复杂构件缺陷机器人修复加工技术研究进展

doi:10.3901/JME.2025.11.001

摘要/Abstract

摘要： 针对交通运输、航空航天、能源国防等高端装备制造领域复杂构件表面缺陷高效高品质修复加工重大需求，对近年来以机器人为制造装备执行体的机器人修复加工技术的研究进展进行了综述。具体围绕机器人修复所涉及的缺陷视觉测量、路径决策规划、加工质量控制等关键技术，系统分析了国内外已公开发表的相关文献，并以汽车车身、高铁车身与发动机叶片为例，阐述了机器人缺陷修复工程应用。最后从多机器人协同、在线信息交互、动态性能监控、混合加工工艺等方面对该领域未来研究方向进行了展望。

关键词: 机器人修复加工, 复杂构件, 视觉测量, 路径决策, 质量控制

Abstract: To address the significant demand for efficient, high-quality repair and machining of surface defects of complex components in high-end equipment manufacturing fields such as transportation, aerospace, energy and defense, the research progress in recent years on robotic repair and machining technology is reviewed. This research systematically analyzes the relevant literature published at domestic and international level around the key technologies of defect visual measurement, path decision planning, and machining quality control involved in robotic repair. It also describes the engineering applications of robotic repair by taking automotive body, high-speed rail body and turbine blade as examples. Finally, the future research directions of this field are envisioned from the aspects of multi-robot collaboration, online information interaction, dynamic performance monitoring, and hybrid machining process.

Key words: robotic repair machining, complex components, vision measurement, path decision, quality control

中图分类号:

TG156

朱大虎, 王升哲, 徐子嫣, 王宜丹, 华林. 复杂构件缺陷机器人修复加工技术研究进展[J]. 机械工程学报, 2025, 61(11): 1-22.

ZHU Dahu, WANG Shengzhe, XU Ziyan, WANG Yidan, HUA Lin. Research Progress in Robotic Repair Machining of Complex Component Defects[J]. Journal of Mechanical Engineering, 2025, 61(11): 1-22.

参考文献

[1] 郭东明. 高性能制造[J]. 机械工程学报，2022，58(21)：225-242. GUO Dongming. High performance manufacturing[J]. Journal of Mechanical Engineering，2022，58(21)：225-242.
[2] 王海斗，张文宇，宋巍. 再制造二十年足迹及发展趋势[J]. 机械工程学报，2023，59(20)：80-95. WANG Haidou，ZHANG Wenyu，SONG Wei. Twenty years of remanufacturing footprint and development trends[J]. Journal of Mechanical Engineering，2023，59(20)：80-95.
[3] PENG X，KONG L B. Detection and characterization of defects in additive manufacturing by polarization-based imaging system[J]. Chinese Journal of Mechanical Engineering，2023，36(1)：110.
[4] 何赟泽，李响，王洪金，等. 基于可见光和热成像的风机叶片全周期无损检测综述[J]. 机械工程学报，2023，59(6)：32-45. HE Yunze，LI Xiang，WANG Hongjin，et al. A review：Full-cycle nondestructive testing based on visible light and thermography of wind turbine blade[J]. Journal of Mechanical Engineering，2023，59(6)：32-45.
[5] AMERI R，HSU C C，BAND S S. A systematic review of deep learning approaches for surface defect detection in industrial applications[J]. Engineering Applications of Artificial Intelligence，2024，130：107717.
[6] 甘纪强，王小平. 基于虚拟样本生成的铺丝表面缺陷检测[J]. 航空学报，2024，45(1)：428624. GAN Jiqiang，WANG Xiaoping. Surface defect detection of fiber placement based on virtual sample generation[J]. Acta Aeronautica et Astronautica Sinica，2024，45(1)：428624.
[7] PIRES J N，AZAR A S，NOGUEIRA F，et al. The role of robotics in additive manufacturing：review of the AM processes and introduction of an intelligent system[J]. Industrial Robot，2022，49(2)：311-331.
[8] AZAMFIREI V，PSAROMMATIS F，LAGROSEN Y. Application of automation for in-line quality inspection，a zero-defect manufacturing approach[J]. Journal of Manufacturing Systems，2023，67：1-22.
[9] XU L D，XU E L，LI L. Industry 4.0：state of the art and future trends[J]. International Journal of Production Research，2018，56(8)：2941-2962.
[10] 周济. 智能制造——“中国制造2025”的主攻方向[J]. 中国机械工程，2015，26(17)：2273-2284. ZHOU Ji. Intelligent manufacturing——Main direction of “Made in China 2025”[J]. China Mechanical Engineering，2015，26(17)：2273-2284.
[11] SHENTU S Z，GONG Z，LIU X J，et al. Hybrid navigation system based autonomous positioning and path planning for mobile robots[J]. Chinese Journal of Mechanical Engineering，2022，35(1)：109.
