A Review of Key Technologies for Automatic Assembly of Multi-branch Complex Pipelines

doi:10.3901/JME.260550

Abstract

Abstract: Multi-branch complex piping system is an important part of aerospace products, ships and automobiles, and its assembly quality directly determines the reliability and life of the products. To overcome the traditional manual assembly problems such as cumbersome manual operation, error-prone and low efficiency, the robot-centred assembly system provides a new idea for the automatic assembly of multi-branch complex piping, and gradually becomes a research hotspot in the field of intelligent assembly. Starting from the characteristics of the development and production of multi-branch complex pipeline, it can be seen through the analysis of its manufacturing process, the core of the multi-branch complex pipeline robot assembly is to solve the problems of pipeline detection and positioning, accurate robot grasping and collaborative assembly. Then, relevant research progress is reviewed from three aspects: intelligent perception, robot control, and automatic assembly, and the shortcomings of existing research are analyzed. Finally, it summarizes the technical challenges faced by the automatic assembly of multi-branch complex pipelines, and looks forward to the future development trend.

Key words: multi-branch complex pipelines, pose estimation, robot control, flexible assembly

CLC Number:

TP391

LIU Shaoli, HU Jia, LIU Jianhua, ZHANG Jiabo, ZHANG Bin, WANG Lifeng. A Review of Key Technologies for Automatic Assembly of Multi-branch Complex Pipelines[J]. Journal of Mechanical Engineering, 2026, 62(15): 221-243.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260550

http://www.cjmenet.com.cn/EN/Y2026/V62/I15/221

References

[1] ZHANG Z，SAADAT M. Multi-objective grasp pose optimisation for robotic 3D pipe assembly manipulation[J]. Robotics and Computer-Integrated Manufacturing，2022，76，102326.
[2] HU Jia，LIU Jianhua，LIU Shaoli，et al. Pipe pose estimation based on machine vision[J]. Measurement，2021，182，109585.
[3] 杜田. 航天动力管路装配偏差传递建模与分析[D]. 上海：上海交通大学，2022. DU Tian. Modeling and analysis of assembly deviation transmission for aerospace power pipeline[D]. Shanghai：Shanghai Jiao Tong University，2022.
[4] 李志强，于洋，刘艳，等. 管路焊接结构的随机振动疲劳损伤分析[J]. 航天器环境工程，2022，39(2)：125-132. LI Zhiqiang，YU Yang，LIU Yang，et al. Analysis of fatigue damage of welded pipe structure under random vibration[J]. Spacecraft Environment Engineering，2022，39(2)：125-132.
[5] 姚然，屈旋，刘富强，等. 航天产品飞行过程中管路系统工作异常的分析与研究[J]. 质量与可靠性，2019(6)：22-25，30. YAO Rang，QU Xuan，LIU Fuqiang，et al. Analysis and research of abnormal phenomena in flight process pipeline system during aerospace product flight[J]. Quality and Reliability，2019(6)：22-25，30.
[6] 中国载人航天. 美“猎鹰”9火箭发射货运飞船升空后爆炸[EB/OL]. (2015-06-29) [2025-03-17]. https://www.cmse.gov.cn/hqsy/lydt/htq/201506/t20150629_26638.html. China Manned Space. U.S. Falcon 9 rocket launching cargo spacecraft explodes after liftoff [EB/OL]. (2015-06-29) [2025-03-17]. https://www.cmse.gov.cn/hqsy/lydt/htq/201506/t20150629_26638.html.
[7] 广东海事. 船舶失控！都是操舵装置管路爆裂的“锅”？[EB/OL]. (2024-08-21) [2025-03-17]. https://www.xindemarinenews.com/topic/hangyunanquan/2024/0821/55991.html. Guangdong Maritime Safety Administration. Ship out of control! Is it the fault of burst steering gear piping? [EB/OL]. (2024-08-21) [2025-03-17]. https://www.xindemarinenews.com/topic/hangyunanquan/2024/0821/55991.html.
[8] SANTOS K R，VILLANI E，OLIVEIRA W R，et al. Comparison of visual servoing technologies for robotized aerospace structural assembly and inspection[J]. Robotics and Computer-Integrated Manufacturing，2022，73，102237.
[9] 董云龙，李祥飞，刘行，等. 航空航天领域机器人化智能装配技术综述[J]. 人工智能，2022(3)：6-20. DONG Yunlong，LI Xiangfei，LIU Xing，et al. Overview of robotized intelligent assembly technologies in aerospace sector[J]. Artificial Intelligence，2022(3)：6-20.
[10] 王治，刘检华，刘少丽，等. 基于双目视觉的管路位姿测量[J]. 计算机集成制造系统，2019，25(5)：1047-1054. WANG Zhi，LIU Jianhua，LIU Shaoli，et al. Accurate measurement method of pipeline pose based on binocular vision[J]. Computer Integrated Manufacturing Systems，2019，25(5)：1047-1054.
