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Comparison of Human-Robot Interaction Torque Estimation Methods in a Wrist Rehabilitation Exoskeleton

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Abstract

In this paper, experimental implementation and comparative accuracy evaluation of five methods for estimation of human-robot interaction torques are presented. These methods vary from the simplest case of using solely commanded motor torques, to partial consideration of the robot dynamics, to advanced methods considering full robot dynamics such as inverse dynamics (ID) and nonlinear disturbance observer (NDO) based algorithms. Dynamic and friction models of the exoskeleton were developed and their parameters were identified using an evolutionary optimization algorithm to ensure high parameter accuracy. When used with accurate model parameters, ID method led to 20 to 22% average error, while NDO method generated 12 to 18% average error, as evaluated in experiments with a force sensor. These values compare to average error values of up to 132% for using motor torques only, and between 25 to 69% when partial dynamics were used. A sensitivity analysis of the ID and NDO methods to inaccuracies in model parameter estimations revealed considerable sensitivity of these advanced methods to model parameter variations. A summary is provided for the typical estimation accuracy levels that can be expected of these methods and discuss the limitations and considerations that should be taken into account for their use.

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References

  1. Aksman, L.M., Carignan, C.R., Akin, D.L.: Force estimation based compliance control of harmonically driven manipulators. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp 4208–4213 (2007)

  2. Alcocer, A., Robertsson, A., Valera, A., Johansson, R.: Force estimation and control in robot manipulators. In: Robot Control 2003 (SYROCO’03): a Proceedings Volume from the 7th IFAC Symposium, Wrocław, Poland, 1–3 September 2003, vol. 1, p 55. International Federation of Automatic Control (2004)

  3. Altpeter, F.: Friction Modeling, Identification and Compensation. Ph.D. thesis, Ecole Polytechnique Federale de Lausanne (1999)

  4. An, C., Atkeson, C., Hollerbach, J.: Estimation of inertial parameters of rigid body links of manipulators. In: 24th IEEE Conference on Decision and Control. pp 990–995 (1985). https://doi.org/10.1109/CDC.1985.268648

  5. Bélanger, P. R., Dobrovolny, P., Helmy, A., Zhang, X.: Estimation of angular velocity and acceleration from shaft-encoder measurements. Int. J. Robot. Res. 17(11), 1225–1233 (1998)

    Article  Google Scholar 

  6. Berkowitz, M.: Spinal Cord Injury: an Analysis of Medical and Social Costs. Demos Medical Publishing (1998)

  7. Bernstein, N.L., Lawrence, D., Pao, L.Y., et al.: Friction modeling and compensation for haptic interfaces. In: IEEE International Conference on First Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (World Haptics Conference), pp 290–295 (2005)

  8. Brewer, B., McDowell, S., Worthen-Chaudhari, L.: Poststroke upper extremity rehabilitation: a review of robotic systems and clinical results. Top. Stroke Rehabil. 14(6), 22–44 (2007)

    Article  Google Scholar 

  9. Burgar, C.G., Lum, P.S., Shor, P.C., Van der Loos, H.F.M.: Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience. J. Rehabil. Res. Dev. 37(6), 663–73 (2000)

    Google Scholar 

  10. Bütefisch, C., Hummelsheim, H., Denzler, P., Mauritz, K.H.: Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand. J. Neurol. Sci. 130(1), 59–68 (1995)

    Article  Google Scholar 

  11. Caputo, J.M., Collins, S.H.: A universal ankle–foot prosthesis emulator for human locomotion experiments. J. Biomech. Eng. 136(3), 035,002 (2014)

    Article  Google Scholar 

  12. Celik, O., O’Malley, M.K., Boake, C., Levin, H.S., Yozbatiran, N., Reistetter, T.A.: Normalized movement quality measures for therapeutic robots strongly correlate with clinical motor impairment measures. IEEE Trans. Neural Syst. Rehabil. Eng. 18(4), 433–444 (2010)

    Article  Google Scholar 

  13. Chawda, V., Celik, O., O’Malley, M.K.: Application of levant’s differentiator for velocity estimation and increased z-width in haptic interfaces. In: IEEE World Haptics Conference (WHC 2011), pp 403–408 (2011)