[12] MEI B，XIE F G，LIU X J，et al. A mobile hybrid robot and its accuracy issue in machining of large-scale structures[J]. IEEE/ASME Transactions on Mechatronics，2024，29(1)：347-357.
[13] ZHAO X W，TAO B，DING H. Multimobile robot cluster system for robot machining of large-scale workpieces[J]. IEEE/ASME Transactions on Mechatronics，2022，27(1)：561-571.
[14] YANG J X，QI Q，ADILI D，et al. An analytical tool path smoothing algorithm for robotic machining with the consideration of redundant kinematics[J]. Robotics and Computer-Integrated Manufacturing，2024，89：102768.
[15] FENG H J，REN X K，LI L F，et al. A novel feature-guided trajectory generation method based on point cloud for robotic grinding of freeform welds[J]. The International Journal of Advanced Manufacturing Technology，2021，115(5-6)：1763-1781.
[16] CHEN Y X，LU Y A，DING Y. Toolpath generation for robotic flank milling via smoothness and stiffness optimization[J]. Robotics and Computer-Integrated Manufacturing，2023，85：102640.
[17] 关英姿，刘文旭，焉宁，等. 空间多机器人协同运动规划研究[J]. 机械工程学报，2019，55(12)：37-43. GUAN Yingzi，LIU Wenxu，YAN Ning，et al. Research on cooperative motion planning of space multi-robots[J]. Journal of Mechanical Engineering，2019，55(12)：37-43.
[18] QU J D，ZHANG F H，WANG Y，et al. Human-like coordination motion learning for a redundant dual-arm robot[J]. Robotics and Computer-Integrated Manufacturing，2019，57：379-390.
[19] SONG Y B，TIAN W J，TIAN Y L，et al. A task-oriented calibration method for a 5-DOF hybrid machining robot based on the equivalent system[J]. Measurement，2023，216：112909.
[20] ZHANG Z Y，XIAO J L，LIU H T，et al. Base placement optimization of a mobile hybrid machining robot by stiffness analysis considering reachability and nonsingularity constraints[J]. Chinese Journal of Aeronautics，2023，36(11)：398-416.
[21] SUN Y W，JIA J J，XU J T，et al. Path，feedrate and trajectory planning for free-form surface machining：A state-of-the-art review[J]. Chinese Journal of Aeronautics，2022，35(8)：12-29.
[22] XU L K，ZHANG D S，XU J T，et al. A stiffness matching-based deformation errors control strategy for dual-robot collaborative machining of thin-walled parts[J]. Robotics and Computer-Integrated Manufacturing，2024，88：102726.
[23] XIAO G J，LIU X T，SONG K K，et al. Research on robotic belt grinding method of blisk for obtaining high surface integrity features with variable inclination angle force control[J]. Robotics and Computer-Integrated Manufacturing，2024，86：102680.
[24] XIAO G J，CHEN S L，SONG K K，et al. A novel trajectory planning method based on reverse compensation of profile error for robotic belt grinding of blisk[J]. Journal of Manufacturing Processes，2022，84：508-521.
[25] 张铁，叶景杨，刘晓刚. 面向机器人砂带打磨的加权手眼标定算法[J]. 机械工程学报，2018，54(17)：142-148. ZHANG Tie，YE Jingyang，LIU Xiaogang. Weighted hand-eye calibration algorithm for robot grinding[J]. Journal of Mechanical Engineering，2018，54(17)：142-148.
[26] LIAO Z Y，WU J Z，WU H M，et al. Profile error estimation and hierarchical compensation method for robotic surface machining[J]. IEEE Robotics and Automation Letters，2024，9(4)：3195-3202.
[27] ZHANG Y，LIU H D，CHENG W K，et al. A novel trajectory planning method for robotic deburring of automotive castings considering adaptive weights[J]. Robotics and Computer-Integrated Manufacturing，2024，86：102677.
[28] TIAN D Z，WU H，ZHANG Y，et al. An optimal reference iteration-based surface reconstruction framework for robotic grinding of additively repaired blade with local deformation[J]. Robotics and Computer-Integrated Manufacturing，2024，88：102737.
[29] ZHU G，CAI M，WANG Z J，et al. A new calibration method for a dynamic coordinate system in a robotic blade grinding and polishing system based on the six-point limit principle[J]. Robotics and Computer-Integrated Manufacturing，2023，83：102561.
[30] ZHANG H Y，LI L，ZHAO J B，et al. The hybrid force/position anti-disturbance control strategy for robot abrasive belt grinding of aviation blade base on fuzzy PID control[J]. The International Journal of Advanced Manufacturing Technology，2021，114(11-12)：3645-3656.
[31] 王卓群，谢福贵，解增辉，等. 机器人化装备测量-加工一体化技术现状及展望[J]. 机器人，2024，46(1)：118-128. WANG Zhuoqun，XIE Fugui，XIE Zenghui，et al. Technical status and prospect of measurement and machining integration technology for robotized equipment[J]. Robot，2024，46(1)：118-128.