[11] GAN Yahui，DUAN Jinjun，CHEN Ming，et al. Multi-robot trajectory planning and position/force coordination control in complex welding tasks[J]. Applied Sciences，2019，9，924.
[12] 赵欢，葛东升，罗来臻，等. 大型构件自动化柔性对接装配技术综述[J]. 机械工程学报，2023，59(14)：277-297. ZHAO Huan，GE Dongsheng，LUO Laizhen，et al. Survey of automated flexible docking assembly technology for large-scale components[J]. Journal of Mechanical Engineering，2023，59(14)：277-297.
[13] JAVIER C M，BOGDANOVA I，PAQUIER B，et al. Scale invariant feature transform on the sphere：Theory and applications[J]. International Journal of Computer Vision，2012(98)：217-241.
[14] HERBERT B，TUYTELAARS T，GOOL L V. SURF：Speeded up robust features[C]// European Conference on Computer Vision，Graz：ECCV，2006：404-417.
[15] RUBLEE E，RABAUD V，KONOLIGE K，et al. ORB：An efficient alternative to SIFT or SURF[C]// IEEE Internal Conference on Computer Vision，Barcelona：ICCV，2011：2564-2571.
[16] RANGANATHAN A. The levenberg-marquardt algorithm[J]. Tutoral on LM Algorithm，2004，11(1)：101-110.
[17] LEPETIT V，MORENO-NOGUER F，FUA P. EPnP：An accurate O(n) solution to the PnP problem[J]. International Journal of Computer Vision，2009(81)：155-166.
[18] HINTERSTOISSER S，CAGNIART C，ILIC S，et al. Gradient response maps for real-time detection of texture-less objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2012，34(5)：876-888.
[19] HODAN T，ZABULIS X，LOURAKIS M，et al. Detection and fine 3D pose estimation of texture-less objects in RGB-D images[C]// IEEE International Conference on Intelligent Robots and Systems，Hamburg：IROS，2015：4421-4428.
[20] TJADEN H，SCHWANECKE U，SCHOMER E. Real-time monocular pose estimation of 3D objects using temporally consistent local color histograms[C]// IEEE International Conference on Computer Vision，Venice：ICCV，2017：124-132.
[21] WANG Shengchun，WANG Hao，ZHOU Yunlai，et al. Automatic laser profile recognition and fast tracking for structured light measurement using deep learning and template matching[J]. Measurement，2021，169，108362.
[22] ZHU Xicheng，HU Xiao，LI Dongyuan，et al. A dual evaluation multi-scale template matching algorithm based on wavelet transform[J]. Electronics Letters，2022(58)：145-147.
[23] XU Yang，LI Kunbo，WANG Jinge，et al. ER-Pose：Learning edge representation for 6D pose estimation of texture-less objects[J]. Neurocomputing，2023(515)：13-25.
[24] JOSIFOVSKI J，KERZEL M，PREGIZER C，et al. Object detection and pose estimation based on convolutional neural networks trained with synthetic data[C]// IEEE International Conference on Intelligent Robots and Systems，Madrid：IROS，2018：6269-6276.
[25] WANG G，MANHARDT F，TOMBARI F，et al. GDR-Net：Geometry-guided direct regression network for monocular 6D object pose estimation[C]// IEEE Conference on Computer Vision and Pattern Recognition，online：CVPR，2021：16611-16621.
[26] SVEIER A，EGELAND O. Dual quaternion particle filtering for pose estimation[J]. IEEE Transactions on Control Systems Technology，2021，29(5)：2012-2025.
[27] LIU Yuanpeng，ZHOU Laishui，ZONG Hua，et al. Regression-based three-dimensional pose estimation for texture-less objects[J]. IEEE Transactions on Multimedia，2019，21(11)：2776-2789.
[28] RAMBACH J，DENG Chengbiao，PAGANI A，et al. Learning 6doF object poses from synthetic single channel images[C]// IEEE International Symposium on Mixed and Augmented Reality Adjunct，Munich：ISMAR-Adjunct，2018：164-169.
[29] DI Yan，MANHARDT F，WANG Gu，et al. So-pose：Exploiting self-occlusion for direct 6D pose estimation[C]// IEEE International Conference on Computer Vision，Montreal：ICCV，2021：12396-12405.
[30] BENGTSON S H，ASTROM H，MOESLUND T B，et al. Pose estimation from RGB images of highly symmetric objects using a novel multi-pose loss and differential rendering[C]// IEEE International Conference on Intelligent Robots and Systems，Prague：IROS，2021：4618-4624.
[31] SOCK J，GARCIA-HERNANDO G，ARMAGAN A，et al. Introducing pose consistency and warp-alignment for self-supervised 6D object pose estimation in color images[C]// International Conference on 3D Vision，online：3DV，2020：291-300.
