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A Human Arm’s Mechanical Impedance Tuning Method for Improving the Stability of Haptic Rendering

Published online by Cambridge University Press:  21 July 2020

Xiong Lu Open the ORCID record for Xiong Lu [Opens in a new window] ,
Beibei Qi ,
Hao Zhao  and
Junbin Sun
Xiong Lu*
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing211106, China
Beibei Qi
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing211106, China
Hao Zhao
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing211106, China
Junbin Sun
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing211106, China
*
*Corresponding author. E-mail: [email protected]
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Summary

Rendering of rigid objects with high stiffness while guaranteeing system stability remains a major and challenging issue in haptics. Being a part of the haptic system, the behavior of human operators, represented as the mechanical impedance of arm, has an inevitable influence on system performance. This paper first verified that the human arm impedance can unconsciously be modified through imposing background forces and resist unstable motions arising from external disturbance forces. Then, a reliable impedance tuning (IT) method for improving the stability and performance of haptic systems is proposed, which tunes human arm impedance by superimposing a position-based background force over the traditional haptic workspace. Moreover, an adaptive IT algorithm, adjusting the maximum background force based on the velocity of the human arm, is proposed to achieve a reasonable trade-off between system stability and transparency. Based on a three-degrees-of-freedom haptic device, maximum achievable stiffness and transparency grading experiments are carried out with 12 subjects, which verify the efficacy and advantage of the proposed method.

