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Multi-DoFs Exoskeleton-Based Bilateral Teleoperation with the Time-Domain Passivity Approach

Published online by Cambridge University Press:  01 March 2019

Domenico Buongiorno Open the ORCID record for Domenico Buongiorno [Opens in a new window] ,
Domenico Chiaradia ,
Simone Marcheschi ,
Massimiliano Solazzi  and
Antonio Frisoli
Domenico Buongiorno*
Affiliation:
PERCRO Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy. E-mails: [email protected], [email protected], [email protected], [email protected]
Domenico Chiaradia
Affiliation:
PERCRO Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy. E-mails: [email protected], [email protected], [email protected], [email protected]
Simone Marcheschi
Affiliation:
PERCRO Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy. E-mails: [email protected], [email protected], [email protected], [email protected]
Massimiliano Solazzi
Affiliation:
PERCRO Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy. E-mails: [email protected], [email protected], [email protected], [email protected]
Antonio Frisoli
Affiliation:
PERCRO Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy. E-mails: [email protected], [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

It is well known that the sense of presence in a tele-robot system for both home-based tele-rehabilitation and rescue operations is enhanced by haptic feedback. Beyond several advantages, in the presence of communication delay haptic feedback can lead to an unstable teleoperation system. During the last decades, several control techniques have been proposed to ensure a good trade-off between transparency and stability in bilateral teleoperation systems under time delays. These proposed control approaches have been extensively tested with teleoperation systems based on identical master and slave robots having few degrees of freedom (DoF). However, a small number of DoFs cannot ensure both an effective restoration of the multi-joint coordination in tele-rehabilitation and an adequate dexterity during manipulation tasks in rescue scenario. Thus, a deep understanding of the applicability of such control techniques on a real bilateral teleoperation setup is needed. In this work, we investigated the behavior of the time-domain passivity approach (TDPA) applied on an asymmetrical teleoperator system composed by a 5-DoFs impedance designed upper-limb exoskeleton and a 4-DoFs admittance designed anthropomorphic robot. The conceived teleoperation architecture is based on a velocity–force (measured) architecture with position drift compensation and has been tested with a representative set of tasks under communication delay (80 ms round-trip). The results have shown that the TDPA is suitable for a multi-DoFs asymmetrical setup composed by two isomorphic haptic interfaces characterized by different mechanical features. The stability of the teleoperator has been proved during several (1) high-force contacts against stiff wall that involve more Cartesian axes simultaneously, (2) continuous contacts with a stiff edge tests, (3) heavy-load handling tests while following a predefined path and (4) high-force contacts against stiff wall while handling a load. The found results demonstrated that the TDPA could be used in several teleoperation scenarios like home-based tele-rehabilitation and rescue operations.

Keywords

Multi-DoFs asymmetrical teleoperationTDPAExoskeletonPassivityTele-rehabilitationDisaster scenario
Type
Articles
Information
Robotica , Volume 37 , Issue 9 , September 2019 , pp. 1641 - 1662
Copyright
© Cambridge University Press 2019 