[32] 孙龙飞，房立金，梁风勇. 新型工业机器人结构设计及其全域刚度预估方法[J]. 机器人，2018，40(5)：673-684. SUN Longfei，FANG Lijin，LIANG Fengyong. Structure design and global stiffness prediction method of a novel industrial robot[J]. Robot，2018，40(5)：673-684.
[33] CHEN Q Z，ZHANG C R，HU T L，et al. Posture optimization in robotic machining based on comprehensive deformation index considering spindle weight and cutting force[J]. Robotics and Computer-Integrated Manufacturing，2022，74：102290.
[34] POZZI M，ACHILLI G M，VALIGI M C，et al. Modeling and simulation of robotic grasping in simulink through simscape multibody[J]. Frontiers in Robotics and AI，2022，9：873558.
[35] JIN X，YE W，LI Q C. New indices for performance evaluation of cable-driven parallel robots：Motion/force transmissibility[J]. Mechanism and Machine Theory，2023，188：105402.
[36] GUO K，ZHANG Y R，SUN J. Towards stable milling：Principle and application of active contact robotic milling[J]. International Journal of Machine Tools and Manufacture，2022，182：103952.
[37] TAO B，ZHAO X W，DING H. Mobile-robotic machining for large complex components：A review study[J]. Science China Technological Sciences，2019，62(8)： 1388-1400.
[38] CAI J Q，LEI T. An autonomous positioning method of tube-to-tubesheet welding robot based on coordinate transformation and template matching[J]. IEEE Robotics and Automation Letters，2021，6(2)：787-794.
[39] 崔晓磊，贺久强，韩聪，等. 复杂异形空心构件协同变载液压成形技术[J]. 机械工程学报，2023，59(20)：143-153. CUI Xiaolei，HE Jiuqiang，HAN Cong，et al. Hydroforming technology of complex special-shaped hollow components by collaborative and variable loadings[J]. Journal of Mechanical Engineering，2023，59(20)：143-153.
[40] 谢福贵，梅斌，刘辛军，等. 一种大型复杂构件加工新模式及新装备探讨[J]. 机械工程学报，2020，56(19)：70-78. XIE Fugui，MEI Bin，LIU Xinjun，et al. Novel mode and equipment for machining large complex components[J]. Journal of Mechanical Engineering，2020，56(19)：70-78.
[41] VEMPRALA S H，BONATTI R，BUCKER A，et al. ChatGPT for robotics：Design principles and model abilities[J]. IEEE Access，2024，12：55682-55696.
[42] SU Y T，YAN P，YI R Z，et al. A cascaded combination method for defect detection of metal gear end-face[J]. Journal of Manufacturing Systems，2022，63：439-453.
[43] KANISHKA K，ACHERJEE B. A systematic review of additive manufacturing-based remanufacturing techniques for component repair and restoration[J]. Journal of Manufacturing Processes，2023，89：220-283.
[44] ZENG C，SU H，LI Y N，et al. An approach for robotic leaning inspired by biomimetic adaptive control[J]. IEEE Transactions on Industrial Informatics，2022，18(3)：1479-1488.
[45] PERVEZ M R，AHAMED M H，AHMED M A，et al. Autonomous grinding algorithms with future prospect towards SMART manufacturing：A comparative survey[J]. Journal of Manufacturing Systems，2022，62：164-185.
[46] ZHANG J D，XU J B，ZHU L Y，et al. An improved MobileNet-SSD algorithm for automatic defect detection on vehicle body paint[J]. Multimedia Tools and Applications，2020，79(31-32)：23367-23385.
[47] LI L L，REN J，WANG P，et al. Defect detection method for high-resolution weld based on wandering Gaussian and multi-feature enhancement fusion[J]. Mechanical Systems and Signal Processing，2023，199：110484.
[48] XIAO R Q，XU Y L，HOU Z，et al. A novel visual guidance framework for robotic welding based on binocular cooperation[J]. Robotics and Computer- Integrated Manufacturing，2022，78：102393.
[49] OU J，ZOU L，WAN Q H，et al. Weld-seam identification and model reconstruction of remanufacturing blade based on three-dimensional vision[J]. Advanced Engineering Informatics，2021，49：101300.
[50] REN Z H，FANG F Z，YAN N，et al. State of the art in defect detection based on machine vision[J]. International Journal of Precision Engineering and Manufacturing - Green Technology，2022，9(2)：661-691.
[51] 赵朗月，吴一全. 基于机器视觉的表面缺陷检测方法研究进展[J]. 仪器仪表学报，2022，43(1)：198-219. ZHAO Langyue，WU Yiquan. Research progress of surface defect detection methods based on machine vision[J]. Chinese Journal of Scientific Instrument，2022，43(1)：198-219.
