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An Identification-Based Method Improving the Transparency of a Robotic Upper Limb Exoskeleton

Published online by Cambridge University Press:  03 February 2021

Dorian Verdel Open the ORCID record for Dorian Verdel [Opens in a new window] ,
Simon Bastide ,
Nicolas Vignais ,
Olivier Bruneau  and
Bastien Berret
Dorian Verdel*
Affiliation:
Université Paris-Saclay, ENS Paris-Saclay, LURPA, 94235 Cachan, France E-mail: olivier.bruneau@ens-paris-saclay.fr Université Paris-Saclay, CIAMS, 91405 Orsay, France E-mails: simon.bastide@universite-paris-saclay.fr, nicolas.vignais@universite-paris-saclay.fr, bastien.berret@universite-paris-saclay.fr CIAMS, Université d’Orléans, 45067 Orléans, France
Simon Bastide
Affiliation:
Université Paris-Saclay, CIAMS, 91405 Orsay, France E-mails: simon.bastide@universite-paris-saclay.fr, nicolas.vignais@universite-paris-saclay.fr, bastien.berret@universite-paris-saclay.fr CIAMS, Université d’Orléans, 45067 Orléans, France
Nicolas Vignais
Affiliation:
Université Paris-Saclay, CIAMS, 91405 Orsay, France E-mails: simon.bastide@universite-paris-saclay.fr, nicolas.vignais@universite-paris-saclay.fr, bastien.berret@universite-paris-saclay.fr CIAMS, Université d’Orléans, 45067 Orléans, France
Olivier Bruneau
Affiliation:
Université Paris-Saclay, ENS Paris-Saclay, LURPA, 94235 Cachan, France E-mail: olivier.bruneau@ens-paris-saclay.fr
Bastien Berret
Affiliation:
Université Paris-Saclay, CIAMS, 91405 Orsay, France E-mails: simon.bastide@universite-paris-saclay.fr, nicolas.vignais@universite-paris-saclay.fr, bastien.berret@universite-paris-saclay.fr CIAMS, Université d’Orléans, 45067 Orléans, France Institut Universitaire de France, Paris, France
*
*Corresponding author. E-mail: dorian.verdel@ens-paris-saclay.fr
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Summary

Over the past decade, research on human–robot collaboration has grown exponentially, motivated by appealing applications to improve the daily life of patients/operators. A primary requirement in many applications is to implement highly “transparent” control laws to reduce the robot impact on human movement. This impact may be quantified through relevant motor control indices. In this paper, we show that control laws based on careful identification procedures improve transparency compared to classical closed-loop position control laws. A new performance index based on the ratio between electromyographic activity and limb acceleration is also introduced to assess the quality of human exoskeleton interaction.

Keywords

Control lawsIdentificationHuman/Robot interactionTransparency indicesMotor control
Type
Article
Information
Robotica , Volume 39 , Issue 9 , September 2021 , pp. 1711 - 1728
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

a

These authors contributed equally.