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References

Buongiorno, D., Barsotti, M., Sotgiu, E., Loconsole, C., Solazzi, M., Bevilacqua, V. and Frisoli, A. “A Neuromusculoskeletal Model of the Human Upper Limb for a Myoelectric Exoskeleton Control Using a Reduced Number of Muscles,” 2015 IEEE World Haptics Conference (WHC) (2015) pp. 273–279. doi:10.1109/WHC.2015.7177725.CrossRefGoogle Scholar
Buongiorno, D., Barone, F., Solazzi, M., Bevilacqua, V. and Frisoli, A., “A Linear Optimization Procedure for an EMG-Driven Neuromusculoskeletal Model Parameters Adjusting: Validation Through a Myoelectric Exoskeleton Control,” In: Haptics: Perception, Devices, Control, and Applications (Bello, F., Kajimoto, H. and Visell, Y., eds.) (Springer International Publishing, Cham, 2016) pp. 218–227. doi:10.1007/978-3-319-42324-1_22.CrossRefGoogle Scholar
Buongiorno, D., Barone, F., Berger, D. J., Cesqui, B., Bevilacqua, V., d’Avella, A. and Frisoli, A., “Evaluation of a Pose-Shared Synergy-Based Isometric Model for Hand Force Estimation: Towards Myocontrol,” In: Converging Clinical and Engineering Research on Neurorehabilitation II (Ibáñez, J., González-Vargas, J., Azorín, J. M., Akay, M. and Pons, J. L., eds.) (Springer International Publishing, Cham, 2017) pp. 953–958. doi:10.1007/978-3-319-46669-9_154.CrossRefGoogle Scholar
Stroppa, F., Stroppa, M. S., Marcheschi, S., Loconsole, C., Sotgiu, E., Solazzi, M., Buongiorno, D. and Frisoli, A., “Real-Time 3D Tracker in Robot-Based Neurorehabilitation,” In: Computer Vision for Assistive Healthcare. Computer Vision and Pattern Recognition (Leo, M. and Farinella, G. M., eds.), Chapter 3 (Academic Press, 2018) pp. 75–104. doi:10.1016/B978-0-12-813445-0.00003-4.CrossRefGoogle Scholar
Buongiorno, D., Barsotti, M., Barone, F., Bevilacqua, V. and Frisoli, A., “A linear approach to optimize an EMG-driven neuromusculoskeletal model for movement intention detection in myo-control: A case study on shoulder and elbow joints,” Front. Neurorob. 12, 74 (2018). doi:10.3389/fnbot.2018.00074.CrossRefGoogle ScholarPubMed
Chiaradia, D., Xiloyannis, M., Antuvan, C. W., Frisoli, A. and Masia, L., “Design and Embedded Control of a Soft Elbow Exosuit,” Proceedings of IEEE International Conference on Soft Robotics (RoboSoft), Livorno, Italy (IEEE, 2018). doi:10.1109/ROBOSOFT.2018.8405386.CrossRefGoogle Scholar
Chiaradia, D., Xiloyannis, M., Solazzi, M., Masia, L. and Frisoli, A., “Comparison of a Soft Exosuit and a Rigid Exoskeleton in an Assistive Task,” In: Wearable Robotics: Challenges and Trends (Carrozza, M. C., Micera, S. and Pons, J. L., eds.) (Springer International Publishing, Cham, 2019) pp. 415–419. doi:10.1007/978-3-030-01887-0_80.CrossRefGoogle Scholar
Xiloyannis, M., Galli, L., Chiaradia, D., Frisoli, A., Braghin, F. and Masia, L., “A Soft Tendon-Driven Robotic Glove: Preliminary Evaluation,” In: Converging Clinical and Engineering Research on Neurorehabilitation III (Masia, L., Micera, S., Akay, M. and Pons, J. L., eds.) (Springer International Publishing, Cham, 2019) pp. 329–333. doi:10.1007/978-3-030-01845-0_66.CrossRefGoogle Scholar
Xiloyannis, M., Chiaradia, D., Frisoli, A. and Masia, L., “Characterisation of Pressure Distribution at the Interface of a Soft Exosuit: Towards a More Comfortable Wear,” In: Wearable Robotics: Challenges and Trends (Carrozza, M. C., Micera, S. and Pons, J. L., eds.) (Springer International Publishing, Cham, 2019) pp. 35–38. doi:10.1007/978-3-030-01887-0_7.CrossRefGoogle Scholar
Reinkensmeyer, D. J., Pang, C. T., Nessler, J. A. and Painter, C. C., “Web-based telerehabilitation for the upper extremity after stroke,” IEEE Trans. Neural Syst. Rehabil. Eng. 10(2), 102–108 (2002).CrossRefGoogle ScholarPubMed
