航空制造关键零部件机器人自主检测技术应用及发展趋势

吴昊天; 陈红; 徐望; 樊绍胜; 童翊轩; 朴玄彬; 冯运; 王耀南

doi:10.16383/j.aas.c250712

航空制造关键零部件机器人自主检测技术应用及发展趋势

doi: 10.16383/j.aas.c250712 cstr: 32138.14/j.aas.c250712

吴昊天^{1, 2,},
陈红^1,,
徐望^3,,
樊绍胜^1,,
童翊轩^{2, 4,},
朴玄彬^5,,
冯运^{2, 4,},
王耀南^{2, 4,}

1.
长沙理工大学人工智能学院长沙 410114
2.
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.
湖南大学人工智能与机器人学院长沙 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(62293510/62293515,62303172), 江西省重点研发计划(20261BCE310010), 国家重点研发计划(2022YFB3303800), 湖南省自然科学基金(2023JJ40179), 长沙理工大学电力机器人科研平台开放基金(2025ZKPT060)资助

详细信息

作者简介:
吴昊天：长沙理工大学人工智能学院副研究员. 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

陈红：长沙理工大学人工智能学院硕士研究生. 主要研究方向为机器人数字孪生.E-mail: ch@csust.edu.cn

徐望：长沙理工大学电气与信息工程学院硕士研究生.主要研究方向为机器人三维视觉测量.E-mail: xuwang@csust.edu.cn

樊绍胜：长沙理工大学人工智能学院教授. 2005年获得湖南大学博士学位.主要研究方向为电力机器人, 电力设备状态监控Email: fss508@163.com

童翊轩：湖南大学人工智能与机器人学院博士研究生. 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

朴玄彬：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

冯运：湖南大学人工智能与机器人学院副教授.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

王耀南：中国工程院院士, 湖南大学人工智能与机器人学院教授. 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

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出版历程
- 收稿日期: 2025-12-11
- 录用日期: 2026-06-15
- 网络出版日期: 2026-09-08

Application and Development Trends of Autonomous Robot Inspection Technology for Key Components in Aviation Manufacturing

WU Hao-Tian^{1, 2
,},
CHEN Hong^1
,,
XU Wang^3
,,
FAN Shao-Sheng^1
,,
TONG Yi-Xuan^{2, 4
,},
PIAO Xuan-Bin^5
,,
FENG Yun^{2, 4
,},
WANG Yao-Nan^{2, 4
,}

1.
School of Artificial Intelligence, Changsha University of Science and Technology, Changsha 410114
2.
National Engineering Research Center for Robot Vision Perception and Control Technology, Changsha 410082
3.
School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114
4.
School of Artificial Intelligence and 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, 62303172), Key Research and Development Program of Jiangxi Province(20261BCE310010), National Key R-esearch and Development Program of China (2022YFB3303800), Natural Science Foundation of Hunan Province(2023JJ40179),an-d Open Research Fund of Science and Technology Innovation Platform of Electric Robots, Changsha University of Science and Technology(2025ZKPT060)

More Information

Author Bio:
WU Hao-Tian　Associate researcher at the School of Artificial Intelligence, Changsha University of Science and Technology. His interests include 3D measurement, point cloud registration, 3D reconstruction, and digital twins

CHEN Hong　Master student at the School of Artificial Intelligence, Changsha University of Science and Technology. Her main research interest is robot digital twins

XU Wang　Master student at the School of Electrical and Information Engineering, Changsha University of Science and Technology. His mian research interest is robot 3D vision measurement

FAN Shao-Sheng　Professor at the School of Artificial Intelligence, Changsha University of Science and Technology. He received his Ph.D. degree from Hunan Unversity in 2005. His research interests include power robotics and condition monitoring of power equipment

TONG Yi-Xuan　Ph.D. candidate at the School of Artificial Intelligence Robotics, Hunan university. He received his bachelor degree from Jiangnan University in 2024. His research interests includee robot digital twins, system modeling, and control theory

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 collaborative operation

FENG Yun　Associate professor at the School of Artificial Intelligence and Robotics, Hunan University. His research interests include robot digital twins, system modeling and operation and maintenance technology

WANG Yao-Nan　Academician at Chinese Academy o-f Engineering, professor at the School of Artificial Intellige-nce and Robotics, Hunan University. He received his Ph.D. degree from Hunan University in 1995. His research int-erests include robotics, intelligent control, and image proc-essing.Corresponding author of this paper