[32] WANG Guangming，ZHANG Chi，WANG Hesheng，et al. Unsupervised learning of depth，optical flow and pose with occlusion from 3D geometry[J]. IEEE Transactions on Intelligent Transportation Systems，2022，23(1)：308-320.
[33] WANG Gu，MANHARDT F，SHAO Jianzhun，et al. Self6D：Self-supervised monocular 6D object pose estimation[C]// European Conference on Computer Vision，online：ECCV，2020：108-125.
[34] WANG Gu，MANHARDT F，LIU Xingyu，et al. Occlusion-aware self-supervised monocular 6D object pose estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2024，46(3)：1788-1803.
[35] PEREZ-VILLAR J I B，GARCIA-MARTIN Á，BESCOS J，et al. Spacecraft pose estimation：Robust 2D and 3D structural losses and unsupervised domain adaptation by intermodel consensus[J]. IEEE Transactions on Aerospace and Electronic Systems，2024，60(3)：2515-2525.
[36] FISCHLER M A，BOLLES R C. Random sample consensus：A paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM，1981，24(6)：381-395.
[37] NING Xiao，YANG Beining，HUANG Si，et al. RGB-based set prediction transformer of 6D pose estimation for robotic grasping application[J]. IEEE Access，2024(12)：138047-138060.
[38] OBERWEGER M，RAD M，LEPETIT V. Making deep heatmaps robust to partial occlusions for 3D object pose estimation[C]// European Conference on Computer Vision，Munich：ECCV，2018：125-141.
[39] PENG Sida，ZHOU Xiaowei，LIU Yuan，et al. PVNet：pixel-wise voting network for 6DoF object pose estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2022，44(6)：3212-3223.
[40] ZHANG Yuqi，LIU Yuanpeng，WU Qiaoyun，et al. EANet：edge-attention 6D pose estimation network for texture-less objects[J]. IEEE Transactions on Instrumentation and Measurement，2022，71(2504413)：1-13.
[41] HE Zaixing，LI Quanzhi，ZHAO Xinyue，et al. ContourPose：monocular 6D pose estimation method for reflective textureless metal parts[J]. IEEE Transactions on Robotics，2023，39(5)：4037-4050.
[42] WU Chenrui，CHEN Long，HE Zaixing，et al. Pseudo-siamese graph matching network for textureless object’s 6D pose estimation[J]. IEEE Transactions on Industrial Electronics，2021(69)：2718-2727.
[43] ZAKHARV S，SHUGUROV I，ILIC S. DPOD：6d pose object detector and refiner[C]// IEEE International Conference on Computer Vision，Seoul：ICCV，2019：1941-1950.
[44] XU Yan，LIN K，ZHANG Guofeng，et al. RNNPose：6-dof object pose estimation via recurrent correspondence field estimation and pose optimization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2024，46(7)：4669-4683.
[45] AVERT A，SAVAKIS A. DeepRM：deep recurrent matching for 6D pose refinement[C]// IEEE Conference on Computer Vision and Pattern Recognition，Vancouver：CVPR，2023：6206-6214.
[46] HU Jia，LIU Shaoli，LIU Jianhua，et al. Latent representation self-supervised pose network for accurate monocular pipe pose estimation[J]. IEEE Transactions on Industrial Informatics，2023，19(5)：7180-7189.
[47] YANG Jun，XUE Wenjie，GHAVIDEL S，et al. 6D pose estimation for textureless objects on RGB frames using multi-view optimization[C]// IEEE International Conference on Robotics and Automation，London：ICRA，2023：2905-2912.
[48] HODAN T，MICHEL F，BRACHMANN E，et al. BOP：Benchmark for 6D object pose estimation[C]// European Conference on Computer Vision，Munich：ECCV，2018：19-34.
[49] ZHAN Yue，WANG Xin，NIE Lang，et al. TG-Pose：Delving into topology and geometry for category-level object pose estimation[J]. IEEE Transactions on Multimedia，2024(26)：9749-9762.
[50] GAO Ao，CHEN Gaoming，YAN Sixun，et al. Pose estimation of sheet stampings based on point pair features[C]// IEEE International Conference on Real- Time Computing and Robotics，Shanxi：RCAR，2023：870-875.
[51] LUO Qifeng，ZHOU Danya，WEI Zhenzhong. Semantic-aware style transfer for unsupervised aircraft pose estimation[J]. Journal of King Saud University Computer and Information Sciences，2025，37，146.
[52] HASHIMOTO S，HIRANO D，HIGUCHI H，et al. Domain adaptation for 6-DoF pose estimation：deep learning with uncertainty[C]// IEEE Aerospace Conference，Big Sky：AERO，2022：1-12.
