机器人自动轴孔装配研究进展

doi:10.11959/j.issn.2096-6652.202223

Abstract

Abstract:

Peg-in-hole assembly is a typical operation task in manufactory.The research of peg-in-hole assembly based on industrial robots is valuable for the application of robots in the automated assembly area.For the peg-in-hole components with high precision or complex shapes, the efficient and reliable assembly is still very challenging.The development of automated robotic peg-in-hole assembly of peg-in-hole was reviewed from the view of control.First, the process of robotic peg-in-hole assembly was introduced.Secondly, the assembly control methods based on the traditional models were described.The newly emerged intelligent assembly methods based on learning mechanism were discussed, especially the applications of imitation learning and reinforcement learning in the automated robotic assembly.The combination of the traditional methods and the artificial intelligent methods will provide new energy for the automated robotic assembly, which will be one of the important developing tendencies in future.

Key words: peg-in-hole assembly, intelligent robot, imitation learning, reinforcement learning, skill learning

CLC Number:

TP242.6

De XU,Fangbo QIN. Research development on automated robotic peg-in-hole assembly[J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(2): 200-211.

Figures/Tables 7

References 54

[1]	JIANG J G , HUANG Z Y , BI Z M ,et al. State-of-the-art control strategies for robotic PiH assembly[J]. Robotics and Computer-Integrated Manufacturing, 2020(65): 101894.
[2]	ABU-DAKKA F J , NEMEC B , KRAMBERGER A ,et al. Solving peg-in-hole tasks by human demonstration and exception strategies[J]. Industrial Robot:an International Journal, 2014,41(6): 575-584.
[3]	XU J , HOU Z M , WANG W ,et al. Feedback deep deterministic policy gradient with fuzzy reward for robotic multiple peg-in-hole assembly tasks[J]. IEEE Transactions on Industrial Informatics, 2019,15(3): 1658-1667.
[4]	BELTRAN-HERNANDEZ C C , PETIT D , RAMIREZ-ALPIZAR G I , ,et al. Variable compliance control for robotic peg-in-hole assembly:a deep-reinforcement-learning approach[J]. Applied Sciences-Basel, 2020,10(19): 6923.
[5]	SONG J Z , CHEN Q L , LI Z D . A peg-in-hole robot assembly system based on Gauss mixture model[J]. Robotics and Computer-Integrated Manufacturing, 2021,67: 101996.
[6]	朱博文, 马立, 陈韦男 ,等. 自动轴孔装配系统的现状与关键技术[J]. 现代制造工程, 2019(5): 156-161.
	ZHU B W , MA L , CHEN W N ,et al. Present situation and key technology of the automatic peg-in-hole assembly system[J]. Modern Manufacturing Engineering, 2019(5): 156-161.
[7]	王鹏, 吴成东, 张凤 . 机器人轴孔装配控制技术研究进展[J]. 科技广场, 2007(3): 6-10.
	WANG P , WU C D , ZHANG F . The research progress on robot peg-in-hole assembly control technology[J]. Science Mosaic, 2007(3): 6-10.
[8]	JASIM I F , PLAPPER P W . Contact-state monitoring of force-guided robotic assembly tasks using expectation maximization-based Gaussian mixtures models[J]. The International Journal of Advanced Manufacturing Technology, 2014,73(5/6/7/8): 623-633.
[9]	潘柏松, 颜天野, 胡鑫达 ,等. 基于几何约束与隐马尔可夫链模型的轴孔装配策略[J]. 计算机集成制造系统, 2021:已录用.
	PAN B S , YAN T Y , HU X D ,et al. Peg-in-hole assembly strategy based on geometric constraintand hidden Markov model[J]. Computer Integrated Manufacturing Systems, 2021:accepted.
[10]	TAKAHASHI J , FUKUKAWA T , FUKUDA T . Passive alignment principle for robotic assembly between a ring and a shaft with extremely narrow clearance[J]. IEEE/ASME Transactions on Mechatronics, 2016,21(1): 196-204.
[11]	WATSON J , MILLER A , CORRELL N . Autonomous industrial assembly using force,torque,and RGB-D sensing[J]. Advanced Robotics, 2020,34(7/8): 546-559.
[12]	SLOTH C , KRAMBERGER A , ITURRATE I . Towards easy setup of robotic assembly tasks[J]. Advanced Robotics, 2020,34(7/8): 499-513.
[13]	HOPPE S , LOU Z Y , HENNES D ,et al. Planning approximate exploration trajectories for model-free reinforcement learning in contact-rich manipulation[J]. IEEE Robotics and Automation Letters, 2019,4(4): 4042-4047.
[14]	李侃, 汪法根, 廖正菊 ,等. 一种用于多轴孔装配的机器人系统研究[J]. 机械设计与研究, 2003,19(3): 25-26.