[52] YUAN X C，WU L S，PENG Q J. An improved Otsu method using the weighted object variance for defect detection[J]. Applied Surface Science，2015，349：472-484.
[53] WU X F，WANG C S，TIAN Z Z，et al. Research on belt deviation fault detection technology of belt conveyors based on machine vision[J]. Machines，2023，11(12)：1039.
[54] ASHIBA H I，ASHIBA M I. Novel proposed technique for automatic fabric defect detection[J]. Multimedia Tools and Applications，2023，82(20)：30783-30806.
[55] KONG Q M，WU Z H，SONG Y T. Online detection of external thread surface defects based on an improved template matching algorithm[J]. Measurement，2022，195：111087.
[56] 郭萌，胡辽林，赵江涛. 基于Kirsch和Canny算子的陶瓷碗表面缺陷检测方法[J]. 光学学报，2016，36(9)：27-33. GUO Meng，HU Liaolin，ZHAO Jiangtao. Surface defect detection method of ceramic bowl based on Kirsch and Canny operator[J]. Acta Optica Sinica，2016，36(09)：27-33.
[57] PASADAS D J，BASKARAN P，RAMOS H G，et al. Detection and classification of defects using ECT and multi-level SVM model[J]. IEEE Sensors Journal，2020，20(5)：2329-2338.
[58] XU J Y，YU H W，XIAO R Q，et al. Features detection of Al alloy porosity during GTAW process based on arc spectrum and improved porosity-focus decision tree[J]. Journal of Manufacturing Processes，2023，88：71-83.
[59] ZHANG Z F，YANG Z，REN W J，et al. Random forest-based real-time defect detection of Al alloy in robotic arc welding using optical spectrum[J]. Journal of Manufacturing Processes，2019，42：51-59.
[60] FU L L，YANG J S，LI S，et al. Artificial neural network-based damage detection of composite material using laser ultrasonic technology[J]. Measurement，2023，220：113435.
[61] LIU R Q，HUANG M，GAO Z M，et al. MSC-DNet：An efficient detector with multi-scale context for defect detection on strip steel surface[J]. Measurement，2023，209：112467.
[62] HU C Q，DUAN Y X，LIU S C，et al. LSTM-RNN-based defect classification in honeycomb structures using infrared thermography[J]. Infrared Physics & Technology，2019，102：103032.
[63] LIU B H，ZHANG T R，YU Y，et al. A data generation method with dual discriminators and regularization for surface defect detection under limited data[J]. Computers in Industry，2023，151：103963.
[64] ZHANG S W，HE M Q，ZHONG Z Y，et al. An industrial interference-resistant gear defect detection method through improved YOLOv5 network using attention mechanism and feature fusion[J]. Measurement，2023，221：113433.
[65] LI D W，XIE Q，GONG X X，et al. Automatic defect detection of metro tunnel surfaces using a vision-based inspection system[J]. Advanced Engineering Informatics，2021，47：101206.
[66] ZHU D H，QIAN C，QU C，et al. An improved SegNet network model for accurate detection and segmentation of car body welding slags[J]. International Journal of Advanced Manufacturing Technology，2022，120(1-2)：1095-1105.
[67] GU Z P，ZHU B K，ZHU G B，et al. Anomalygpt：Detecting industrial anomalies using large vision-language models[C]// Proceedings of the AAAI Conference on Artificial Intelligence，Vancouver，AAAI Press，2024，38(3)：1932-1940.
[68] LU S F，ZHANG J，HAO F，et al. Automatic detection of chip pin defect in semiconductor assembly using vision measurement[J]. Measurement Science Review，2022，22(5)：231-240.
[69] MOE S，GRAVDAHL J T，PETTERSEN K Y. Set-based control for autonomous spray painting[J]. IEEE Transactions on Automation Science and Engineering，2018，15(4)：1785-1796.
[70] WANG T，WANG Z J，CAO Y，et al. A multi-BRIEF-descriptor stereo matching algorithm for binocular visual sensing of fillet welds with indistinct features[J]. Journal of Manufacturing Processes，2021，66：636-650.
[71] SHANG H，LIU C Y，WANG R J. Measurement methods of 3D shape of large-scale complex surfaces based on computer vision：A review[J]. Measurement，2022，197：111302.
[72] XU J，ZHANG S. Status，challenges，and future perspectives of fringe projection profilometry[J]. Optics and Lasers in Engineering，2020，135：106193.
[73] WANG Z Y，ZHU D H. An accurate detection method for surface defects of complex components based on support vector machine and spreading algorithm[J]. Measurement，2019，147：106886.
[74] ARNAL L，SOLANES J E，MOLINA J，et al. Detecting dings and dents on specular car body surfaces based on optical flow[J]. Journal of Manufacturing Systems，2017，45：306-321.
[75] CHEN L M，ZHANG S. Large depth-of-field microscopic structured-light 3D imaging with focus stacking[J]. Optics and Lasers in Engineering，2023，167：107623.