[53] KIM B，CHOI M，SON S W，et al. Vision-force guided precise robotic assembly for 2.5D components in a semi structured environment[J]. Assembly Automation，2021，42(2)：200-207.
[54] GAN Yahui，DUAN Jinjun，CHEN Ming，et al. Multi-robot trajectory planning and position/force coordination control in complex welding tasks[J]. Applied Sciences，2019，9(5)：924.
[55] RUTGEERTS J，SLAETS P，SCHILLEBEECKX F，et al. A demonstration tool with Kalman filter data processing for robot programming by human demonstration[C]// IEEE International Conference on Intelligent Robots and Systems，Edmonton：IROS，2005：3592-3597.
[56] ZOU Peng，ZHU Qiuguo，WU Jun，et al. An approach for peg-in-hole assembly based on force feedback control[C]// Chinese Automation Congress，Hangzhou：CAC，2019：3269-3273.
[57] TRAN T H，WANG Y J，CHENG C K，et al. Using maximum likelihood to calibrate a six-dof force/torque sensor[J]. Microsystem Technologies，2018(24)：4493-4509.
[58] YAO Lei，GAO Qingguang，ZHANG Dailin，et al. An integrated compensation method for the force disturbance of a six-axis force sensor in complex manufacturing scenarios[J]. Sensors，2021，21，4706.
[59] XIAO Juliang，ZHANG Qiulong，HONG Ying，et al. Collision detection algorithm for collaborative robots considering joint friction[J]. International Journal of Advanced Robotic Systems，2018，15(4)：1-13.
[60] ZENG Fen，XIAO Juliang，LIU Haitao. Force/torque sensorless compliant control strategy for assembly tasks using a 6-DOF collaborative robot[J]. IEEE Access，2019(7)：108795-108805.
[61] WANG Yancheng，WU Xin，MEI Deqing，et al. Flexible tactile sensor array for distributed tactile sensing and slip detection in robotic hand grasping[J]. Sensors and Actuators A：Physical，2019，297，111512.
[62] SOTGIU E，AGUIAM D E，CALAZA C，et al. Surface texture detection with a new sub-mm resolution flexible tactile capacitive sensor array for multimodal artificial finger[J]. Journal of Microelectromechanical Systems，2020，29(5):629-636.
[63] KIM M S，AHN H R，LEE S，et al. A dome-shaped piezoelectric tactile sensor arrays fabricated by an air inflation technique [J]. Sensors and Actuators A：Physical，2014，212：151-158.
[64] YAN Youcan，HU Zhe，YANG Zhengbao，et al. Soft magnetic skin for super-resolution tactile sensing with force self-decoupling[J]. Science Robotics，2021，6(51)：eabc8801.
[65] DU Yipai，ZHANG Guanlan，ZHANG Yazhan，et al. High-resolution 3-dimensional contact deformation tracking for finger vision sensor with dense random color pattern[J]. IEEE Robotics and Automation Letters，2021，6(2)：2147-2154.
[66] SHI Beichao，WANG Fujun，HUO Zhichen，et al. Contact force sensing and control for inserting operation during precise assembly using a micromanipulator with force sensors[J]. IEEE Transactions on Automation Science and Engineering，2023，20(3)：2147-2155.
[67] BEKIROGLU Y，LAAKSONEN J，JORGENSEN J A，et al. Assessing grasp stability based on learning and haptic data[J]. IEEE Transactions on Robotics，2011，27(3)：616-629.
[68] TAYLOR I H，DONG S，RODRIGUEZ A. GelSlim 3.0：High-resolution measurement of shape，force and slip in a compact tactile-sensing finger[C]// International Conference on Robotics and Automation，Xian：ICRA，2021：10781-10787.
[69] MIYAZAWA N，KATO D，KOBAYASHI Y，et al. Optimal action selection to estimate the aperture of bag by force-torque sensor[J]. Advanced Robotics，2024(38)：95-111.
[70] 胡静怡，崔少伟，张超凡，等. 基于触觉感知和伺服的物体三维边缘重建方法[J]. 智能科学与技术学报，2022，4(2)：233-245. HU Jingyi，CUI Shaowei，ZHANG Chaofan，et al. 3D edge reconstruction method based on tactile sensing and servo[J]. Chinese Journal of Intelligent Science and Technology，2022，4(2)：233-245.
[71] 李鹏程，张奇，张大为，等. 融合视觉与力觉的卫星装配误差在线测量与补偿方法[J]. 航空制造技术，2023，66(7)：57-66. LI Pengcheng，ZHANG Qi，ZHANG Dawei，et al. Online measurement and compensation of satellite assembly errors based on vision and force sensing[J]. Aeronautical Manufacturing Technology，2023，66(7)：57-66.
[72] NOTTENSTEINER K，SACHTLER A，ALBU- SCHAFFER A. Towards autonomous robotic assembly：Using combined visual and tactile sensing for adaptive task execution[J]. Journal of Intelligent and Robotic Systems，2021，101，49.