Keywords

Haptic renderingSystem stabilityImpedance tuningMagnetic fieldMechanical impedanceMaximum achievable stiffness
Type
Articles
Information
Robotica , Volume 39 , Issue 4 , April 2021 , pp. 652 - 664
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Chen, X. J. and Hu, J. L., “A review of haptic simulator for oral and maxillofacial surgery based on virtual reality,” Expert Rev Med Devic. 15(6), 435–444 (2018).CrossRefGoogle ScholarPubMed
Kim, D. H., Kim, Y., Park, J. S. and Kim, S. W., “Virtual reality simulators for endoscopic sinus and skull base surgery: The present and future,” Clin Exp Otorhinolar. 12(1), 12–17 (2019).Google ScholarPubMed
Abidi, M. H., Al-Ahmari, A. M., Ahmad, A., Darmoul, S. and Ameen, W., “Semi-immersive virtual turbine engine simulation system,” Int J Turbo Jet Eng. 35(2), 149–160 (2018).Google Scholar
Aviles-Rivero, A. I., Alsaleh, S. M., Philbeck, J., Raventos, S. P., Younes, N., Hahn, J. K. and Casals, A., “Sensory substitution for force feedback recovery: A perception experimental study,” ACM T Appl Percept. 15(3) (2018).Google Scholar
Colgate, J. E., Grafing, P. E., Stanley, M. C. and Schenkel, G., “Implementation of stiff virtual walls in force-reflecting interfaces,” IEEE Virtual Reality Annual International Symposium, (1993) pp. 202–208.Google Scholar
Colgate, J. E. and Schenkel, G., “Passivity of a Class of Sampled-Data Systems - Application to Haptic Interfaces,” Proceedings of the 1994 American Control Conference, (1994) pp. 3236–3240.Google Scholar
Colgate, J. E., Stanley, M. C. and Brown, J. M., “Issues in the haptic display of tool use,” Iros ’95–1995 Ieee/Rsj International Conference on Intelligent Robots and Systems: Human Robot Interaction and Cooperative Robots, Proceedings, (1995) pp. 140–145.Google Scholar
Colgate, J. E., Brown, J. M. and IEEE, “Factors affecting the Z-width of a haptic display,” in 1994 Ieee International Conference on Robotics and Automation: Proceedings, Vols 1-4 (Ieee International Conference on Robotics and Automation, Los Alamitos: IEEE, Computer Soc Press (1994) pp. 3205–3210.Google Scholar
Hu, L. Y., Li, J. H., Liu, X. P., Xiong, P. W. and He, S. X., “The maximum output force controller and its application to a virtual surgery system,” Int J Adv Robot Syst. 15(2), 10 (2018).CrossRefGoogle Scholar
Mendez, V., Tavakoli, M. and Li, J., “A method for passivity analysis of multilateral haptic systems,” Adv Robotics. 28(18), 1205–1219 (2014).CrossRefGoogle Scholar
Mashayekhi, A., Boozarjomehry, R. B., Nahvi, A., Meghdari, A. and Asgari, P., “Improved passivity criterion in haptic rendering: Influence of Coulomb and viscous friction,” Adv Robotics. 28(10), 695–706 (2014).CrossRefGoogle Scholar
Budai, C., Kovacs, L. L. and Kovecses, J., “Combined effect of sampling and coulomb friction on haptic systems dynamics,” J Comput Nonlin Dyn. 13(6), 10 (2018).Google Scholar
Baek, S. Y., Park, S. and Ryu, J., “An enhanced force bounding approach for stable haptic interaction by including friction,” Int J Precis Eng Man. 18(6), 813–824 (2017).CrossRefGoogle Scholar
Park, S., Uddin, R. and Ryu, J., “Stiffness-reflecting energy-bounding approach for improving transparency of delayed haptic interaction systems,” Int J Control Autom. 14(3), 835–844 (2016).CrossRefGoogle Scholar
Mashayekhi, A., Behbahani, S., Ficuciello, F. and Siciliano, B., “Analytical stability criterion in Haptic Rendering: The Role of Damping,” IEEE-ASME T Mech. 23(2), 596–603 (2018).CrossRefGoogle Scholar
Lapanaphan, N. and Bohez, E. L. J., “High impedance actuator fusion: A new concept for a haptic system,” Int J Robot Autom. 30(5), 447–457 (2015).Google Scholar
Jafari, A., Nabeel, M. and Ryu, J. H., “The input-to-state stable (ISS) approach for stabilizing haptic interaction with virtual environments,” IEEE T Robot. 33(4), 948–963 (2017).CrossRefGoogle Scholar
Koul, M., Manivannan, M. and Saha, S. K., “Effect of dual-rate sampling on the stability of a haptic interface,” J Intell Robot Syst. 91(3–4), 479–491 (2018).CrossRefGoogle Scholar
Dang, Q. V., Vermeiren, L., Dequidt, A. and Dambrine, M., “Experimental study on the stability of an impedance-type force-feedback architecture based on an augmented-state observer for a haptic system under time delay using a LMI approach,” P I Mech Eng I-J Sys. 230(1), 58–71 (2016).Google Scholar