References

Huo, W., Arnez-Paniagua, V., Ding, G., Amirat, Y. and Samer, M., “Adaptive proxy-based controller of an active ankle foot orthosis to assist lower limb movements of paretic patients,” Robotica 37(12), 2147–2164 (2019).CrossRefGoogle Scholar
Bogue, R., “Robotic exoskeletons: A review of recent progress,” Ind. Robot Int. J. 42(1), 5–10 (2015).Google Scholar
de Looze, M. P., Bosch, T., Krause, F., Stadler, K. S. and O’Sullivan, L. W., “Exoskeletons for industrial application and their potential effects on physical work load,” Ergonomics 59(5), 671–681 (2016).CrossRefGoogle ScholarPubMed
Frisoli, A., Procopio, C., Chisari, C., Creatini, I., Bonfiglio, L., Bergamasco, M., Rossi, B. and Carboncini, M. C., “Positive effects of robotic exoskeleton training of upper limb reaching movements after stroke,” J. Neuroeng. Rehabilit. 9(1), 36 (2012).CrossRefGoogle ScholarPubMed
Frisoli, A., Borelli, L., Montagner, A., Marcheschi, S., Procopio, C., Salsedo, F., Bergamasco, M., Carboncini, M. C., Tolaini, M. and Rossi, B., “Arm Rehabilitation with a Robotic Exoskeleleton in Virtual Reality,” IEEE 10th International Conference on Rehabilitation Robotics, 2007. ICORR 2007 (IEEE, 2007) pp. 631–642.Google Scholar
Sylla, N., Bonnet, V., Colledani, F. and Fraisse, P., “Ergonomic contribution of ABLE exoskeleton in automotive industry,” Int. J. Ind. Ergon. 44(4), 475–481 (2014).CrossRefGoogle Scholar
Mooney, L. M., Rouse, E. J. and Herr, H. M., “Autonomous exoskeleton reduces metabolic cost of human walking during load carriage,” J. NeuroEng. Rehabil. 11(1), 80 (2014).CrossRefGoogle ScholarPubMed
Veerbeek, J. M., Langbroek-Amersfoort, A. C., van Wegen, E. E., Meskers, C. G. and Kwakkel, G., “Effects of robot-assisted therapy for the upper limb after stroke: A systematic review and meta-analysis,” Neurorehabil. Neural Repair 31(2), 107–121 (2017).CrossRefGoogle Scholar
Bastide, S., Vignais, N., Geffard, F. and Berret, B., “Interacting with a ‘Transparent’ Upper-Limb Exoskeleton: A Human Motor Control Approach,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018) pp. 4661–4666.Google Scholar
Jarrassé, N., Tagliabue, M., Robertson, J. V. G., Maiza, A., Crocher, V., Roby-Brami, A. and Morel, G., “A methodology to quantify alterations in human upper limb movement during co-manipulation with an exoskeleton,” IEEE Trans. Neural Syst. Rehabil. Eng. 18(4), 389–397 (2010).CrossRefGoogle ScholarPubMed
Di Natali, C., Poliero, T., Sposito, M., Graf, E., Bauer, C., Pauli, C., Bottenberg, E., De Eyto, A., O’Sullivan, L., Hidalgo, A. F., Scherly, D., Stadler, K. S., Caldwell, D. G. and Jess, O., “Design and evaluation of a soft assistive lower limb exoskeleton,” Robotica 37, 1–21 (2019).CrossRefGoogle Scholar
Ercolini, G., Trigili, E., Baldoni Simona Crea, A. and Vitiello, N., “A novel generation of ergonomic upper-limb wearable robots: Design challenges and solutions,” Robotica 37 (12,SI), 2056–2072 (2018).Google Scholar
Huang, V. S. and Krakauer, J. W., “Robotic neurorehabilitation: A computational motor learning perspective,” J. NeuroEng. Rehabil. 6(5), 1–13 (2009).CrossRefGoogle ScholarPubMed
Jarrassé, N. and Morel, G., “Connecting a human limb to an exoskeleton,” IEEE Trans. Robot. 28(3), 697–709 (2012).CrossRefGoogle Scholar
Jin, X., Cai, Y., Prado, A. and Agrawal, S. K., “Effects of Exoskeleton Weight and Inertia on Human Walking,” 2017 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, Singapore, 2017) pp. 1772–1777.CrossRefGoogle Scholar
Martelli, D., Vannetti, F., Cortese, M., Tropea, P., Francesco, G., Micera, S., Monaco, V. and Vitiello, N., “The effects on biomechanics of walking and balance recovery in a novel pelvis exoskeleton during zero-torque control,” Robotica 32(08), 1317–1330 (2014).CrossRefGoogle Scholar
Xiloyannis, M., Chiaradia, D., Frisoli, A. and Masia, L., “Physiological and kinematic effects of a soft exosuit on arm movements,” J. NeuroEng. Rehabilit. 16(1), 29 (2019).CrossRefGoogle ScholarPubMed