[73] WANG Yue，SUN Yongbin，LIU Ziwei，et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics，2019，38(5)：1-12.
[74] YANG Bin，WANG Xuan，XING Y，et al. Modality fusion vision transformer for hyperspectral and LiDAR data collaborative classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2024(17)：17052-17065.
[75] YAO Shilong，TANG Ruijie，BAI Long，et al. An RNN-LSTM enhanced compact and affordable micro force sensing system for interventional continuum robots with interchangeable end-effector instruments[J]. IEEE Transactions on Instrumentation and Measurement，2023，72(4008711)：1-11.
[76] MEESSEN K J，PAULIDES J J H，LOMONOVA E A. Force calculation in 3D cylindrical structures using Fourier analysis and the Maxwell stress tensor[J]. IEEE Transactions on Magnetics，2013，49(1)：536-545.
[77] LYU Chengang，YANG Bo，CHANG Xinyi，et al. FBG tactile sensing system based on GAF and CNN[J]. IEEE Sensors Journal，2022，22(19)：18841-18849.
[78] MUROYAMA M，TANAKA S. Object judgment with an event-driven MEMS-LSI integrated tactile sensor array system[J]. IEEJ Transactions on Sensors and Micromachines，2023，143(7)：164-170.
[79] WAN Teng，DU Shaoyi，XU Yiting，et al. RGB-D point cloud registration via infrared and color camera[J]. Multimedia Tools and Applications，2019(78)：33223-33246.
[80] MEI Jiangyuan，LIU Meizhu，WANG Yuanfang，et al. Learning a mahalanobis distance-based dynamic time warping measure for multivariate time series classification[J]. IEEE Transactions on Cybernetics，2016，46(6)：1363-1374.
[81] WEI Xiong，WANG Zifan. TCN-attention-HAR：Human activity recognition based on attention mechanism time convolutional network[J]. Scientific Reports，2024，14(1)：7414.
[82] NIU Changm SHANG Junyuan，HUANG Junchu，et al. Structure consistency and class discriminative feature learning for heterogeneous domain adaptation[J]. IEEE 3rd International Conference on Communication and Information Systems，Singapore：ICCIS，2018：217-221.
[83] DUAN Yao，YI Renjiao，GAO Yuanming，et al. EFECL：Feature encoding enhancement with contrastive learning for indoor 3D object detection[J]. Computational Visual Media，2023，9(4)：875-892.
[84] LI Mingcheng，YANG Dingkang，ZHAO Xiao，et al. Correlation-decoupled knowledge distillation for multimodal sentiment analysis with incomplete modalities[C]// IEEE Conference on Computer Vision and Pattern Recognition，Seattle：CVPR，2024：12458-12468.
[85] ZHU Zhixiao，CHEN Guangyi，TANG Gang. Domain adaptation with multi-adversarial learning for open-set cross-domain intelligent bearing fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement，2023，72(3533411)：1-11.
[86] SFERRAZZA C，DANDREA R. Design. Motivation and evaluation of a full-resolution optical tactile sensor[J]. Sensor，2019，19(4)：928-931.
[87] SUN H，KUCHENBECKER K J，MARTIUS G. A soft thumb-sized vision-based sensor with accurate all-round force perception[J]. Nature Machine Intelligence，2022，4(2)：135-145.
[88] CUI Shaowei，WANG Rui，WEI Junhang，et al. Grasp state assessment of deformable objects using visual-tactile fusion perception[C]// IEEE International Conference on Robotics and Automation，online：ICRA，2020：538-544.
[89] ZAPATA-IMPATA B S，Gil P，TORRES F. Tactile-driven grasp stability and slip prediction[J]. Robotics，2019，8(4)：85-94.
[90] JAMES J W，LEPORA N F. Slip detection for grasp stabilization with a multifingered tactile robot hand[J]. IEEE Transactions on Robotics，2021，37(2)：506-519.
[91] ZHANG Yazhan，KAN Zicheng，YANG Yang，et al. Effective estimation of contact force and torque for vision-based tactile sensor with helmholtz-hodge decomposition[J]. IEEE Robotics and Automation Letters，2019，4(4)：4094-4101.
[92] MURPHY R. Computer vision and machine learning in science fiction[J]. Science Robotics，2019，4(30)：7221-7235.
[93] DIZIOGLU B，LAKSHIMINARAYANA K. Mechanics of form closure[J]. Acta Mechanica，1984，52(1)：107-118.
[94] NGUYEN V D. Constructing force-closure grasps[J]. The International Journal of Robotics Research，1988，7(3)：3-16.
[95] NATARAJAN S，BROWN G，CALLI B. Aiding grasp synthesis for novel objects using heuristic-based and data-driven active vision methods[J]. Frontiers in Robotics and AI，2021，8.