摘要

摘要: 航空制造业作为战略性高新技术产业, 在推动国民经济发展和保障国防安全中具有不可替代的作用. 本综述以航空发动机叶片、机身结构件等航空制造关键零部件为切入点, 先详细梳理目前国内外零部件检测技术的发展现状, 围绕航空制造中零部件表面信息的自主获取、航空发动机叶片等关键零部件的尺寸自主测量、飞机蒙皮和机身部件的缺陷自主检测等关键技术展开分析. 并从机器人智能制造系统的视角出发, 阐述智能制造的数据综合评价与数字孪生应用关键技术, 介绍航空制造生产线机器人自主检测系统的典型案例. 最后, 基于智能制造相关技术, 展望航空制造关键零部件机器人自主检测在自主感知、集群作业和人机协同等方面的发展趋势, 并进一步阐述落地的关键难点和潜在解决方案. 相关内容可为深化机器人化自主检测技术研究、精准推进智能制造提供理论参考.
- 航空制造关键零部件 /
- 表面信息获取 /
- 尺寸测量 /
- 缺陷检测 /
- 综合评价 /
- 数字孪生
Abstract: As a strategic high-tech industry, the aviation manufacturing plays an irreplaceable role in promoting national economic development and safeguarding national defense security. This paper focuses on key components in aviation manufacturing such as aircraft engine blades and airframe structural parts. It offers an overview of the global state of inspection technologies for such components and analyzes key technologies in aviation manufacturing, including autonomous surface information acquisition, autonomous dimensional measurement of key components (e.g., aircraft engine blades), and autonomous defect detection in aircraft skins and airframe components. From the perspective of robotic intelligent manufacturing systems, it elucidates key technologies for comprehensive data evaluation and digital twin applications in smart manufacturing, while presenting a case study of an autonomous robotic inspection system deployed in an aerospace production line. Finally, based on intelligent manufacturing technologies, it projects future trends in autonomous robotic inspection for key components in aviation manufacturing, covering autonomous perception, swarm operations, and human-machine collaboration, and further elaborates on key challenges and potential solutions for deployment. These findings provide theoretical references for deepening research into robotic autonomous inspection technologies and advancing intelligent manufacturing with precision.
- key components in aviation manufacturing /
- surface information acquisition /
- dimensional measurement /
- defect detection /
- comprehensive evaluation /
- digital twin

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图 1 技术路线图

Fig. 1 Technology roadmap

下载: 全尺寸图片幻灯片

图 2 三维图像扫描系统示意图

Fig. 2 Schematic diagram of the 3D image scanning system

下载: 全尺寸图片幻灯片

图 3 基于改进实例分割技术的零部件表面缺陷检测过程图

Fig. 3 Process diagram for component surface defect detection based on enhanced instance segmentation technology

下载: 全尺寸图片幻灯片

图 4 数字孪生功能应用图

Fig. 4 Digital twin function application diagram

下载: 全尺寸图片幻灯片

表 1 国内外航空制造关键零部件机器人自主检测技术对比分析

Table 1 Comparative analysis of autonomous robot inspection technologies for key components in domestic and international aviation manufacturing

对比维度	国外技术	国内技术
技术路线	非接触式测量为主, 注重设备与算法协同优化, 自主化技术成熟	非接触式与接触式结合, 设备研发成熟, 自主协同技术快速发展
精度	缺陷检测精度可达40 μm, 尺寸测量误差为0.05 mm	缺陷检测精度可达0.1 mm, 尺寸测量误差为0.08 mm
成本	设备成本高, 典型非接触式结构光/激光扫描机器人测量单元均价超500万元	设备成本适中, 同类型非接触式机器人测量单元均价为200~300万元 (不含定制化夹具与专用型激光跟踪仪)
应用场景	覆盖大型部件装配、复杂曲面测量等多场景, 适配性强	聚焦涡轮叶片、蒙皮等关键部件, 场景针对性强
产业化程度	规模化推广, 空客、波音等企业批量应用	试点应用为主, 商飞等企业逐步推广

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表 2 尺寸测量方法对比

Table 2 Comparison of dimension measurement methods

测量方法	典型精度	测量速度	适用场景	主要局限性
三坐标测量机	±1  μm	慢	实验室高精度检测	接触式、效率低、无法测柔性件
激光扫描仪	±10  μm	快	复杂曲面、中大型部件	高反光表面效果差、需多次转站
结构光投影	±5  μm	中	静态精细测量、叶片型面	对环境光敏感、动态场景适应性弱
摄影测量	±50  μm	快	超大部件全场变形测量	精度相对较低、依赖标志点
机器人携传感器测量	±20  μm	快	在线、柔性检测	标定复杂、累积误差控制难

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表 3 零部件表面缺陷检测方法对比

Table 3 Comparison of surface defect detection methods for components

检测算法	推理速度	适用缺陷类型	优缺点
YOLOv6^[91]	快	黑斑、划痕、凹坑(AP_S=0.308)	小目标检测有, 类别长尾分布有影响
改进的YOLOv8^[89]	快	裂纹(AP=0.981)、微孔(AP=0.926)	速度快、易部署; 泛化能力一般
Faster R-CNN^[97]	中等	划痕(AP=0.798)、碰伤(AP=0.821)、麻点(AP=0.752)	精度高; 速度慢、计算资源要求高
U-Net(分割网络) ^[90]	中等偏慢	裂纹、气孔、腐蚀等(mIoU=0.934)	像素级定位; 训练数据要求高、实时性较差
Global Prior Transformer Network^[88]	中等	裂纹、烧蚀、过热、刻痕等(mAP@0.5=0.849)	全局特征捕捉能力强;数据需求量大、计算复杂度高

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