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Impedance matching control between a human arm and a haptic joystick for long-term

Published online by Cambridge University Press:  25 October 2021

Jiwook Choi ,
Zhanming Gu ,
Jangmyung Lee Open the ORCID record for Jangmyung Lee [Opens in a new window]  and
Inho Lee
Jiwook Choi
Affiliation:
Department of Electronics Engineering, Pusan National University, Busan609-735, Korea
Zhanming Gu
Affiliation:
Department of Electronics Engineering, Pusan National University, Busan609-735, Korea
Jangmyung Lee
Affiliation:
Department of Electronics Engineering, Pusan National University, Busan609-735, Korea
Inho Lee*
Affiliation:
Department of Electronics Engineering, Pusan National University, Busan609-735, Korea
*
*Corresponding author. E-mail: [email protected]
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Abstract

An impedance matching control framework between a human and a haptic joystick for long-term teleoperation is proposed in this research. An impedance model of the human arm is established analyzing the characteristics of human perception, decision, and action. The coefficients of the human arm’s impedance have been identified using a least squares method. The human arm’s impedance matching algorithm generates a corresponding motion vector for the human arm, which is determined by the interaction force measured by a force/torque sensor considering the impedance modeling of the human arm. The impedance control has been adopted for the haptic joystick to match the desired impedance to that of the human arm, which is aimed to minimize the energy consumption of the human arm for long-term teleoperation. By minimizing the fatigue of the operator, the remote control accuracy of the teleoperation can be improved. A PD control with gravity compensation algorithm has been adopted to maintain desired trajectory for the joystick by the operator more conveniently. The effectiveness of matching control has been demonstrated by trajectory following experiments for a mobile robot.

Keywords

human arm’s impedance modelingimpedance estimationimpedance matching controlhaptic joystick
Type
Reply
Information
Robotica , Volume 40 , Issue 6 , June 2022 , pp. 1880 - 1893
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