[96] GENG Wenjie，CAO Zhiqiang，TANG Yingbo，et al. Visual grasping with spectral clustering and heuristic searching for robot in cluttered environments[C]// IEEE International Conference on Robotics and Biomimetics，Jinghong：ROBIO，2022：1656-1661.
[97] WANG Kaimeng，FAN Yongxiang，SAKUMA I. Robot grasp planning from human demonstration[C]// 15th International Conference on Computer and Automation Engineering，Sydney：ICCAE，2023：421-426.
[98] YE Congcong，YANG Jixiang，DING Han. Bagging for gaussian mixture regression in robot learning from demonstration[J]. Journal of Intelligent Manufacturing，2022(33)：867-879.
[99] LIAO Zhiwei，JIANG Gedong，ZHAO Fei，et al. Dynamic skill learning from human demonstration based on the human arm stiffness estimation model and riemannian DMP[J]. IEEE/ASME Transactions on Mechatronics，2023，28(2)：1149-1160.
[100] LEE S，LEE Y. Real-time industrial bin-picking with a hybrid deep learning-engineering approach[C]// IEEE International Conference on Big Data and Smart Computin，Kyoto：IEEE，2020：584-588.
[101] MAHLER J，POKORNY F T，HOU B，et al. Dex-net 1.0：A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards[C]// IEEE International Conference on Robotics and Automation，Stockholm：ICRA，2016：1957-1964.
[102] MAHLER J，MATL M，LIU X，et al. Dex-net 3.0：Computing robust vacuum suction grasp targets in point clouds using a new analytic model and deep learning[C]// IEEE International Conference on Robotics and Automation，Brisbane：ICRA，2018：5620-5627.
[103] MAHLER J，MATL M，SATISH V，et al. Learning ambidextrous robot grasping policies[J]. Science Robotics，2019，4(26)：eaau4984.
[104] SAXENA A，DRIEMEYER J，NG A Y. Robotic grasping of novel objects using vision[J]. The International Journal of Robotics Research，2008，27(2)：157-173.
[105] MORRISON D，CORKE P，LEITNER J. Learning robust，real-time，reactive robotic grasping[J]. The International Journal of Robotics Research，2020，39(2-3)：183-201.
[106] FU Kui，DANG Xuanju，ZHANG Yichen. Taylor neural network for unseen object instance segmentation in hierarchical grasping[J]. IEEE/ASME Transactions on Mechatronics，2024，29(5)：3485-3496.
[107] ZHANG C D，SONG S M. Forward kinematics of a class of parallel (Stewart) platforms with closed-form solutions[C]// IEEE International Conference on Robotics and Automation，Sacramento：ICRA，1991：2676-2681.
[108] ASADA H，KAKUMOTO Y. The dynamic RCC hand for high-speed assembly[C]// IEEE International Conference on Robotics and Automation，Philadelphia：ICRA，1988：120-125.
[109] ZHAO Fuliang，WU P S Y. VRCC：A variable remote center compliance device[J]. Mechatronics，1998，8(6)：657-672.
[110] ZHANG Qi，HU Zhengtao，WAN Wweiwei，et al. Compliant peg-in-hole assembly using a very soft wrist[J]. IEEE Robotics and Automation Letters，2023，9(1)：17-24.
[111] CHOI S，KIM D，KIM Y，et al. A novel compliance compensator capable of measuring six-axis force/torque and displacement for a robotic assembly[J]. IEEE/ASME Transactions on Mechatronics，2024，29(1)：29-40.
[112] 王媛媛. 融合视觉和力觉的工业机器人柔顺装配技术研究[D]. 重庆：重庆理工大学，2021. WANG Yuanyuan. Research on technology of compliant assembly of industrial robot fusing vision and force sense[D]. Chongqing：Chongqing University of Technology，2021.
[113] HOGAN N. Impedance control：An approach to manipulation[C]// American Control Conference，San Diego：ACC，1984：304-313.
[114] DUAN Jinjun，GAN Yahui，CHEN Ming，et al. Adaptive variable impedance control for dynamic contact force tracking in uncertain environment[J]. Robotics and Autonomous Systems，2018(102)：54-65.
[115] WANG Yixuan，SUN Tairen，YANG Jiantao. Super-twisting nonsingular terminal sliding mode-based robust impedance control of robots[J]. Complexity，2022，9263699.
[116] WIMBOCK T，OTT C，ALBU-SCHAFFER A，et al. Comparison of object-level grasp controllers for dynamic dexterous manipulation[J]. The International Journal of Robotics Research，2012，31(1)：3-23.
[117] PFANNE M，CHALON M，STULP F，et al. Object-level impedance control for dexterous in-hand manipulation[J]. IEEE Robotics and Automation Letters，2020，5(2)：2987-2994.