Proietti, T., Crocher, V., Roby-Brami, A. and Jarrasse, N., “Upper-limb robotic exoskeletons for neurorehabilitation: A review on control strategies,” IEEE Rev. Biomed. Eng. 9, 4–14 (2016).CrossRefGoogle ScholarPubMed
Jarrassé, N., Paik, J., Pasqui, V. and Morel, G., “How can Human Motion Prediction Increase Transparency?,” IEEE International Conference on Robotics and Automation (2008) pp. 2134–2139.Google Scholar
Ajoudani, A., Zanchettin, A. M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K. and Khatib, O., “Progress and prospects of the human-robot collaboration,” Auto. Robots 42, 957–975 (2017).CrossRefGoogle Scholar
Pirondini, E., Coscia, M., Marcheschi, S., Roas, G., Salsedo, F., Frisoli, A., Bergamasco, M. and Micera, S., “Evaluation of the effects of the Arm Light Exoskeleton on movement execution and muscle activities: A pilot study on healthy subjects,” J. NeuroEng. Rehabil. 13, (2016), Article 9.CrossRefGoogle ScholarPubMed
Atkeson, C. G. and Hollerbach, J. M., “Kinematic features of unrestrained vertical arm movements,” J. Neurosci. 5(9), 2318–2330 (1985).CrossRefGoogle ScholarPubMed
Gaveau, J., Berret, B., Demougeot, L., Fadiga, L., Pozzo, T. and Papaxanthis, C., “Energy-related optimal control accounts for gravitational load: comparing shoulder, elbow, and wrist rotations,” J. Neurophysiol. 111 (1), 4–16 (2014).CrossRefGoogle ScholarPubMed
Flash, T. and Hogan, N., “The coordination of arm movements: An experimentally confirmed mathematical model,” J. Neurosci. 5(7), 1688–1703 (1985).CrossRefGoogle ScholarPubMed
Morasso, P., “Spatial control of arm movements,” Exp. Brain Res. 42(2), 223–227 (1981).CrossRefGoogle ScholarPubMed
Garrec, P., “Screw and Cable Acutators (SCS) and Their Applications to Force Feedback Teleoperation, Exoskeleton and Anthropomorphic Robotics,” In: Robotics 2010 Current and Future Challenges (2010) pp. 167–191.Google Scholar
Garrec, P., Friconneau, J. P., Méasson, Y. and Perrot, Y., “ABLE, an Innovative Transparent Exoskeleton for the Upper-Limb,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2008) pp. 1483–1488.Google Scholar
Mallat, R., Khalil, M., Venture, G., Bonnet, V. and Mohammed, S., “Human-Exoskeleton Joint Misalignment: A Systematic Review,” In 2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME) (IEEE, 2019).Google Scholar
Schiele, A. and van der Helm, F. C. T., “Kinematic design to improve ergonomics in human machine interaction,” IEEE Trans. Neural Syst. Rehabil. Eng. 14(4), 456–469 (2006).CrossRefGoogle ScholarPubMed
Peternel, L., Noda, T., Petrič, T., Ude, A., Morimoto, J. and Babič, J., “Adaptive control of exoskeleton robots for periodic assistive behaviours based on EMG feedback minimisation,” PLOS ONE 11(2), e0148942, 1–26 (2016).CrossRefGoogle ScholarPubMed
Carignan, C., Tang, J. and Roderick, S., “Development of an Exoskeleton Haptic Interface for Virtual Task Training,” 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2009).Google Scholar
Hamon, P., Gautier, M. and Garrec, P., “Dynamic Identification of Robots with a Dry Friction Model Depending on Load and Velocity,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010) pp. 6187–6193.Google Scholar
Franken, M., Stramigioli, S., Reilink, R., Secchi, C. and Macchelli, A., “Bridging the gap between passivity and transparency,” Robot. Sci. Syst. V 36, 36–44 (2009).Google Scholar
Khalil, W. and Dombre, E., Modélisation, identification et commande des robot (2003).Google Scholar
Moberg, S., Modeling and Control of Flexible Manipulators Ph.D. Thesis (Linkping University, 2010).Google Scholar
Östring, M., Gunnarsson, S. and Norrlöf, M., “Closed-loop identification of an industrial robot containing flexibilities,” Control Eng. Practice 11(3), 291–300 (2003).CrossRefGoogle Scholar
Ljung, L., System Identification: Theory for the User (Prentice Hall PTR, Upper Saddle River, New Jersey, 1999).Google Scholar