†

Deceased

References

Takemoto, A. and Yano, K., “Obstacle avoidance control system of rotary crane using proposed haptic joystick,” in World Haptics Conference (IEEE press, Pisa, Italy, 2005), pp. 22–23.Google Scholar
Qin, Q. and Yutaka, I., “Softness Comparison of Stabilization Control in Remote Robot System with Force Feedback,” TENCON 2018 IEEE Region 10 Conference (IEEE press, Jeju, Korea, 2018), pp. 32–37.10.1109/TENCON.2018.8650151CrossRefGoogle Scholar
Krishnan, K. R. R. and Ganesh, M., “Reshaping the Singularities in 3 D.O.F 3-RRR Planar Parallel Manipulator by Varying the Position of the Actuator,” Int. Conf. on Robotics: Current Trends and future Challenges (RCTFC) (IEEE Press, India, Thanjavur, 2016), pp. 37–42.10.1109/RCTFC.2016.7893413CrossRefGoogle Scholar
Cho, S. K. and Jin, H. Z., Teleoperation of a mobile robot using a force-reflection joystick with sensing mechanism of rotating magnetic field, IEEE/ASME Trans. Mechatronics. 15(1), 17–26 (2010).Google Scholar
Lee, S. S. and Lee, J. M., “Design of a general purpose 6-DOF haptic interface,” Mechatronics, 13(7), 697–722 (2003).10.1016/S0957-4158(02)00038-7CrossRefGoogle Scholar
Huang, J. and Huo, W.-G., “Control of upper-limb power-assist exoskeleton using a human-robot interface based on motion intention recognition,” IEEE Trans. Autom. Sci. Eng. 12(4), 1257–1270 (2003).10.1109/TASE.2015.2466634CrossRefGoogle Scholar
Tong, L.-N. and Hou, Z.-G., “Multi-channel sEMG time series analysis based human motion recognition method,” Acta Automatica Sinica 40(5), 810–821 (2014).Google Scholar
Ngeo, J. G. and Tamei, T., “Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model,” J NeuroEng. Rehabil. 11(122), 1–14 (2014).10.1186/1743-0003-11-122CrossRefGoogle ScholarPubMed
Hashemi, J. and Morin, E., “Enhanced dynamic EMG-force estimation through calibration and PCI modeling,” IEEE Trans. Neural Syst. Rehabil. Eng. 23(1), 41–50 (2015).CrossRefGoogle ScholarPubMed
Parajuli, N. and Bifulco, P., “Real-time EMG based pattern recognition control for hand prostheses: A review on existing methods, challenges and future implementation,” Sensors 19(20), 15–23 (2019).10.3390/s19204596CrossRefGoogle ScholarPubMed
Mulas, M. and Folgheraiter, M., “An EMG-Controlled Exoskeleton for Hand Rehabilitation,” Int. Conf. on Rehabilitation Robotics (ICORR) (IEEE press, Chicago, USA, 2005), pp. 371–374.Google Scholar
Scheme, E. and Englehart, K., “Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use,” J. Rehabil. Res. Dev. 48(6), 643–659 (2011).10.1682/JRRD.2010.09.0177CrossRefGoogle ScholarPubMed
Ibarra, J. C. P. and Siqueira, A. A.-G., “Impedance Control of Rehabilitation Robots for Lower Limbs, Review,” Conf. on Robotics: SBR-LARS Robotics Symposium and Robo-control (IEEE Press, Sao Carlos, Brazil, 2014), pp. 235–240.CrossRefGoogle Scholar
Li, Y. N. and He, W., “Neural network control of a rehabilitation robot by state and output feedback,” J. Intell. & Rob. Sys. 80(1), 15–31 (2015).Google Scholar
Napper, S. A. and Seaman, R. L., “Applications of robots in rehabilitation,” Robot. Auton. Syst. 5(3), 227–239 (1989).10.1016/0921-8890(89)90047-XCrossRefGoogle Scholar
Hogan, N., “Impedance Control: An Approach to Manipulation,” 1984 American Control Conference (IEEE Press, San Diego, USA, 1984), pp. 304–313.10.23919/ACC.1984.4788393CrossRefGoogle Scholar
Bernhardt, M. and Frey, M., “Hybrid Force-Position Control Yields Cooperative Behavior of the Rehabilitation Robot LOKOMAT,” Int. Conf. on Rehabilitation Robotics (ICORR) (IEEE Press, Chicago, USA, 2005), pp. 536–539.Google Scholar
Ficuciello, F. and Romano, A., “Cartesian Impedance Control of Redundant Manipulators for Human-Robot Co-Manipulation,” Int. Conf. on Intelligent Robots and Systems (IEEE Press, Chicago, USA, 2014), pp. 2120–2125.10.1109/IROS.2014.6942847CrossRefGoogle Scholar
Ficuciello, F. and Romano, A., “Variable impedance control of redundant manipulators for intuitive Human–Robot physical interaction,” IEEE Trans. Robot. 31(4) (2015) pp. 850–863.CrossRefGoogle Scholar
Wang, X. L, “Force Estimation Based Position Impedance Control for Robotic Machining Process,” Int. Conf. on Mechanic Automation and Control Engineering (IEEE Press, Wuhan, China, 2010), pp. 37–40.Google Scholar
Artemiadis, P. K. and Katsiaris, P. T., “Human Arm Impedance: Characterization and Modeling in 3D Space,” Int. Conf. on Intelligent Robots and Systems (IEEE Press, Taipei, Taiwan, 2010), pp. 3103–3108.10.1109/IROS.2010.5652025CrossRefGoogle Scholar
Li, H. J. and Song, A. G., “Virtual-environment modeling and correction for force-reflecting teleoperation with time delay,” IEEE Trans. Ind. Electron. 54(2), 1227–1233 (2007).Google Scholar
Kook, H. S. and Kota, S., “A Dynamic Multi-Channel Decision-Fusion Strategy to Classify Differential Brain Activity,” Int. Conf. of the IEEE Engineering in Medicine and Biology Society (IEEE Press, Louisville, USA, 2007), pp. 3212–3215.10.1109/IEMBS.2007.4353013CrossRefGoogle Scholar
Talukdar, C. and Barman, R., “Analysis of noise and its removal in nerve conduction study signal,” Int. Conf. on Innovations in Electronics, Signal Processing and Communication (IESC) (IEEE Press, Louisville, Shillong, India, 2017), pp. 143–148.CrossRefGoogle Scholar
Merfeld, D. M. and Clark, T. K., “Dynamics of individual perceptual decisions,” IEEE Trans. Robot. 115(1), 39–59 (2016).Google ScholarPubMed
Atsushi, F. and Shohei, O., “A Muscle Tension Estimation Method by using Mechanical Impedance of Human Knee Joint During Training by Manipulator,” Int. Conf. on Mechatronics (IEEE Press, Changchun, China, 2007), pp. 1–6.Google Scholar
Song, A., Pan, L., Xu, G. and Li, H., “Impedance identification and adaptive control of rehabilitation robot for upper-limb passive training, foundations and applications of intelligent systems,” Adv. Intell. Syst. Comput. 213, 691–710 (2013).Google Scholar