	LI K , WANG F G , LIAO Z J ,et al. Study on a robotic system for multiaxial bore assembling[J]. Machine Design and Research, 2003,19(3): 25-26.
[15]	魏明明, 傅卫平, 蒋家婷 ,等. 操作机器人轴孔装配的行为动力学控制策略[J]. 机械工程学报, 2015,51(5): 14-21.
	WEI M M , FU W P , JIANG J T ,et al. Dynamics of behavior control strategy in peg-in-hole assembly task of manipulator[J]. Journal of Mechanical Engineering, 2015,51(5): 14-21.
[16]	徐远, 宋爱国, 李会军 . 基于机器视觉和力反馈的自动装配技术研究[J]. 测控技术, 2019,38(4): 11-16.
	XU Y , SONG A G , LI H J . Research on automatic assembly technology based on machine vision and force feedback[J]. Measurement ＆Control Technology, 2019,38(4): 11-16.
[17]	陈婵娟, 赵飞飞, 李承 ,等. 多传感器协助机器人精确装配[J]. 机械设计与制造, 2020(3): 281-284.
	CHEN C J , ZHAO F F , LI C ,et al. Multi-sensor assisted robotic accurate assembly[J]. Machinery Design ＆ Manufacture, 2020(3): 281-284.
[18]	ZENG F , XIAO J L , LIU H T . Force/torque sensorless compliant control strategy for assembly tasks using a 6-DOF collaborative robot[J]. IEEE Access, 2019,7: 108795-108805.
[19]	PARK H , PARK J , LEE D H ,et al. Compliance-based robotic peg-in-hole assembly strategy without force feedback[J]. IEEE Transactions on Industrial Electronics, 2017,64(8): 6299-6309.
[20]	陈钢, 王玉琦, 贾庆轩 ,等. 机器航天员轴孔装配过程中的力位混合控制方法[J]. 宇航学报, 2017,38(4): 410-419.
	CHEN G , WANG Y Q , JIA Q X ,et al. Hybrid force and position control strategy of robonaut performing peg-in-hole assembly task[J]. Journal of Astronautics, 2017,38(4): 410-419.
[21]	吴炳龙, 曲道奎, 徐方 . 基于力/位混合控制的工业机器人精密轴孔装配[J]. 浙江大学学报(工学版), 2018,52(2): 379-386.
	WU B L , QU D K , XU F . Industrial robot high precision peg-in-hole assembly based on hybrid force/position control[J]. Journal of Zhejiang University (Engineering Science), 2018,52(2): 379-386.
[22]	董悫, 张立建, 易旺民 ,等. 基于动力学前馈的空间机器人多销孔装配力柔顺控制[J]. 机械工程学报, 2019,55(4): 207-217.
	DONG Q , ZHANG L J , YI W M ,et al. Force compliance control of multi-peg-in-hole assembling by space robot based on dynamic feedforward[J]. Journal of Mechanical Engineering, 2019,55(4): 207-217.
[23]	XING D P , LIU F F , XU D ,et al. Simultaneous control in belief space for circular insertion in precision assembly[J]. IEEE Transactions on Industrial Informatics, 2021,17(3): 1842-1851.
[24]	XING D P , LIU X W , LIU F F ,et al. Efficient insertion strategy for precision assembly with uncertainties using a passive mechanism[J]. IEEE Transactions on Industrial Informatics, 2021,17(2): 1263-1273.
[25]	XING D P , LIU F F , XU D . Joint alignment and simultaneous insertion of multiple objects in precision assembly[J]. IEEE Transactions on Industrial Informatics, 2021,17(1): 230-239.
[26]	XING D P , LIU F F , XU D . Efficient coordinated control strategy to handle randomized inclination in precision assembly[J]. IEEE Transactions on Industrial Informatics, 2020,16(9): 5814-5824.
[27]	LIU S , XING D P , LI Y F ,et al. Robust insertion control for precision assembly with passive compliance combining vision and force information[J]. IEEE/ASME Transactions on Mechatronics, 2019,24(5): 1974-1985.
[28]	曲吉旺, 许家忠, 张大朋 ,等. 基于显微视觉的微球微管精密装配[J]. 高技术通讯, 2019,29(9): 914-924.
	QU J W , XU J Z , ZHANG D P ,et al. Precision assembly for micro-sphere and micro-tube based on microscopic vision[J]. Chinese High Technology Letters, 2019,29(9): 914-924.
[29]	XING D P , LYU Y , LIU S ,et al. Efficient insertion of multiple objects parallel connected by passive compliant mechanisms in precision assembly[J]. IEEE Transactions on Industrial Informatics, 2019,15(9): 4878-4887.
[30]	XING D P , LIU F F , LIU S ,et al. Efficient insertion of partially flexible objects in precision assembly[J]. IEEE Transactions on Automation Science and Engineering, 2019,16(2): 706-715.
[31]	XING D P , LIU F F , LIU S ,et al. Efficient collision detection and detach control for convex prisms in precision manipulation[J]. IEEE Transactions on Industrial Informatics, 2018,14(12): 5316-5326.
[32]	LIU S , XU D , LIU F F ,et al. Relative pose estimation for alignment of long cylindrical components based on microscopic vision[J]. IEEE/ASME Transactions on Mechatronics, 2016,21(3): 1388-1398.