[76] HAN H，WU S Q，SONG Z. Curved LCD based deflectometry method for specular surface measurement[J]. Optics and Lasers in Engineering，2022，151：106909.
[77] XIE Y L，ZHU Z M，LI M C. Joint polarization-color coding strategy based on Stokes parameter for robust strip-edge detection in 3D measurement[J]. Measurement，2023，221：113493.
[78] ZHOU L，SUN G X，LI Y，et al. Point cloud denoising review：from classical to deep learning-based approaches[J]. Graphical Models，2022，121：101140.
[79] WANG G L，WU L S，HU Y，et al. Point cloud simplification algorithm based on the feature of adaptive curvature entropy[J]. Measurement Science and Technology，2021，32(6)：065004.
[80] LIU C Y，AN B W，HOU Y G，et al. A novel scratch detection and measurement method for automotive stamping parts[J]. IEEE Transactions on Instrumentation and Measurement，2022，71：1-13.
[81] XIE Q，LU D N，DU K P，et al. Aircraft skin rivet detection based on 3D point cloud via multiple structures fitting[J]. Computer-Aided Design，2020，120：102805.
[82] ZONG Y L，LIANG J，WANG H，et al. An intelligent and automated 3D surface defect detection system for quantitative 3D estimation and feature classification of material surface defects[J]. Optics and Lasers in Engineering，2021，144：106633.
[83] LEE E T，FAN Z Y，SENCER B. A new approach to detect surface defects from 3D point cloud data with surface normal Gabor filter (SNGF)[J]. Journal of Manufacturing Processes，2023，92：196-205.
[84] ZHANG X C，LI W，LIOU F. Damage detection and reconstruction algorithm in repairing compressor blade by direct metal deposition[J]. The International Journal of Advanced Manufacturing Technology，2018，95(5)：2393-2404.
[85] YU N G，LI H Z，XU Q，et al. 3D reconstruction and defect pattern recognition of bonding wire based on stereo vision[J]. CAAI Transactions on Intelligence Technology，2024，9(2)：348-364.
[86] HUO L T，LIU Y，YANG Y T，et al. Review：Research on product surface quality inspection technology based on 3D point cloud[J]. Advances in Mechanical Engineering，2023，15(3)：16878132231159523.
[87] ZHANG C，CUI J，WU J G，et al. Attention mechanism and texture contextual information for steel plate defects detection[J]. Journal of Intelligent Manufacturing，2024，35(5)：2193-2214.
[88] LIU S，SUN H L，ZHANG Z X，et al. A multiscale deep feature for the instance segmentation of water leakages in tunnel using MLS point cloud intensity images[J]. IEEE Transactions on Geoscience and Remote Sensing，2022，60：1-16.
[89] LI H J，HAO K R，WEI B，et al. A reliable solder joint inspection method based on a light-weight point cloud network and modulated loss[J]. Neurocomputing，2022，488：315-327.
[90] HU Q M，HAO K R，WEI B，et al. An efficient solder joint defects method for 3D point clouds with double-flow region attention network[J]. Advanced Engineering Informatics，2022，52：101608.
[91] LV Y J，PENG Z，QU C，et al. An adaptive trajectory planning algorithm for robotic belt grinding of blade leading and trailing edges based on material removal profile model[J]. Robotics and Computer-Integrated Manufacturing，2020，66：101987.
[92] WU C Q，YANG P W，LEI T，et al. A teaching-free welding position guidance method for fillet weld based on laser vision sensing and EGM technology[J]. Optik，2022，262：169291.
[93] 涂秋平. 随机微小车身焊渣精准检测及机器人磨抛路径自主决策[D]. 武汉：武汉理工大学，2022. TU Qiuping. Accurate detection of random small car body welding slags and autonomous decision-making of robotic grinding path[D]. Wuhan：Wuhan University of Technology，2022.
[94] IMAM H Z，AL-MUSAIBELI H，ZHENG Y F，et al. Vision-based spatial damage localization method for autonomous robotic laser cladding repair processes[J]. Robotics and Computer-Integrated Manufacturing，2023，80：102452.
[95] ZHU D H，FENG X Z，XU X H，et al. Robotic grinding of complex components：A step towards efficient and intelligent machining - challenges，solutions，and applications[J]. Robotics and Computer-Integrated Manufacturing，2020，65：101908.
[96] 王志远. 视觉引导的机器人自适应磨抛技术及其在叶片损伤修复中的应用[D]. 武汉：武汉理工大学，2021. WANG Zhiyuan. Visually-guided adaptive robotic belt grinding technology and its application in repairing damaged blades[D]. Wuhan：Wuhan University of Technology，2021.
[97] ZHANG Y，QIAO J，ZHANG G Y，et al. Artificial intelligence-assisted repair system for structural and electrical restoration using 3D printing[J]. Advanced Intelligent Systems，2022，4(10)：2200162.