Liu, Y. W., Meng, F. W., Guan, B. W. and Zhang, S. H., “Robust stability analysis based on LMI for haptic interface systems with uncertain delay,” Complexity. 2018(7), Article ID 9342479, 1–10 (2018).Google Scholar
Kim, M. and Lee, D. Y., “Multirate haptic rendering using local stiffness matrix for stable and transparent simulation involving interaction with deformable objects,” IEEE T Ind Electron. 67(1), 820–828 (2020).Google Scholar
Campeau-Lecours, A., Otis, M., Belzile, P. L. and Gosselin, C., “A time-domain vibration observer and controller for physical human-robot interaction,” Mechatronics. 36, 45–53 (2016).CrossRefGoogle Scholar
Aydin, Y., Tokatli, O., Patoglu, V. and Basdogan, C., “Stable physical human-robot interaction using fractional order admittance control,” IEEE T Haptics. 11(3), 464–475 (2018).CrossRefGoogle Scholar
Lu, X. and Song, A. G., “Stable haptic rendering with detailed energy-compensating control,” Comput Graph-UK. 32(5), 561–567 (2008).CrossRefGoogle Scholar
Amirkhani, S., Bahadorian, B., Nahvi, A. and Chaibakhsh, A., “Stable haptic rendering in interactive virtual control laboratory,” Intel Serv Robot. 11(3), 289–300 (2018).CrossRefGoogle Scholar
Li, H. B., Zhang, L. and Kawashima, K., “Operator dynamics for stability condition in haptic and teleoperation system: A survey,” Int J Med Robot Comp. 14(2), 10 (2018)Google ScholarPubMed
Burdet, E., Osu, R., Franklin, D. W., Milner, T. E. and Kawato, M., “The central nervous system stabilizes unstable dynamics by learning optimal impedance,” Nature. 414(6862), 446–449 (2001).CrossRefGoogle ScholarPubMed
Bi, Q., Yang, C. J., Deng, X. L. and Fan, J. C., “Human finger mechanical impedance modeling: Using multiplicative uncertain model,” P I Mech Eng C-J Mec. 230(12), 1978–1986 (2016).CrossRefGoogle Scholar
Woo, H. S. and Lee, D. Y., “Exploitation of the impedance and characteristics of the human arm in the design of haptic interfaces,” IEEE T Ind Electron. 58(8), 3221–3233 (2011).CrossRefGoogle Scholar
Besa, A. J., Valero, F. J., Suner, J. L. and Carballeira, J., “Characterisation of the mechanical impedance of the human hand-arm system: The influence of vibration direction, hand-arm posture and muscle tension,” INT J Ind Ergonom. 37(3), 225–231 (2007).CrossRefGoogle Scholar
Piovesan, D., Pierobon, A. and Ivaldi, F. A. M., “Critical damping conditions for third order muscle models: Implications for force control,” J Biomech Eng-T ASME. 135(10), 8 (2013).CrossRefGoogle ScholarPubMed
Krutky, M. A., Trumbower, R. D. and Perreault, E. J., “Influence of environmental stability on the regulation of end-point impedance during the maintenance of arm posture,” J Neurophysiol. 109(4), 1045–1054 (2013).CrossRefGoogle ScholarPubMed
Krutky, M. A., Ravichandran, V. J., Trumbower, R. D. and Perreault, E. J., “Interactions between limb and environmental mechanics influence stretch reflex sensitivity in the human arm,” J Neurophysiol. 103(1), 429–440 (2010).CrossRefGoogle ScholarPubMed
Mizrahi, J., “Mechanical impedance and its relations to motor control, limb dynamics, and motion biomechanics,” J Med Biol Eng. 35(1), 1–20 (2015).CrossRefGoogle ScholarPubMed
Dehghan, S. A. M., Koofigar, H. R. and Ekramian, M., “An adaptive arm’s mechanical impedance estimator for rehabilitation robots without force and acceleration sensors,” Int J Syst Sci. 49(13), 2784–2796 (2018).CrossRefGoogle Scholar
Piovesan, D., Kolesnikov, M., Lynch, K. and Mussa-Ivaldi, F. A., “The concurrent control of motion and contact force in the presence of predictable disturbances,” J Mech Robot-Trans ASME. 11(6), 14 (2019).Google Scholar
Song, A. G., Pan, L. Z., Xu, G. Z. and Li, H. J., “Adaptive motion control of arm rehabilitation robot based on impedance identification,” Robotica. 33(9), 1795–1812 (2015).CrossRefGoogle Scholar
Xiong, L., Ai-guo, S. and Yong-qiang, Y., “Improved haptic rendering through tuning the mechanical impedance of human arm,” 2011 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems (2011) pp. 5 Google Scholar
Hao, Z., Chen-xi, X. and Xiong, L., “Influence of Mechanical Impedance of Human Arm on the Stability of Haptic Rendering,” 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) (2012) pp. 130–134.Google Scholar
Hogan, N., “Controlling impedance at the man/machine interface,” Proceedings. 1989 IEEE International Conference on Robotics and Automation (Cat. No.89CH2750-8), (1989) pp. 1626–1631.Google Scholar