[33]	XING D P , LIU F F , QIN F B ,et al. Coordinated insertion control for inclined precision assembly[J]. IEEE Transactions on Industrial Electronics, 2016,63(5): 2990-2999.
[34]	JASIM I F , PLAPPER P W , VOOS H . Adaptive sliding mode fuzzy control for unknown robots with arbitrarily-switched constraints[J]. Mechatronics, 2015,30: 174-186.
[35]	NOTTENSTEINER K , SACHTLER A , ALBU-SCHAEFFER A , . Towards autonomous robotic assembly:using combined visual and tactile sensing for adaptive task execution[J]. Journal of Intelligent and Robotic Systems, 2021,101(3): 49.
[36]	CORTESAO R , KOEPPE R , NUNES U ,et al. Data fusion for robotic assembly tasks based on human skills[J]. IEEE Transactions on Robotics, 2004,20(6): 941-952.
[37]	PETRI? T , GAMS A , COLASANTO L ,et al. Accelerated sensorimotor learning of compliant movement primitives[J]. IEEE Transactions on Robotics, 2018,34(6): 1636-1642.
[38]	LIU N L , ZHOU X D , LIU Z M ,et al. Learning peg-in-hole assembly using Cartesian DMPs with feedback mechanism[J]. Assembly Automation, 2020,40(6): 895-904.
[39]	SONG H C , KIM Y L , SONG J B . Guidance algorithm for complex-shape peg-in-hole strategy based on geometrical information and force control[J]. Advanced Robotics, 2016,30(8): 552-563.
[40]	QIN F B , XU D , ZHANG D P ,et al. Robotic skill learning for precision assembly with microscopic vision and force feedback[J]. IEEE/ASME Transactions on Mechatronics, 2019,24(3): 1117-1128.
[41]	DING G W , LIU Y B , ZANG X Z ,et al. A task-learning strategy for robotic assembly tasks from human demonstrations[J]. Sensors, 2020,20(19): 5505.
[42]	PEREZ-DEL-PULGAR C J , SMISEK J , RIVAS-BLANCO I , ,et al. Using Gaussian mixture models for gesture recognition during haptically guided telemanipulation[J]. Electronics, 2019,8(7): 772.
[43]	GAO X , LING J , XIAO X H ,et al. Learning force-relevant skills from human demonstration[J]. Complexity, 2019:5262859.
[44]	TI B Y , GAO Y S , LI Q ,et al. Human intention understanding from multiple demonstrations and behavior generalization in dynamic movement primitives framework[J]. IEEE Access, 2019,7: 36186-36194.
[45]	SENDA K , TANI Y . Autonomous robust skill generation using reinforcement learning with plant variation[J]. Advances in Mechanical Engineering, 2014(6): 276264.
[46]	KIM Y L , AHN K H , SONG J B . Reinforcement learning based on movement primitives for contact tasks[J]. Robotics and Computer-Integrated Manufacturing, 2020,62: 101863.
[47]	KHADER S A , YIN H , FALCO P ,et al. Stability-guaranteed reinforcement learning for contact-rich manipulation[J]. IEEE Robotics and Automation Letters, 2021,6(1): 1-8.
[48]	SENDA K , HISHINUMA T , TANI Y . Approximate Bayesian reinforcement learning based on estimation of plant[J]. Autonomous Robots, 2020,44(5): 845-857.
[49]	DONG S , NAGHDY F . Application of hidden Markov model to acquisition of manipulation skills from haptic rendered virtual environment[J]. Robotics and Computer-Integrated Manufacturing, 2007,23(3): 351-360.
[50]	CHO N J , LEE S H , KIM J B ,et al. Learning,improving,and generalizing motor skills for the peg-in-hole tasks based on imitation learning and self-learning[J]. Applied Sciences-Basel, 2020,10(8): 2719.
[51]	王斐, 齐欢, 周星群 ,等. 基于多源信息融合的协作机器人演示编程及优化方法[J]. 机器人, 2018,40(4): 551-559.
	WANG F , QI H , ZHOU X Q ,et al. Demonstration programming and optimization method of cooperative robot based on multi-source information fusion[J]. Robot, 2018,40(4): 551-559.
[52]	MA Y Q , XU D , QIN F B . Efficient insertion control for precision assembly based on demonstration learning and reinforcement learning[J]. IEEE Transactions on Industrial Informatics, 2021,17(7): 4492-4502.
[53]	LI Y , XU D . Skill learning for robotic insertion based on one-shot demonstration and reinforcement learning[J]. International Journal of Automation and Computing, 2021,18(3): 457-467.
[54]	刘乃龙, 刘钊铭, 崔龙 . 基于深度强化学习的仿真机器人轴孔装配研究[J]. 计算机仿真, 2019,36(12): 296-301.
	LIU N L , LIU Z M , CUI L . Deep reinforcement learning based robotic assembly in simulation[J]. Computer Simulation, 2019,36(12): 296-301.