[98] WANG P H，XIANG Q Y，HU Y B，et al. Contour-guided zigzag path planning for the automation of pothole spray repair based on the depth image[J]. International Journal of Pavement Engineering，2023，24(2)：2036339.
[99] XU K，LI Y G，XIANG B F. Image processing-based contour parallel tool path optimization for arbitrary pocket shape[J]. The International Journal of Advanced Manufacturing Technology，2019，102(5)：1091-1105.
[100] ZENG Y，YU Y Q，ZHAO X Y，et al. Trajectory planning of spray gun with variable posture for irregular plane based on boundary constraint[J]. IEEE Access，2021，9：52902-52912.
[101] HUANG N D，JIN Y Q，LU Y A，et al. Spiral toolpath generation method for pocket machining[J]. Computers & Industrial Engineering，2020，139：106142.
[102] WANG S Z，XU Z Y，WU C Q，et al. Towards region-based robotic machining system from perspective of intelligent manufacturing：A technology framework with case study[J]. Journal of Manufacturing Systems，2023，70：451-463.
[103] ANDRADE F，LLOFRIU M，TANCO M M，et al. Active localization strategy for hypotheses pruning in challenging environments[J]. Journal of Intelligent & Robotic Systems，2022，106(2)：47.
[104] XIAO M B，DING Y，YANG G L. A model-based trajectory planning method for robotic polishing of complex surfaces[J]. IEEE Transactions on Automation Science and Engineering，2022，19(4)：2890-2903.
[105] MARTIN J G，MUROS F J，MAESTRE J M，et al. Multi-robot task allocation clustering based on game theory[J]. Robotics and Autonomous Systems，2023，161：104314.
[106] WANG G，LI W L，JIANG C，et al. Trajectory planning and optimization for robotic machining based on measured point cloud[J]. IEEE Transactions on Robotics，2022，38(3)：1621-1637.
[107] JIANG C，LI W L，LI W P，et al. A novel dual-robot accurate calibration method using convex optimization and Lie derivative[J]. IEEE Transactions on Robotics，2023，40：960-977.
[108] CHEN Y X，DING Y. Posture optimization in robotic flat-end milling based on sequential quadratic programming[J]. Journal of Manufacturing Science and Engineering，2023，145(6)：061001.
[109] ZHANG Z J，CHEN S Y，ZHU X P，et al. Two hybrid end-effector posture-maintaining and obstacle-limits avoidance schemes for redundant robot manipulators[J]. IEEE Transactions on Industrial Informatics，2020，16(2)：754-763.
[110] LEE J H，KIM S H，MIN B K. Posture optimization in robotic drilling using a deformation energy model[J]. Robotics and Computer-Integrated Manufacturing，2022，78：102395.
[111] JIAO J C，TIAN W，ZHANG L，et al. Variable stiffness identification and configuration optimization of industrial robots for machining tasks[J]. Chinese Journal of Mechanical Engineering，2022，35(1)：115.
[112] WANG L W，LIU Y，YU Y，et al. Optimization of redundant degree of freedom in robotic milling considering chatter stability[J]. The International Journal of Advanced Manufacturing Technology，2022，121(11)：8379-8394.
[113] LIAO Z Y，WANG Q H，XIE H L，et al. Optimization of robot posture and workpiece setup in robotic milling with stiffness threshold[J]. IEEE/ASME Transactions on Mechatronics，2022，27(1)：582-593.
[114] ZHAO Y T，GAN L，LI W G，et al. Path planning of spot welding robot based on multi-objective grey wolf algorithm[J]. Journal of Intelligent & Fuzzy Systems，2021，41(6)：6181-6189.
[115] WANG X W，YAN Y X，GU X S. Spot welding robot path planning using intelligent algorithm[J]. Journal of Manufacturing Processes，2019，42：1-10.
[116] ZHAO H，ZHANG B C，SUN J W，et al. Spot-welding path planning method for the curved surface workpiece of body-in-white based on a memetic algorithm[J]. The International Journal of Advanced Manufacturing Technology，2021，117(9)：3083-3100.
[117] XIE Y E，ZHANG Z D，WU X D，et al. Obstacle avoidance and path planning for multi-joint manipulator in a space robot[J]. IEEE Access，2020，8：3511-3526.
[118] 朱大虎，徐小虎，蒋诚，等. 复杂叶片机器人磨抛加工工艺技术研究进展[J]. 航空学报，2021，42(10)：524265. ZHU Dahu，XU Xiaohu，JlANG Cheng，et al. Research progress in robotic grinding technology for complex blades[J]. Acta Aeronautica et Astronautica Sinica，2021，42(10)：524265.
[119] 朱大虎，徐小虎，吴超群，等. 机器人磨抛理论、技术与应用[M]. 北京：化学工业出版，2024. ZHU Dahu，XU Xiaohu，WU Chaoqun，et al. Theory，technology and application of robotic grinding[M]. Beijing：Chemical Industry Press，2024.
