基于阻抗控制的人机协作范式研究及其机器人装配应用

赵兴炜, 陈沂洺, 钱璐, 陶波, 丁汉

工程(英文) ›› 2023, Vol. 30 ›› Issue (11) : 83-92.

PDF(2376 KB)
PDF(2376 KB)
工程(英文) ›› 2023, Vol. 30 ›› Issue (11) : 83-92. DOI: 10.1016/j.eng.2022.08.022
研究论文
Article

基于阻抗控制的人机协作范式研究及其机器人装配应用

作者信息 +

Human-Robot Collaboration Framework Based on Impedance Control in Robotic Assembly

Author information +
History +

摘要

人-机器人协作由于人和机器人的优势互补而成为一个新兴的研究领域。本文提出了一种基于阻抗控制人机协作理论框架。在人机理论协框架下,人是决策者,机器人是执行者,装配任务提供环境约束。机器人是执行装配动作的主要执行者,具有位置控制、拖动控制、正阻抗控制和负阻抗控制等方式。为了揭示人机协作框架的特点,本文讨论了不同控制模式的切换条件图和人机协同耦合系统的稳定性分析。最后进行了人机协作装配实验,在装配公差为0.08 mm或配合过盈配合时,均可完成装配任务。实验表明,人机协同装配具有人与机器人的互补优势,能够有效地完成复杂的装配任务。

Abstract

Human-robot (HR) collaboration (HRC) is an emerging research field because of the complementary advantages of humans and robots. An HRC framework for robotic assembly based on impedance control is proposed in this paper. In the HRC framework, the human is the decision maker, the robot acts as the executor, while the assembly environment provides constraints. The robot is the main executor to perform the assembly action, which has the position control, drag and drop, positive impedance control, and negative impedance control modes. To reveal the characteristics of the HRC framework, the switch condition map of different control modes and the stability analysis of the HR coupled system are discussed. In the end, HRC assembly experiments are conducted, where the HRC assembly task can be accomplished when the assembling tolerance is 0.08 mm or with the interference fit. Experiments show that the HRC assembly has the complementary advantages of humans and robots and is efficient in finishing complex assembly tasks.

关键词

人机协作 / 阻抗控制 / 机器人装配

Keywords

Human-robot collaboration / Impedance control / Robotic assembly

引用本文

导出引用
赵兴炜, 陈沂洺, 钱璐. 基于阻抗控制的人机协作范式研究及其机器人装配应用. Engineering. 2023, 30(11): 83-92 https://doi.org/10.1016/j.eng.2022.08.022

