Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-22T09:11:52.606Z Has data issue: false hasContentIssue false
  • English
  • Français

Force tracking control for motion synchronization in human-robot collaboration

Published online by Cambridge University Press:  26 August 2014

Yanan Li  and
Shuzhi Sam Ge
Yanan Li
Affiliation:
Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 138632, Singapore
Shuzhi Sam Ge*
Affiliation:
Department of Electrical and Computer Engineering, and Social Robotics Laboratory, Interactive Digital Media Institute, National University of Singapore, Singapore 117576, Singapore
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

In this paper, motion synchronization is investigated for human–robot collaboration such that the robot is able to “actively” follow its human partner. Force tracking is achieved with the proposed method under the impedance control framework, subject to uncertain human limb dynamics. Adaptive control is developed to deal with point-to-point movement, and learning control and neural networks control are developed to generate periodic and arbitrary continuous trajectories, respectively. Stability and tracking performance of the closed-loop system are discussed through rigorous analysis. The validity of the proposed method is verified through simulation and experiment studies.

Keywords

Motion synchronizationHuman-robot collaborationForce tracking
Type
Articles
Information
Robotica , Volume 34 , Issue 6 , June 2016 , pp. 1260 - 1281
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Al-Jarrah, O. M. and Zheng, Y. F., “Arm-manipulator Coordination for Load Sharing using Compliant Control,” Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, MN, USA (Apr. 1996) pp. 1000–1005.Google Scholar
2.Al-Jarrah, O. M. and Zheng, Y. F., “Arm-manipulator Coordination for Load Sharing using Variable Compliant Control,” Proceedings of the IEEE International Conference on Robotics and Automation, Albuquerque, NM, USA (Apr. 1997) pp. 895–900.Google Scholar
3.Iqbal, K. and Zheng, Y. F., “Arm-manipulator Coordination for Load Sharing using Predictive Control,” Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, MI, USA (May 1999) pp. 2539–2544.Google Scholar
4.Maeda, Y., Takayuki, H. and Tamio, A., “Human-robot Cooperative Manipulation with Motion Estimation,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, HI, USA (Nov. 2001) pp. 2240–2245.Google Scholar
5.Rahman, M. M., Ikeura, R. and Mizutani, K., “Investigation of the impedance characteristic of human arm for development of robots to cooperate with humans,” JSME Int. J. 45 (2), 510–518 (2002).CrossRefGoogle Scholar
6.Corteville, B., Aertbelien, E., Bruyninckx, H., Schutter, J. D. and Brussel, H. V., “Human-inspired Robot Assistant for Fast Point-to-point Movements,” Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy (Apr. 2007) pp. 3639–3644.Google Scholar
7.Erden, M. S. and Tomiyama, T., “Human-intent detection and physically interactive control of a robot without force sensors,” IEEE Trans. Robot. 26 (2), 370–382 (2010).Google Scholar
8.Bascetta, L., Ferretti, G., Magnani, G. and Rocco, P., “Walk-through programming for robotic manipulators based on admittance control,” Robotica 31, 1143–1153 (2013).Google Scholar
9.Li, Y. and Ge, S. S., “Human-robot collaboration based on motion intention estimation,” IEEE/ASME Trans. Mechatronics 19 (3), 1007–1014 (2014).CrossRefGoogle Scholar
10.Chung, J. H., “Control of an operator-assisted mobile robotic system,” Robotica 20, 439–446 (2002).Google Scholar
11.Stanisic, R. Z. and Fernandez, A. V., “Simultaneous velocity, impact and force control,” Robotica 27, 1039–1048 (2009).CrossRefGoogle Scholar
12.Hogan, N., “Impedance control: an approach to manipulation – Part I: Theory; Part II: Implementation; Part III: Applications,” J. Dyn. Syst. Meas. Control 107 (1), 1–24 (1985).Google Scholar
13.Li, Y. and Ge, S. S., “Impedance learning for robots interacting with unknown environments,” IEEE Trans. Control Syst. Technol. 22 (4), 1422–1432 (2014).CrossRefGoogle Scholar
14.Seraji, H. and Colbaugh, R., “Force tracking in impedanc control,” Int. J. Robot. Res. 16 (1), 97–117 (1997).CrossRefGoogle Scholar
15.Jung, S., Hsia, T. C. and Bonitz, R. G., “Force tracking impedance control of robot manipulators under unknown environment,” IEEE Trans. Control Syst. Technol. 12 (3), 474–483 (2004).CrossRefGoogle Scholar
16.Lee, K. and Buss, M., “Force Tracking Impedance Control with Variable Target Stiffness,” Proceedings of the 17th IFAC World Congress, Seoul, Korea (Jul. 2008) pp. 6751–6756.CrossRefGoogle Scholar
17.Stanisic, R. Z. and Fernandez, A. V., “Adjusting the parameters of the mechanical impedance for velocity, impact and force control,” Robotica 30, 583–597 (2012).CrossRefGoogle Scholar
18.Burdet, E. and Milner, T. E., “Quantization of human motions and learning of accurate movements,” Biol. Cybern. 78, 307–318 (1998).Google Scholar
19.Wang, Z., Peer, A. and Buss, M., “An HMM Approach to Realistic Haptic Human-robot Interaction,” Proceedings of the 3rd Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, Salt Lake City, UT, USA (Mar. 2009) pp. 374–379.Google Scholar
20.Hogan, N., “The mechanics of multi-joint posture and movement control,” Biol. Cybern. 52 (5), 315–331 (1985).Google Scholar
21.Tsumugiwa, T., Yokogawa, R. and Hara, K., “Variable Impedance Control based on Estimation of Human Arm Stiffness for Human-Robot Cooperative Calligraphic Task,” Proceedings of the IEEE International Conference on Robotics and Automation, Washington, DC, USA (May 2002) pp. 644–650.Google Scholar
22.Slotine, J. J. E. and Li, W., “On the adaptive control of robotic manipulators,” Int. J. Robot. Res. 6 (3), 49–59 (1987).Google Scholar
23.Arimoto, S., “Learning control theory for robotic motion,” Int. J. Adapt. Control Signal Process. 4 (6), 543–564 (1990).CrossRefGoogle Scholar
24.Sun, M., Ge, S. S. and Mareels, I. M. Y., “Adaptive repetitive learning control of robotic manipulators without the requirement for initial repositioning,” IEEE Trans. Robot. 22 (3), 563–568 (2006).Google Scholar
25.Yan, R., Tee, K. P. and Li, H., “Adaptive learning tracking control of robotic manipulators with uncertainties,” J. Control Theory Appl. 8 (2), 160–165 (2010).CrossRefGoogle Scholar
26.Roy, J. and Whitcomb, L. L., “Adaptive force control of position/velocity controlled robots: Theory and experiment,” IEEE Trans. Robot. Autom. 18 (2), 121–137 (2002).Google Scholar
27.Corke, P. I., “A robotics toolbox for MATLAB,” IEEE Robot. Autom. Mag. 3, 24–32 (Mar. 1996).Google Scholar
28.Ge, S. S., Cabibihan, J. J., Zhang, Z., Li, Y., Meng, C., He, H., Safizadeh, M. R., Li, Y. B. and Yang, J., “Design and Development of Nancy, a Social Robot,” Proceedings of the International Conference on Ubiquitous Robots and Ambient Intelligence, Incheon, Korea (Nov. 23–26, 2011) pp. 568–573.Google Scholar