of gear reducer based on RobotStudio[J]. Electromechanical Engineering Technology,2024,53(10):167-171.
[18] GUO M,GUO S,MENG Q,et al. Intelligent virtual trial assembly of prefabricated frame structures for large and complex construction scenes[J]. Automation in Construction,2025,172:1-8.
[19] 余志伟. 基于Solid Works的针型阀虚拟设计与干涉检查[J]. 机械管理开发,2024,39(9):294-296. YU Zhiwei. Virtual design and interference check of needle valve based on Solid Works[J]. Mechanical Management Development,2024,39(9):294-296.
[20] WANG B,YOU W. Virtual assembly collision detection algorithm using backpropagation neural network[J]. Computers,Materials & Continua,2024,81(1):1085-1100.
[21] 吴德烽,李爱国. 三维表面扫描机器人误差建模与补偿方法[J]. 机械工程学报,2012,48(13):61-67. WU Defeng,LI Aiguo. Error modeling and compensation method of 3D surface scanning robot[J]. Journal of Mechanical Engineering,2012,48(13):61-67.
[22] SUN H. Integrating virtual resequencing with car resequencing via selectivity banks for mixed-model assembly lines[J]. Computers & Industrial Engineering,2024,189:1-10.
[23] 张莹,丁宁. 基于虚拟现实技术的船舶装配工艺仿真优化[J]. 舰船科学技术,2024,46(7):171-174. ZHANG Ying,DING Ning. Simulation and optimization of ship assembly process based on virtual reality technology[J]. Ship Science and Technology,2024,46(7):171-174.
[24] 吴学毅,2024. 发动机虚拟装配序列与路径规划研究[D]. 西安:西安理工大学,2024. WU Xueyi,2024. Research on virtual assembly sequence and path planning of engine[D]. Xi'an:Xi'an University of Technology,2024.
[25] 胡权. 基于UE4的虚拟装配技术研究与实现[D]. 成都:电子科技大学,2024. HU Quan. Research and implementation of virtual assembly technology based on UE4[D]. Chengdu:University of Electronic Science and Technology of China,2024.
[26] 刘大同,郭凯,王本宽,等. 数字孪生技术综述与展望[J]. 仪器仪表学报,2018,39(11):1-10. LIU Datong,GUO Kai,WANG Benkuan,et al. Overview and prospect of digital twinning technology[J]. Chinese Journal of Scientific Instrument,2018,39(11):1-10.
[27] 庄存波,刘检华,熊辉,等. 产品数字孪生体的内涵、体系结构及其发展趋势[J]. 计算机集成制造系统,2017,23(4):753-768. ZHUANG Cunbo,LIU Jianhua,XIONG Hui,et al. Connotation,architecture and development trend of product digital twins[J]. Computer Integrated Manufacturing System,2017,23(4):753-768.
[28] 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,2024,9(36):2181-2192. XU Jian,LIU Gaofeng,ZHAO Yijian,et al. Synchronization and grasping method of digital twins of assembly robot[J]. Journal of System Simulation,2024,9(36):2181-2192.
[29] 刘治红. 基于数字孪生的装配车间孪生体的快速构建与虚实同步研究[D]. 绵阳:西南科技大学,2024. LIU Zhihong. Rapid construction of twins in assembly workshop based on digital twins and research on synchronization between reality and reality[D]. Mianyang:Southwest University of Science and Technology,2024.
[30] 刘达新,王科,刘振宇,等. 基于数据融合与知识推理的机器人装配单元数字孪生建模方法研究[J]. 机械工程学报,2024,60(5):36-50. LIU Daxin,WANG Ke,LIU Zhenyu,et al. Research on digital twin modeling method of robot assembly unit based on data fusion and knowledge reasoning[J]. Journal of Mechanical Engineering,2024,60(5):36-50.
[31] 郭飞燕,刘检华,邹方,等. 数字孪生驱动的装配工艺设计现状及关键实现技术研究[J]. 机械工程学报,2019,55(17):110-132. GUO Feiyan,LIU Jianhua,ZOU Fang,et al. Research on assembly process design status and key realization technology of digital twin drive[J]. Journal of Mechanical Engineering,2019,55(17):110-132.
[32] 孙文潇,王健,梁周雁,等. 法线特征约束的激光点云精确配准[J]. 武汉大学学报(信息科学版),2020,45(7):988-995. SUN Wenxiao,WANG Jian,LIANG Zhouyan,et al. Accurate registration of laser point clouds constrained by normal features[J]. Journal of Wuhan University (Information Science Edition),2020,45(7):988-995.
[33] YANG M,LEE E. Segmentation of measured point data using a parametric quadric surface approximation[J]. Computer-Aided Design,1999,31(7):449-457.