  14. Chen, W.H., Ballance, D.J., Gawthrop, P.J., nReilly, J.: A nonlinear disturbance observer for robotic manipulators. IEEE Trans. Ind. Electron. 47(4), 932–938 (2000)

    Article  Google Scholar 

  15. Collins, S.H.: What do walking humans want from mechatronics? In: Proceedings of 2013 IEEE International Conference on Mechatronics (ICM), pp 24–27. IEEE (2013)

  16. Colomé, A., Pardo, D., Alenya, G., Torras, C.: External force estimation during compliant robot manipulation. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp 3535–3540 (2013)

  17. Dehghan, S.A.M., Danesh, M., Sheikholeslam, F.: Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator in the presence of parametric uncertainty. Adv. Robot. 29(4), 209–223 (2015)

    Article  Google Scholar 

  18. Engelbrecht, A.P.: Computational Intelligence: an Introduction. Wiley, New York (2007)

    Book  Google Scholar 

  19. Eom, K.S., Suh, I.H., Chung, W.K., Oh, S.R.: Disturbance observer based force control of robot manipulator without force sensor. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, pp 3012–3017 (1998)

  20. García, J.G., Robertsson, A., Ortega, J.G., Johansson, R.: Generalized contact force estimator for a robot manipulator. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp 4019–4024 (2006)

  21. Gupta, A., O’Malley, M.K.: Disturbance-observer-based force estimation for haptic feedback. J. Dyn. Syst. Meas. Control. 133(1), 014,505 (2011)

    Article  Google Scholar 

  22. Gupta, A., O’Malley, M.K., Patoglu, V., Burgar, C.: Design, control and performance of ricewrist: a force feedback wrist exoskeleton for rehabilitation and training. Int. J. Robot. Res. 27(2), 233–251 (2008)

    Article  Google Scholar 

  23. Hacksel, P., Salcudean, S.: Estimation of environment forces and rigid-body velocities using observers. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp 931–936 (1994)

  24. Hogan, N., Krebs, H.I., Rohrer, B., Fasoli, S., Stein, J., Volpe, B.T.: Technology for recovery after stroke. In: Barnes, M.P., Dobkin, B.H., Bogousslavsky, J. (eds.) Recovery After Stroke, pp 604–622. Cambridge University Press (2005)

  25. Jones, T.A., Allred, R.P., Adkins, D.A.L., Hsu, J.E., O’Bryant, A., Maldonado, M.A.: Remodeling the brain with behavioral experience after stroke. Stroke 40(3 suppl 1), S136–S138 (2009)

    Article  Google Scholar 

  26. Jung, J., Lee, J., Huh, K.: Robust contact force estimation for robot manipulators in three-dimensional space. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 220(9), 1317–1327 (2006)

    Article  Google Scholar 

  27. Korayem, M.H., Haghighi, R.: Nonlinear disturbance observer for robot manipulators in 3d space. In: Intelligent Robotics and Applications, pp 14–23. Springer (2008)

  28. Krebs, H.I., Hogan, N., Aisen, M.L., Volpe, B.T.: Robot-aided neurorehabilitation. IEEE Trans. Rehabil. Eng. 6(1), 75–87 (1998)

    Article  Google Scholar 

  29. Lee, H.S., Tomizuka, M.: Robust motion controller design for high-accuracy positioning systems. IEEE Trans. Ind. Electron. 43(1), 48–55 (1996)

    Article  Google Scholar 

  30. Lloyd-Jones, D., Adams, R., Carnethon, M., De Simone, G., Ferguson, T.B., Flegal, K., Ford, E., Furie, K., Go, A., Greenlund, K., et al.: Heart disease and stroke statistics–2009 update: a report from the american heart association statistics committee and stroke statistics subcommittee. Circulation 119(3), e21 (2009)

    Google Scholar 

  31. Martinez, J.A., Ng, P., Lu, S., Campagna, M.S., Celik, O.: Design of wrist gimbal: a forearm and wrist exoskeleton for stroke rehabilitation. In: IEEE International Conference on Rehabilitation Robotics (ICORR 2013), pp 1–6 (2013)