[120] ZHANG B X，WU S J，WANG D Z，et al. A review of surface quality control technology for robotic abrasive belt grinding of aero-engine blades[J]. Measurement，2023，220：113381.
[121] 李杰. CFRP材料机器人磨抛加工质量智能预测方法研究[D]. 武汉：华中科技大学，2022. LI Jie. Research on intelligent prediction method of CFRP robot grinding and polishing quality[D]. Wuhan：Huazhong University of Science & Technology，2022.
[122] CHEN C，WANG Y，GAO Z T，et al. Intelligent learning model-based skill learning and strategy optimization in robot grinding and polishing[J]. Science China Technological Sciences，2022，65(9)：1957-1974.
[123] ZHOU H Y，ZHAO H，LI X F，et al. Accurate modeling of material removal depth in convolutional process grinding for complex surfaces[J]. International Journal of Mechanical Sciences，2024，267：109005.
[124] CHEN B，QI J，ZHANG D. An adaptive parameters adjustment and planning method for robotic belt grinding using modified quality model[J]. Proceedings of the Institution of Mechanical Engineers，Part B：Journal of Engineering Manufacture，2021，235(4)：605-615.
[125] PILLAI J U，SANGHRAJKA I，SHUNMUGAVEL M，et al. Optimisation of multiple response characteristics on end milling of aluminum alloy using Taguchi-Grey relational approach[J]. Measurement，2018，124：291-298.
[126] RAO K V，PARIMI S，RAJU L S，et al. Modelling and optimization of weld bead geometry in robotic gas metal arc-based additive manufacturing using machine learning，finite-element modelling and graph theory and matrix approach[J]. Soft Computing，2022，26(7)：3385-3399.
[127] 李杰，杨泽源，时强胜，等. 基于小样本数据驱动模型的橡胶材料机器人磨抛去除廓形预测方法[J]. 机械工程学报，2022，58(14)：15-24. LI Jie，YANG Zeyuan，SHI Qiangsheng，et al. Small sample data-driven model for material removal profile prediction[J]. Journal of Mechanical Engineering，2022，58(14)：15-24.
[128] KHALIPOURAZARI S，KHALILPOURAZARY S. Optimization of time，cost and surface roughness in grinding process using a robust multi-objective dragonfly algorithm[J]. Neural Computing and Applications，2020，32(8)：3987-3998.
[129] 夏绪辉，杨熠，王蕾，等. 基于涂层模型的再制造异形件涂油方法及工艺参数优化[J]. 机械工程学报，2018，54(13)：215-224. XIA Xuhui，YANG Yi，WANG Lei，et al. Spraying method and optimization of process parameters for remanufacturing profiled workpieces based on coating model[J]. Journal of Mechanical Engineering，2018，58(13)：215-224.
[130] CHEN T C T，WANG Y C. Evaluating innovative future robotic applications in manufacturing using a fuzzy collaborative intelligence approach[J]. The International Journal of Advanced Manufacturing Technology，2024，130(11)：6027-6041.
[131] AGUILERA R C，ACEVEDO F A，ORTIZ M P，et al. Robotic arm with BIoT machine learning system[J]. Fractals，2020，28(4)：2050088.
[132] ZHANG X X，ZHENG L Y，FAN W，et al. Knowledge graph and function block based Digital Twin modeling for robotic machining of large-scale components[J]. Robotics and Computer-Integrated Manufacturing，2024，85：102609.
[133] LIU P F，QIAN L，ZHAO X W，et al. Joint knowledge graph and large language model for fault diagnosis and its application in aviation assembly[J]. IEEE Transactions on Industrial Informatics，2024，20(6)：8160-8169.
[134] TIAN D Z，ZHUANG K J，ZHU D H. A technology framework for robotic profiling of blade edges based on model reconstruction and trajectory replanning[J]. Journal of Manufacturing Processes，2023，94：214-227.
[135] LIAO Z Y，WANG Q H，XU Z H，et al. Uncertainty-aware error modeling and hierarchical redundancy optimization for robotic surface machining[J]. Robotics and Computer-Integrated Manufacturing，2024，87：102713.
[136] XIE H，LI W L，ZHU D H，et al. A systematic model of machining error reduction in robotic grinding[J]. IEEE/ASME Transactions on Mechatronics，2020，25(6)：2961-2972.
[137] MA S D，HAO K，LU Y，et al. Error compensation method of industrial robots considering non-kinematic and weak rigid base errors[J]. Precision Engineering，2023，82：304-315.
[138] XIONG G，LI Z L，DING Y，et al. A closed-loop error compensation method for robotic flank milling[J]. Robotics and Computer-Integrated Manufacturing，2020，63：101928.
[139] NAMETALA C A L，SOUZA A M，PEREIRA B R，et al. A simulator based on artificial neural networks and NSGA-II for prediction and optimization of the grinding process of superalloys with high performance grinding wheels[J]. CIRP Journal of Manufacturing Science and Technology，2020，30：157-173.