Metrics

Recommended 0

No Suggested Reading articles found!

Research development on automated robotic peg-in-hole assembly

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 54

Related Articles 15

Metrics

Recommended 0

[1]	Zhou YU, Jing BI, Haitao YUAN. A path planning method for complex naval battle field based on an improved DQN algorithm [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(3): 418-425.
[2]	Shuai MA, Qiming FU, Jianping CHEN, Fan FENG, You LU, Zhengwei LI, Shunian QIU. HVAC model-free optimal control method based on double-pools DQN [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(3): 426-444.
[3]	Yuxiang SUN, Yihui PENG, Bin LI, Jiawei ZHOU, Xinlei ZHANG, Xianzhong ZHOU. Overview of intelligent game:enlightenment of game AI to combat deduction [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(2): 157-173.
[4]	Jiacheng LIU, Xiangwen ZHANG. TD3-based energy management strategy for hybrid energy storage system of electric vehicle [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(2): 277-287.
[5]	Pu FENG, Wenjun WU, Jie LUO, Xin YU, Yongkai TIAN. Emergence measurement of robot swarm intelligence based on swarm entropy [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(1): 65-74.
[6]	Han WANG, Yang YU, Yuan JIANG. A cooperative multi-agent reinforcement learning algorithm based on dynamic self-selection parameters sharing [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(1): 75-83.
[7]	Xiaoyan DENG, Zhuliang YU, Canguang LIN, Zhenghui GU, Yuanqing LI. Research on human-computer shared control technology based on brain-computer interface [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(1): 85-92.
[8]	Zhiqiang HU. The framework model on internal mechanism of big data intelligent command and control [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(1): 101-109.
[9]	Zhaoyang LIU, Chaoxu MU, Changyin SUN. An overview on algorithms and applications of deep reinforcement learning [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(4): 314-326.
[10]	Jinna LI, Weiran CHENG. An overview of optimal consensus for data driven multi-agent system based on reinforcement learning [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(4): 327-340.
[11]	Qing-Shan JIA, Jingxian TANG, Junjie WU, Xiao HU, Yiting LIN, Heng XIA. Reinforcement learning for green and reliable data center [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(4): 341-347.
[12]	Tao LI, Qinglai WEI. Intelligent heating temperature control system based on deep reinforcement learning [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(4): 348-353.
[13]	Rizhong WANG, Huiping LI, Di CUI, Demin XU. Depth control of autonomous underwater vehicle using deep reinforcement learning [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(4): 354-360.
[14]	Huiqiao FU, Kaiqiang TANG, Guizhou DENG, Xinpeng WANG, Chunlin CHEN. Motion planning for hexapod robot using deep reinforcement learning [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(4): 361-371.
[15]	Yingying LIU, Zhanshan WANG. Output synchronization of heterogeneous multi-agent system:a reinforcement learning approach based on data [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(4): 394-400.