[118] RAIBERT M H，CRAIG J J. Hybrid position/force control of manipulators[J]. Journal of Dynamic Systems，Measurement，and Control，1981，103(2)：126-133.
[119] ANDERSON R J，SPONG M W. Hybrid impedance control of robotic manipulators[J]. IEEE Journal on Robotics and Automation，1988，4(5)：549-556.
[120] ALMAGHOUT K，BOBY R A，OTHMAN M，et al. Robotic pick and assembly using deep learning and hybrid vision/force control[C]// International Conference Nonlinearity，Information and Robotics，Innopolis：NIR，2021：1-6.
[121] LIU Song，XING Dengpeng，LI Youfu，et al. Robust insertion control for precision assembly with passice compliance combing vision and force information[J]. IEEE/ASME Transactions on Mechatronics，2019，24(5)：1974-1985.
[122] BOBY R A. Kinematic identification of industrial robot using end-effector mounted monocular camera bypassing measurement of 3d pose[J]. IEEE/ASME Transactions on Mechatronics，2022，27(1)：383-394.
[123] YAO Cenglin，LI Yongzhou，ANISARI M D，et al. Optimization of industrial process parameter control using improved genetic algorithm for industrial robot[J]. Journal of Behavioral Robotics，2022，13(1)：67-75.
[124] ZHANG Chenyu，MENG Ziwen，NAN Tongqing，et al. Design of automatic assembly system for aircraft control system parts based on improved genetic algorithm[C]// Asia-Pacific Conference on Software Engineering，Social Network Analysis and Intelligent Computing，New Delhi：SSAIC，2024：45-50.
[125] SHENG Lianchao，LI Wei. Optimization design by genetic algorithm controller for trajectory control of a 3-RRR parallel robot[J]. Algorithm，2018，11(7)：7.
[126] FATEH S，FATEH M M. Adaptive fuzzy control of robot manipulators with asymptotic tracking performance[J]. Journal of Control，Automation and Electrical Systems，2020(31)：52-61.
[127] WANG Bin，ZHANG Jiwen，WU Dan. Force-vision fusion fuzzy control for robotic batch precision assembly of flexibly absorbed pegs[J]. Robotics and Computer-Integrated Manufacturing，2025，92，102861.
[128] NGUYEN H-T，CHEAH C C. Analytic deep neural network-based robot control[J]. IEEE/ASME Transactions on Mechatronics，2022，27(4)：2176-2184.
[129] ZHANG Zuguo，WU Qingcong，LI Xiong，et al. Barrier Lyapunov function-based robot control with an augmented neural network approximator[J]. Industrial Robot，2022，49(2)：359-367.
[130] WANG Xia，XU Bin，CHENG Yixin，et al. Robust adaptive learning control of space robot for target capturing using neural network[J]. IEEE Transactions on Neural Networks and Learning Systems，2023，34(10)：7567-7577.
[131] DING Yufeng，ZHAO Junchao，MIN Xinpu，et al. Impedance control and parameter optimization of surface polishing robot based on reinforcement learning[J]. Proceedings of the Institution of Mechanical Engineers，Part B：Journal of Engineering Manufacture，2022，237(1-2)：216-228.
[132] CHAI Runqi，NIU Hanlin，CARRASCO J，et al. Design and experimental validation of deep reinforcement learning-based fast trajectory planning and control for mobile robot in unknown environment[J]. IEEE Transactions on Neural Networks and Learning Systems，2024，35(4)：5778-5792.
[133] WANG Minghui，ZENG Bi，WANG Qiujie. Research on motion planning based on flocking control and reinforcement learning for multi-robot systems[J]. Machines，2021，9(4)：77.
[134] CHEMWENO P，PINTELON L，DECRE W. Orienting safety assurance with outcomes of hazard analysis and risk assessment：A review of the ISO 15066 standard for collaborative robot systems[J]. Safety Science，2020，129：104832.
[135] KIRSCHNER R J，MANSFELD N，ABDOLSHAH S，et al. Experimental analysis of impact forces in constrained collisions according to ISO/TS 15066[C]// IEEE International Conference on Intelligence and Safety for Robotics，Nagoya：ISR，2021：1-5.
[136] KIRSCHNER R J，MANSFELD N，PENA G，et al. Notion on the correct use of the robot effective mass in the safety context and comments on ISO/TS 15066[C]// IEEE International Conference on Intelligence and Safety for Robotics，Nagoya：ISR，2021：6-9.
[137] GHANBARZADEH A，NAJAFI E. Safe physical human-robot interaction through variable impedance control based on ISO/TS 15066[J]. International Journal on Interactive Design and Manufacturing，2024，19(7)：4741-4758.
[138] FERRAGUTI F，BERTULETTI M，LANDI C T，et al. A control barrier function approach for maximizing performance while fulfilling to ISO/TS 15066 regulations[J]. IEEE Robotics and Automation Letters，2020，5(4)：5921–5928.