[140] GE J，DENG Z H，LI Z Y，et al. Adaptive parameter optimization approach for robotic grinding of weld seam based on laser vision sensor[J]. Robotics and Computer-Integrated Manufacturing，2023，82：102540.
[141] PANDIYAN V，SHEVCHIK S，WASMER K，et al. Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques：A review[J]. Journal of Manufacturing Processes，2020，57：114-135.
[142] FERRARINI S，BILANCIA P，RAFFAELI R，et al. A method for the assessment and compensation of positioning errors in industrial robots[J]. Robotics and Computer-Integrated Manufacturing，2024，85：102622.
[143] ZHANG B B，WU J，WANG L P，et al. A method to realize accurate dynamic feedforward control of a spray-painting robot for airplane wings[J]. IEEE/ASME Transactions on Mechatronics，2018，23(3)：1182-1192.
[144] FENG X Z，LÜ R，QIAN C，et al. Estimation and compensation of angular misalignment at robot end brush roller-workpiece contact interface via elastic contact force perception[J]. International Journal of Advanced Manufacturing Technology，2022，121(1-2)：367-377.
[145] PANDIYAN V，MURUGAN P，TJAHJOWIDODO T，et al. In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning[J]. Robotics and Computer-Integrated Manufacturing，2019，57：477-487.
[146] FAN H L，LIU X，FUH J Y H，et al. Embodied intelligence in manufacturing：leveraging large language models for autonomous industrial robotics[J]. Journal of Intelligent Manufacturing，2024.
[147] PELLEGRINELLI S，PEDROCCHI N，TOSATTI L M，et al. Multi-robot spot-welding cells for car-body assembly：Design and motion planning[J]. Robotics and Computer-Integrated Manufacturing，2017，44：97-116.
[148] 徐嘉星. 高铁白车身腻子在线检测修磨技术研究及实现[D]. 武汉：华中科技大学，2022. XU Jiaxing. Research and implementation of online detection and polishing technology for high speed rail body-in-white putty[D]. Wuhan：Huazhong University of Science & Technology，2022.
[149] 冯晓志. 高铁白车身腻子涂层缺陷机器人测量-加工一体化技术研究[D]. 武汉：武汉理工大学，2024. FENG Xiaozhi. Research on robot measurement and machining integration for putty coating defects in high-speed rail body[D]. Wuhan：Wuhan University of Technology，2024.
[150] WANG S Z，XU Z Y，WANG Y D，et al. Model-enabled robotic machining framework for repairing paint film defects[J]. Robotics and Computer-Integrated Manufacturing，2024，89：102791.
[151] 李鹏涛，左洪福，肖文，等. 航空发动机叶片损伤及其修复技术研究与展望[J]. 航空学报，2024，45(18)：029635. LI Pengtao，ZUO Hongfu，XIAO Wen，et al. Research and prospect of aero engine blade damage and its repair technology[J]. Acta Aeronautica et Astronautica Sinica，2024，2024，45(18)：029635.
[152] XIE F G，CHONG Z H，LIU X J，et al. Precise and smooth contact force control for a hybrid mobile robot used in polishing[J]. Robotics and Computer-Integrated Manufacturing，2023，83：102573.
[153] WANG G，LI W L，JIANG C，et al. Simultaneous calibration of multicoordinates for a dual-robot system by solving the AXB= YCZ problem[J]. IEEE Transactions on Robotics，2021，37(4)：1172-1185.
[154] 李文龙，朱大虎，丁汉. 机器人加工视觉测量与力控技术[M]. 北京：科学出版社，2024. LI Wenlong，ZHU Dahu，DING Han. Visual measurement and force control technology for robotic machining [M]. Beijing：Science Press，2024.
[155] 贾爱亭，洪波，李湘文，等. 基于轨迹在线识别的3D折线焊缝机器人摆动GMAW实时跟踪方法[J]. 机械工程学报，2022，58(14)：116-125. JIA Aiting，HONG Bo，LI Xiangwen，et al. Real time tracking method of 3D Zigzag welding robot swing GMAW based on online trajectory detection[J]. Journal of Mechanical Engineering，2022，58(14)：116-125.
[156] 刘亚军，訾斌，王正雨，等. 智能喷涂机器人关键技术研究现状及进展[J]. 机械工程学报，2022，58(7)：53-74. LIU Yajun，ZI Bin，WANG Zhengyu，et al. Research progress and trend of key technology of intelligent spraying robot[J]. Journal of Mechanical Engineering，2022，58(7)：53-74.
[157] XU W X，MIAO H H，ZHAO Z B，et al. Multi-scale convolutional gated recurrent unit networks for tool wear prediction in smart manufacturing[J]. Chinese Journal of Mechanical Engineering，2021，34(1)：53.