Carignan, C. R. and Krebs, H. I., “Telerehabilitation robotics: Bright lights, big future?” J. Rehabil. Res. Dev. 43(5), 695 (2006).CrossRefGoogle ScholarPubMed
Katyal, K. D., Brown, C. Y., Hechtman, S. A., Para, M. P., McGee, T. G., Wolfe, K. C., Murphy, R. J., Kutzer, M. D., Tunstel, E. W., McLoughlin, M. P. and Johannes, M. S., “Approaches to Robotic Teleoperation in a Disaster Scenario: From Supervised Autonomy to Direct Control,” 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), Chicago, IL, USA (IEEE, 2014) pp. 1874–1881.CrossRefGoogle Scholar
Martins, H. and Ventura, R., “Immersive 3-D Teleoperation of a Search and Rescue Robot Using a Head-Mounted Display,” IEEE Conference on Emerging Technologies & Factory Automation, 2009. ETFA 2009, Mallorca, Spain (IEEE, 2009), pp. 1–8.CrossRefGoogle Scholar
Cisneros, R., Kajita, S., Sakaguchi, T., Nakaoka, S., Morisawa, M., Kaneko, K. and Kanehiro, F., “Task-Level Teleoperated Manipulation for the HRP-2Kai Humanoid Robot,” 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), Seoul, South Korea (IEEE, 2015) pp. 1102–1108.CrossRefGoogle Scholar
Lawrence, D., “Stability and transparency in bilateral teleoperation,” IEEE Trans. Rob. Autom. 9(5), 624–637 (1993).CrossRefGoogle Scholar
Chan, L., Naghdy, F. and Stirling, D., “Application of adaptive controllers in teleoperation systems: A survey,” IEEE Trans. Hum.-Mach. Syst. 44(3), 337–352 (2014).Google Scholar
Hokayem, P. F. and Spong, M. W., “Bilateral teleoperation: An historical survey,” Automatica 42(12), 2035–2057 (2006).CrossRefGoogle Scholar
Anderson, R. and Spong, M., “Asymptotic stability for force reflecting teleoperators with time delays,” 1989 IEEE International Conference on Robotics and Automation, 1989. Proceedings, Scottsdale, AZ, USA (IEEE, 1989) pp. 1618–1625.Google Scholar
Niemeyer, G. and Slotine, J. J., “Stable adaptive teleoperation,” IEEE J. Oceanic Eng. 16(1), 152–162 (1991).CrossRefGoogle Scholar
Lawrence, D. A., “Stability and transparency in bilateral teleoperation,” Proceedings of the 31st IEEE Conference on Decision and Control, 1992, Tucson, AZ, USA (IEEE, 1992) pp. 2649–2655.Google Scholar
Buttolo, P., Braathen, P. and Hannaford, B., “Sliding control of force reflecting teleoperation: Preliminary studies,” Presence: Teleoperators Virtual Environ. 3(2), 158–172 (1994).Google Scholar
Leung, G. M., Francis, B. A. and Apkarian, J., “Bilateral controller for teleoperators with time delay via μ-synthesis,” IEEE Trans. Rob. Autom. 11(1), 105–116 (1995).CrossRefGoogle Scholar
Mitra, P. and Niemeyer, G., “Model-mediated telemanipulation,” Int. J. Rob. Res. 27(2), 253–262 (2008).CrossRefGoogle Scholar
Schwarz, M., Rodehutskors, T., Droeschel, D., Beul, M., Schreiber, M., Araslanov, N., Ivanov, I., Lenz, C., Razlaw, J., Schüller, S., Schwarz, D., Topalidou-Kyniazopoulou, A. and Behnke, S., “Nimbro rescue: Solving disaster-response tasks with the mobile manipulation robot momaro,” J. Field Rob. 34(2), 400–425 (2017).CrossRefGoogle Scholar
Ajoudani, A., Lee, J., Rocchi, A., Ferrati, M., Hoffman, E. M., Settimi, A., Caldwell, D. G., Bicchi, A. and Tsagarakis, N. G., “A Manipulation Framework for Compliant Humanoid Coman: Application to a Valve Turning Task,” 2014 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Madrid, Spain (IEEE, 2014) pp. 664–670.CrossRefGoogle Scholar
Kim, Y. S., Lee, J., Lee, S. and Kim, M., “A force reflected exoskeleton-type masterarm for human-robot interaction,” IEEE Tran. Syst. Man Cybern. Part A: Syst. Hum. 35(2), 198–212 (2005).CrossRefGoogle Scholar