[139] ROVEDA L，PEDROCCHI N，BESCHI M，et al. High-accuracy robotized industrial assembly task control schema with force overshoots avoidance[J]. Control Engineering Practice，2017(71)：142-153.
[140] KHAN Z，NASIR A，MEKID S. Fault-tolerant control strategies for industrial robots：state of the art and future perspective on AI-based fault management[J]. Artificial Intelligence Review，2025，58：362.
[141] XIA Jiyu，OUYANG H，ZHANG Menghua. Fault-tolerant controller design based on adaptive backstepping for tower cranes with actuator faults[J]. ISA Transactions，2024(146)：463-471.
[142] 巴晓甫，赵安安，郝巨，等. 模块化柔性飞机装配生产线设计[J]. 航空制造技术，2018，61(9)：72-77. BA Xiaofu，ZHAO Anan，HAO Ju，et al. Design of modular flexible aircraft assembly line[J]. Aeronautical Manufacturing Technology，2018，61(9)：72-77.
[143] ZHANG Zhiyong，WANG Xiaodong，ZHAO Hongtu，et al. The machine vision measurement module of the modularized flexible precision assembly station for assembly of micro- and meso-sized parts[J]. Micromachines，2020，11：918.
[144] SHIRINZADEH B，ZHONG Yongmin，TILAKARATNA P D W，et al. A hybrid contact state analysis methodology for robotic-based adjustment of cylindrical pair[J]. The International Journal of Advanced Manufacturing Technology，2011，52(1)：329-342.
[145] CHEN Zhong，XIE Shengyang，ZHANG Xianmin. Position/force visual-sensing-based robotic sheet-like peg-in-hole assembly[J]. IEEE Transactions on Instrumentation and Measurement，2022(71)：1-11.
[146] 秦方博，徐德. 机器人操作技能模型综述[J]. 自动化学报，2019，45(8)：1401-1418. QIN Fangbo，XU De. Review of robot manipulation skill models[J]. ACTA Automatica Sinica，2019，45(8)：1401-1418.
[147] WANG Qianji，LIU Yongkui，ZHU Zilu，et al. A phased robotic assembly policy based on a PL-LSTM-SAC algorithm[J]. Journal of Manufacturing Systems，2025(78)：351-369.
[148] STYRUD J，IOVINO M，NORRLOF M，et al. Combining planning and learning of behavior trees for robotic assembly[C]// International Conference on Robotics and Automation，Philadelphia：ICRA，2022：11511-11517.
[149] LI Shufei，FAN Junming，ZHENG Pai，et al. Transfer learning-enabled action recognition for human-robot collaborative assembly[J]. Procedia CIRP，2021(104)：1795-1800.
[150] ZHU Zuyuan，HU Huosheng. Robot learning from demonstration in robotic assembly：A survey[J]. Robotics，2018，7(2)：17.
[151] ZHANG Songsong，HUANG Haisong，FAN Qingsong，et al. Subtask-learning based for robot self-assembly in flexible collaborative assembly in manufacturing[J]. The International Journal of Advanced Manufacturing Technology，2022(120)：6807-6819.
[152] MA Yanqin，XU De，QIN Fangbo. Efficient insertion control for precision assembly based on demonstration learning and reinforcement learning[J]. IEEE Transactions on Industrial Informatics，2021，17(7)：4492-4502.
[153] NEVES M，NETO P. Deep reinforcement learning applied to an assembly sequence planning problem with user preferences[J]. The International of Advanced Manufacturing Technology，2022，122(11-12)：4235-4245.
[154] 李凤鸣. 面向复杂零部件的机器人柔性装配方法研究[D]. 济南：山东大学，2021. LI Mingfeng. Research on flexible robotic assembly method for complex components[D]. Jinan：Shandong University，2021.
[155] SHI Yunlei，YUAN Chengjie，ATHANASIOS T，et al. A sim-to-real learning-based framework for contact-rich assembly by utilizing CycleGAN and force control[J]. IEEE Transactions on Cognitive and Developmental Systems，2023(15)：2144-2155.
[156] WU Shangsi，FANG Haonan，WANG Peng，et al. A method for human-robot collaborative assembly action recognition based on skeleton data and transfer learning[J]. Huaman Factors in Robots，Drones and Unmanned Systems，2024，138.
[157] LIU Dong，ZHANG Xiaomin，DU Yu，et al. Industrial insert robotic assembly based on model-based meta-reinforcement learning[C]// IEEE International Conference on Robotics and Biomimetics，Sanya：ROBIO，2021：1508-1512.
[158] PNEG Jieyang，WANG Dongkun，ZHAO Junkai，et al. Meta-learning enhanced adaptive robot control strategy for automated PCB assembly[J]. Journal of Manufacturing Systems，2025(78)：46-57.