参考文献

[1]
A. De Santis, B. Siciliano, A. De Luca, A. Bicchi. An atlas of physical human-robot interaction. Mechanism Mach Theory, 43 (3) ( 2008), pp. 253-270
[2]
Y. Li, S.S. Ge. Human-robot collaboration based on motion intention estimation. IEEE/ASME Trans Mechatron, 19 (3) ( 2014), pp. 1007-1014
[3]
C.P. Day. Robotics in industry—their role in intelligent manufacturing. Engineering, 4 (4) ( 2018), pp. 440-445
[4]
B. Wang. The future of manufacturing: a new perspective. Engineering, 4 (5) ( 2018), pp. 722-728
[5]
J. Krüger, T.K. Lien, A. Verl. Cooperation of human and machines in assembly lines. CIRP Ann, 58 (2) ( 2009), pp. 628-646
[6]
Ding H, Schipper M, Matthias B. Optimized task distribution for industrial assembly in mixed human-robot environments—case study on IO module assembly. In:Proceedings of 2014 IEEE International Conference on Automation Science and Engineering (CASE); 2014 Aug 18-22; Taipei, China. IEEE; 2014. p. 19-24.
[7]
H. Bley, G. Reinhart, G. Seliger, M. Bernardi, T. Korne. Appropriate human involvement in assembly and disassembly. CIRP Ann, 53 (2) ( 2004), pp. 487-509
[8]
Bonilla BL, Asada HH. A robot on the shoulder coordinated human-wearable robot control using Coloured Petri Nets and Partial Least Squares predictions. In:Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA); 2014 May 31-Jun 7; Hong Kong, China. IEEE; 2014. p. 119-25.
[9]
Liu C, Tomizuka M. Modeling and controller design of cooperative robots in workspace sharing human-robot assembly teams. In:Proceedings of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems; 2014 Sep 14-18; Chicago, IL, USA. IEEE; 2014. p. 1386-91.
[10]
L. Roveda, M. Magni, M. Cantoni, D. Piga, G. Bucca. Human-robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian optimization. Robot Auton Syst, 136 ( 2021), Article 103711
[11]
J. Jiang, Z. Huang, Z. Bi, X. Ma, G. Yu. State-of-the-art control strategies for robotic PiH assembly. Robot Comput Integr Manuf, 65 ( 2020), Article 101894
[12]
A. Realyvásquez-Vargas, K.C. Arredondo-Soto, J.L. García-Alcaraz, B.Y. Márquez-Lobato, J. Cruz-García. Introduction and configuration of a collaborative robot in an assembly task as a means to decrease occupational risks and increase efficiency in a manufacturing company. Robot Comput Integr Manuf, 57 ( 2019), pp. 315-328
[13]
K. Ramirez-Amaro, M. Beetz, G. Cheng. Transferring skills to humanoid robots by extracting semantic representations from observations of human activities. Artif Intell, 247 ( 2017), pp. 95-118
[14]
B. Huang, M. Li, R.L. De Souza, J.J. Bryson, A. Billard. A modular approach to learning manipulation strategies from human demonstration. Auton Robots, 40 (5) ( 2016), pp. 903-927 DOI: 10.1007/s10514-015-9501-9
[15]
C. Yang, C. Zeng, P. Liang, Z. Li, R. Li, C.Y. Su. Interface design of a physical human-robot interaction system for human impedance adaptive skill transfer. IEEE Trans Autom Sci Eng, 15 (1) ( 2018), pp. 329-340
[16]
C. Yang, C. Zeng, C. Fang, W. He, Z. Li. A DMPs-based framework for robot learning and generalization of humanlike variable impedance skills. IEEE/ASME Trans Mechatron, 23 (3) ( 2018), pp. 1193-1203 DOI: 10.1109/tmech.2018.2817589
[17]
A. Mörtl, M. Lawitzky, A. Kucukyilmaz, M. Sezgin, C. Basdogan, S. Hirche. The role of roles: physical cooperation between humans and robots. Int J Robot Res, 31 (13) ( 2012), pp. 1656-1674 DOI: 10.1177/0278364912455366
[18]
R.B. Gillespie, J.E. Colgate, M.A. Peshkin. A general framework for robot control. IEEE Trans Robot Autom, 17 (4) ( 2001), pp. 391-401
[19]
M.S. Erden, A. Billard. Robotic assistance by impedance compensation for hand movements while manual welding. IEEE Trans Cybern, 46 (11) ( 2016), pp. 2459-2472
[20]
N. Jarrassé, V. Sanguineti, E. Burdet. Slaves no longer: review on role assignment for human-robot joint motor action. Adapt Behav, 22 (1) ( 2014), pp. 70-82 DOI: 10.1177/1059712313481044
[21]
S. Musić, S. Hirche. Control sharing in human-robot team interaction. Annu Rev Contr, 44 ( 2017), pp. 342-354
[22]
M. Khoramshahi, A. Billard. A dynamical system approach to task-adaptation in physical human-robot interaction. Auton Robots, 43 (4) ( 2019), pp. 927-946 DOI: 10.1007/s10514-018-9764-z
[23]
R. Zhang, Q. Lv, J. Li, J. Bao, T. Liu, S. Liu. A reinforcement learning method for human-robot collaboration in assembly tasks. Robot Comput Integr Manuf, 73 (2022), Article 102227
[24]
N. Hogan. Impedance control: an approach to manipulation: part II—implementation. J Dyn Sys Meas Control, 107 (1) ( 1985), pp. 8-16 DOI: 10.1115/1.3140713
[25]