  32. Masia, L., Casadio, M., Giannoni, P., Sandini, G., Morasso, P.: Performance adaptive training control strategy for recovering wrist movements in stroke patients: a preliminary, feasibility study. J. Neuroeng. Rehabil. 6 (1), 1 (2009)

    Article  Google Scholar 

  33. Mohammadi, A., Tavakoli, M., Marquez, H., Hashemzadeh, F.: Nonlinear disturbance observer design for robotic manipulators. Control. Eng. Pract. 21(3), 253–267 (2013)

    Article  Google Scholar 

  34. Muir, G.D., Steeves, J.D.: Sensorimotor stimulation to improve locomotor recovery after spinal cord injury. Trends Neurosci. 20(2), 72–77 (1997)

    Article  Google Scholar 

  35. Murakami, T., Yu, F., Ohnishi, K.: Torque sensorless control in multidegree-of-freedom manipulator. IEEE Trans. Ind. Electron. 40(2), 259–265 (1993)

    Article  Google Scholar 

  36. Naerum, E., Cornellà, J., Elle, O.J.: Contact force estimation for backdrivable robotic manipulators with coupled friction. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 3021–3027 (2008)

  37. Ohishi, K., Miyazaki, M., Fujita, M.: Hybrid control of force and position without force sensor. In: Proceedings of the IEEE International Conference on Industrial Electronics, Control, Instrumentation, and Automation, pp 670–675 (1992)

  38. Ohnishi, K., Shibata, M., Murakami, T.: Motion control for advanced mechatronics. IEEE/ASME Trans. Mechatron. 1(1), 56–67 (1996)

    Article  Google Scholar 

  39. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: a Practical Approach to Global Optimization. Springer Science & Business Media, New York (2006)

    MATH  Google Scholar 

  40. Saadatzi, M., Long, D.C., Celik, O.: Torque estimation in a wrist rehabilitation robot using a nonlinear disturbance observer. In: ASME 2015 Dynamic Systems and Control Conference (2015)

  41. Spong, M.W., Hutchinson, S., Vidyasagar, M.: Robot Modeling and Control, vol. 3. Wiley, New York (2006)

    Google Scholar 

  42. Stolt, A., Linderoth, M., Robertsson, A., Johansson, R.: Force controlled robotic assembly without a force sensor. In: IEEE International Conference on Robotics and Automation (ICRA), pp 1538–1543. IEEE (2012)

  43. Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  44. Ugurlu, B., Nishimura, M., Hyodo, K., Kawanishi, M., Narikiyo, T.: A framework for sensorless torque estimation and control in wearable exoskeletons. In: 12th IEEE International Workshop on Advanced Motion Control (AMC), pp 1–7 (2012)

  45. Ugurlu, B., Nishimura, M., Hyodo, K., Kawanishi, M., Narikiyo, T.: Proof of concept for robot-aided upper limb rehabilitation using disturbance observers. IEEE Transactions on Human-Machine Systems 45(1), 110–118 (2015). https://doi.org/10.1109/THMS.2014.2362816

    Article  Google Scholar 

  46. Wahrburg, A., Zeiss, S., Matthias, B., Ding, H.: Contact force estimation for robotic assembly using motor torques. In: IEEE International Conference on Automation Science and Engineering (CASE), pp 1252–1257. IEEE (2014)

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Appendix: Dynamic Equations of the Exoskeleton