Gong, C., Yuan, J. and Ni, J., “Nongeometric error identification and compensation for robotic system by inverse calibration,” Int. J. Mach. Tools Manuf. 40(14), 2119–2137 (2000).CrossRefGoogle Scholar
Hayati, S. and Mirmirani, M., “Improving the absolute positioning accuracy of robot manipulators,” J. Robot. Syst. 2(4), 397–413 (1985).CrossRefGoogle Scholar
Renders, J.-M., Hanus, R. and Rossignol, E., “Kinematic calibration and geometrical parameter identification for robots,” IEEE Trans. Robot. Autom. 7, 721–732 (1992).CrossRefGoogle Scholar
Gautier, M., “Dynamic Identification of Robots with Power Model,” Proceedings of the 1997 IEEE International Conference on Robotics and Automation (1997) pp. 1922–1927.Google Scholar
Geffard, F., Andriot, C., Micaelli, A. and Morel, G., “On the Use of a Base Force/Torque Sensor in Teleoperation,” IEEE International Conference on Robotics and Automation (2000) pp. 2677–2683.Google Scholar
Gäfvert, M., Lischinsky, P., Olsson, H., Åström, K. J. and Canudas de Wit, C., “Friction models and friction compensation,” Eur. J. Control 4(176), 176–195 (1998).Google Scholar
Pham, M. T., Gautier, M. and Poignet, P., “Identification of Joint Stiffness with Bandpass Filtering,” IEEE International Conference on Robotics and Automation (2001) pp. 2867–2872.Google Scholar
Vuong, N. D. and Ang, M. H., Jr., Dynamic model identification for industrial robots. Acta Polytechnica Hungarica 6(5), 51–68 (2009).Google Scholar
World Medical Association, “World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects,” Bull. World Health Organizat. 79(4), 373–374 (2001).Google Scholar
Hermens, H. J., Freriks, B., Disselhorst-Klug, C. and Rau, G., eds., European recommendations for surface ElectroMyoGraphy: Results of the SENIAM project, SENIAM, vol. 8 (Roessingh Research and Development, Enschede, 1999).Google Scholar
Liu, G., “On Velocity Estimation Using Position Measurements,” Proceedings of the American Control Conference (2002) pp. 1115–1120.Google Scholar
Potvin, J., and Brown, S., “Less is more: High pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates,” J. Electromyography Kinesiol. 14(3), 389–399 (2004).CrossRefGoogle ScholarPubMed
Cavanagh, P. R. and Komi, P. V., “Electromechanical delay in human skeletal muscle under concentric and eccentric contractions,” Eur. J. Appl. Physiol. Occupat. Physiol. 42(3), 159–163 (1979).CrossRefGoogle ScholarPubMed
Berret, B. and Jean, F., “Why don’t we move slower? The value of time in the neural control of action,” J. Neurosci. 36(4), 1056–1070 (2016).CrossRefGoogle ScholarPubMed
Viviani, P. and Terzuolo, C., “Trajectory determines movement dynamics,” Neuroscience 7(2), 431–437 (1982).CrossRefGoogle ScholarPubMed
Cooke, J. and Brown, S., “Movement-related phasic muscle activation: III. The duration of phasic agonist activity initiating movement,” Exp. Brain Res. 99(3), 473–482 (1994).CrossRefGoogle ScholarPubMed
Cooke, J. D. and Brown, S. H., “Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern,” J. Neurophysiol. 63(3), 465–472 (1990).CrossRefGoogle ScholarPubMed
Ingram, J. N., Howard, I. S., Flanagan, J. R. and Wolpert, D. M., “A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics,” PLoS Comput. Biol. 7(9) (2011).CrossRefGoogle ScholarPubMed
Shadmehr, R. and Mussa-lvaldi, F. A., “Adaptive representation of dynamics during learning of a motor task,” J. Neurosci. 14(5), 3208–3224 (1994).CrossRefGoogle ScholarPubMed
Gaveau, J., Grospretre, S., Angelaki, D. and Papaxanthis, C., “A cross-species neural integration of gravity for motor optimisation,” bioRxiv (2019), 728857.CrossRefGoogle Scholar
Pohlert, T., The Pairwise Multiple Comparison of Mean Ranks Package (PMCMR). R-Package (2016) pp. 14–19.Google Scholar
Berret, B., Castanier, C., Bastide, S. and Deroche, T., “Vigour of self-paced reaching movement: Cost of time and individual traits,” Sci. Rep. 8(1), 10655–10669, (2018).CrossRefGoogle ScholarPubMed