X. Zhao, B. Tao, L. Qian, Y. Yang, H. Ding. Asymmetrical nonlinear impedance control for dual robotic machining of thin-walled workpieces. Robot Comput Integr Manuf, 63 ( 2020), Article 101889
[26]
S. Cremer, S.K. Das, I.B. Wijayasinghe, D.O. Popa, F.L. Lewis. Model-free online neuroadaptive controller with intent estimation for physical human-robot interaction. IEEE Trans Robot, 36 (1) ( 2020), pp. 240-253 DOI: 10.1109/tro.2019.2946721
[27]
E. Burdet, R. Osu, D.W. Franklin, T.E. Milner, M. Kawato. The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature, 414 (6862) ( 2001), pp. 446-449
[28]
Y. Li, G. Ganesh, N. Jarrassé, S. Haddadin, A. Albu-Schaeffer, E. Burdet. Force, impedance, and trajectory learning for contact tooling and haptic identification. IEEE Trans Robot, 34 (5) ( 2018), pp. 1170-1182 DOI: 10.1109/tro.2018.2830405
[29]
X. Chen, N. Wang, H. Cheng, C. Yang. Neural learning enhanced variable admittance control for human-robot collaboration. IEEE Access, 8 ( 2020), pp. 25727-25737 DOI: 10.1109/access.2020.2969085
[30]
L. Roveda, J. Maskani, P. Franceschi, A. Abdi, F. Braghin, L. Molinari Tosatti, et al.. Model-based reinforcement learning variable impedance control for human-robot collaboration. J Intell Robot Syst, 100 (2) ( 2020), pp. 417-433 DOI: 10.1007/s10846-020-01183-3
[31]
X. Zhao, S. Han, B. Tao, Z. Yin, H. Ding. Model-based actor-critic learning of robotic impedance control in complex interactive environment. IEEE Trans Ind Electron, 69 (12) ( 2022), pp. 13225-13235 DOI: 10.1109/tie.2021.3134082
[32]
N. Jarrassé, T. Charalambous, E. Burdet. A framework to describe, analyze and generate interactive motor behaviors. PLoS One, 7 (11) ( 2012), p. e49945 DOI: 10.1371/journal.pone.0049945
[33]
Y. Li, G. Carboni, F. Gonzalez, D. Campolo, E. Burdet. Differential game theory for versatile physical human-robot interaction. Nat Mach Intell, 1 (1) ( 2019), pp. 36-43
[34]
R. Gervasi, L. Mastrogiacomo, F. Franceschini. A conceptual framework to evaluate human-robot collaboration. Int J Adv Manuf Technol, 108 (3) ( 2020), pp. 841-865 DOI: 10.1007/s00170-020-05363-1
[35]
D. Mukherjee, K. Gupta, L.H. Chang, H. Najjaran. A survey of robot learning strategies for human-robot collaboration in industrial settings. Robot Comput Integr Manuf, 73 ( 2022), Article 102231
[36]
Xing H, Torabi A, Ding L, Gao H, Li W, Mushahwar VK, et al. Human-robot collaboration for heavy object manipulation: Kinesthetic teaching of the role of wheeled mobile manipulator. In:Proceedings of 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2021 Sep 27-Oct 1; Prague, Czech Republic. IEEE; 2021. p. 2962-9.
[37]
W. Kim, L. Peternel, M. Lorenzini, J. Babič, A. Ajoudani. A human-robot collaboration framework for improving ergonomics during dexterous operation of power tools. Robot Comput Integr Manuf, 68 (2021), Article 102084
[38]
L. Peternel, N. Tsagarakis, D. Caldwell, A. Ajoudani. Robot adaptation to human physical fatigue in human-robot co-manipulation. Auton Robots, 42 (5) ( 2018), pp. 1011-1021 DOI: 10.1007/s10514-017-9678-1
[39]
V. Gopinath, K. Johansen, M. Derelöv, Å. Gustafsson, S. Axelsson. Safe collaborative assembly on a continuously moving line with large industrial robots. Robot Comput Integr Manuf, 67 (2021), Article 102048
[40]
V. Villani, F. Pini, F. Leali, C. Secchi. Survey on human-robot collaboration in industrial settings: safety, intuitive interfaces and applications. Mechatronics, 55 ( 2018), pp. 248-266
[41]
A. Ajoudani, A.M. Zanchettin, S. Ivaldi, A. Albu-Schäffer, K. Kosuge, O. Khatib. Progress and prospects of the human-robot collaboration. Auton Robots, 42 (5) ( 2018), pp. 957-975 DOI: 10.1007/s10514-017-9677-2
[42]
E. Matheson, R. Minto, E.G.G. Zampieri, M. Faccio, G. Rosati. Human-robot collaboration in manufacturing applications: a review. Robotics, 8 (4) ( 2019), p. 100 DOI: 10.3390/robotics8040100
[43]
H. Liu, L. Wang. Gesture recognition for human-robot collaboration: a review. Int J Ind Ergon, 68 ( 2018), pp. 355-367
[44]
H. Liu, L. Wang. Remote human-robot collaboration: a cyber-physical system application for hazard manufacturing environment. J Manuf Syst, 54 ( 2020), pp. 24-34
[45]
O. Khatib. A unified approach for motion and force control of robot manipulators: the operational space formulation. IEEE J Robot Autom, 3 (1) ( 1987), pp. 43-53
[46]
S.P. Buerger, N. Hogan. Complementary stability and loop shaping for improved human-robot interaction. IEEE Trans Robot, 23 (2) ( 2007), pp. 232-244
[47]
M.S. Branicky. Stability of hybrid systems: State of the art. Proceedings of the 36th IEEE Conference on Decision and Control, IEEE, San Diego, CA, USA (1997 Dec 12), pp. 120-125
PDF(2376 KB)

Accesses

Citation

Detail

段落导航
相关文章

/