Appendix: Dynamic Equations of the Exoskeleton

$$ \tau = \textbf{M}(\boldsymbol{\theta})\boldsymbol{\ddot{\theta}}+\textbf{C}(\boldsymbol{\theta},\boldsymbol{\dot{\theta}})\boldsymbol{\dot{\theta}}+\textbf{G}(\boldsymbol{\theta}) $$
(26)
$$ \textbf{M}(\boldsymbol{\theta})= \left[\begin{array}{cc} m_{11} & m_{12} \\ m_{21} & m_{22} \end{array}\right] $$
(27)
$$\begin{array}{@{}rcl@{}} m_{11} &=& i_{2xx} + i_{1yy} + i_{2xy}sin(2\theta_{2}) \\ && + c_{1x}^{2}m_{1} + c_{2y}^{2}m_{2} + c_{1z}^{2}m_{1} \\ && + c_{2z}^{2}m_{2} - i_{2xx}sin(\theta_{2})^{2}\\ && + i_{2yy}sin(\theta_{2})^{2} + c_{2x}^{2}m_{2}sin(\theta_{2})^{2}\\ && - c_{2y}^{2}m_{2}sin(\theta_{2})^{2} - c_{2x}c_{2y}m_{2}sin(2\theta_{2}) \end{array} $$
$$\begin{array}{@{}rcl@{}} m_{12} &=& m_{21} = -i_{2yz}sin(\theta_{2}) - i_{2xz}cos(\theta_{2})\\ &&+ c_{2y}c_{2z}m_{2}sin(\theta_{2}) + c_{2x}c_{2z}m_{2}cos(\theta_{2}) \end{array} $$
$$m_{22} = m_{2}c_{2x}^{2} + m_{2}c_{2y}^{2} + i_{2zz} $$
$$ \textbf{C}(\boldsymbol{\theta},\boldsymbol{\dot{\theta}})\boldsymbol{\dot{\theta}}=\left[ \begin{array}{c} c_{1} \\ c_{2} \end{array} \right] $$
(28)
$$\begin{array}{@{}rcl@{}} c_{1} &=& i_{2xz}\dot{\theta}_{2}^{2}sin(\theta_{2}) - i_{2yz}\dot{\theta}_{2}^{2}cos(\theta_{2})\\ &&+ 2i_{2xy}\dot{\theta}_{1}\dot{\theta}_{2}cos(2\theta_{2})- i_{2xx}\dot{\theta}_{1}\dot{\theta}_{2}sin(2\theta_{2})\\ &&+ i_{2yy}\dot{\theta}_{1}\dot{\theta}_{2}sin(2\theta_{2})- c_{2x}c_{2z}m_{2}\dot{\theta}_{2}^{2}sin(\theta_{2})\\ &&+ c_{2y}c_{2z}m_{2}\dot{\theta}_{2}^{2}cos(\theta_{2}) + c_{2x}^{2}m_{2}\dot{\theta}_{1}\dot{\theta}_{2}sin(2\theta_{2})\\ &&- c_{2y}^{2}m_{2}\dot{\theta}_{1}\dot{\theta}_{2}sin(2\theta_{2}) - 2c_{2x}c_{2y}m_{2}\dot{\theta}_{1}\dot{\theta}_{2}cos(2\theta_{2}) \end{array} $$
$$\begin{array}{@{}rcl@{}} c_{2} &=& -i_{2}{xy}\dot{\theta}_{1}^{2}cos(2\theta_{2}) + 0.5i_{2xx}\dot{\theta}_{1}^{2}sin(2\theta_{2})\\ &&- 0.5i_{2yy}\dot{\theta}_{1}^{2}sin(2\theta_{2}) -0.5c_{2x}^{2}m_{2}\dot{\theta}_{1}^{2}sin(2\theta_{2})\\ &&+ 0.5c_{2y}^{2}m_{2}\dot{\theta}_{1}^{2}sin(2\theta_{2}) + c_{2x}c_{2y}m_{2}\dot{\theta}_{1}^{2}cos(2\theta_{2}) \end{array} $$
$$ \textbf{G}(\boldsymbol{\theta}) = \left[ \begin{array}{c} g_{1} \\ g_{2} \end{array} \right] $$
(29)
$$\begin{array}{@{}rcl@{}} g_{1} &=& g(c_{1x}m_{1}sin(\theta_{1}) + c_{1z}m_{1}cos(\theta_{1}) + c_{2z}m_{2}cos(\theta_{1})\\ &&- c_{2x}m_{2}sin(\theta_{2})sin(\theta_{1}) + c_{2y}m_{2}cos(\theta_{2})sin(\theta_{1})) \end{array} $$
$$ g_{2} = gm_{2}(c_{2y}sin(\theta_{2})cos(\theta_{1}) + c_{2x}cos(\theta_{2})cos(\theta_{1})). $$
(30)

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Saadatzi, M., Long, D.C. & Celik, O. Comparison of Human-Robot Interaction Torque Estimation Methods in a Wrist Rehabilitation Exoskeleton. J Intell Robot Syst 94, 565–581 (2019). https://doi.org/10.1007/s10846-018-0786-8

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