Pirondini, E., Coscia, M., Marcheschi, S., Roas, G., Salsedo, F., Frisoli, A., Bergamasco, M. and Micera, S., “Evaluation of a New Exoskeleton for Upper Limb Post-stroke Neuro-rehabilitation: Preliminary Results,” In: Replace, Repair, Restore, Relieve–Bridging Clinical and Engineering Solutions in Neurorehabilitation (Springer, Aalborg, 2014) pp. 637–645.Google Scholar
Atashzar, S. F., Shahbazi, M., Tavakoli, M. and Patel, R. V., “A passivity-based approach for stable patient–robot interaction in haptics-enabled rehabilitation systems: Modulated time-domain passivity control,” IEEE Trans. Control Syst. Technol. 25(3), 991–1006 (2017).CrossRefGoogle Scholar
Artigas, J., Balachandran, R., Riecke, C., Stelzer, M., Weber, B., Ryu, J. H. and Albu-Schaeffer, A., “KONTUR-2: Force-Feedback Teleoperation from the International Space Station,” 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden (IEEE, 2016) pp. 1166–1173.CrossRefGoogle Scholar
Chawda, V. and O’Malley, M. K., “Position synchronization in bilateral teleoperation under time-varying communication delays,” IEEE/ASME Trans. Mechatron. 20(1), 245–253 (2015).CrossRefGoogle Scholar
Han, B., Ryu, J. H. and Jung, I. K., “FPGA Based Time Domain Passivity Observer and Passivity Controller,” IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2009. AIM 2009, Singapore (IEEE, 2009) pp. 433–438.CrossRefGoogle Scholar
Chawda, V., Van Quang, H., O’Malley, M. K. and Ryu, J. H., “Compensating Position Drift in Time Domain Passivity Approach Based Teleoperation,” 2014 IEEE Haptics Symposium (HAPTICS), Houston, TX, USA (IEEE, 2014) pp. 195–202.Google Scholar
Artigas, J., Ryu, J. H. and Preusche, C., “Time domain passivity control for position-position teleoperation architectures,” Presence 19(5), 482–497 (2010).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(1), 9 (2016).CrossRefGoogle ScholarPubMed
Vertechy, R., Frisoli, A., Dettori, A., Solazzi, M. and Bergamasco, M., “Development of a New Exoskeleton for Upper Limb Rehabilitation,” IEEE International Conference on Rehabilitation Robotics, 2009. ICORR 2009, Kyoto, Japan (IEEE, 2009) pp. 188–193.CrossRefGoogle Scholar
Solazzi, M., Abbrescia, M., Vertechy, R., Loconsole, C., Bevilacqua, V. and Frisoli, A., “An Interaction Torque Control Improving Human Force Estimation of the Rehab-Exos Exoskeleton,” 2014 IEEE Haptics Symposium (HAPTICS), Houston, TX, USA (IEEE, 2014) pp. 187–193.CrossRefGoogle Scholar
Hannaford, B. and Ryu, J. H., “Time-domain passivity control of haptic interfaces,” IEEE Trans. Rob. Autom. 18(1), 1–10 (2002).CrossRefGoogle Scholar
Ryu, J. H., Kwon, D. S. and Hannaford, B., ‘Stable teleoperation with time-domain passivity control’, IEEE Trans. Rob. Autom. 20(2), 365–373 (2004).CrossRefGoogle Scholar
Panzirsch, M., Hulin, T., Artigas, J., Ott, C. and Ferre, M., “Integrating Measured Force Feedback in Passive Multilateral Teleoperation,” International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, London, UK (Springer, 2016) pp. 316–326.CrossRefGoogle Scholar
Artigas, J., Ryu, J. H., Preusche, C. and Hirzinger, G., “Network Representation and Passivity of Delayed Teleoperation Systems,” 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA (IEEE, 2011) pp. 177–183.CrossRefGoogle Scholar
Kim, Y. S. and Hannaford, B., “Some Practical Issues in Time Domain Passivity Control of Haptic Interfaces,” 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001. Proceedings, Maui, HI, USA, vol. 3 (IEEE, 2001) pp. 1744–1750.Google Scholar
Ryu, J. H., “Bilateral Control with Time Domain Passivity Approach Under Time-Varying Communication Delay,” The 16th IEEE International Symposium on Robot and Human Interactive Communication, 2007. RO-MAN 2007, Jeju, South Korea (IEEE, 2007) pp. 986–991.Google Scholar
Sciavicco, L. and Siciliano, B., Modelling and Control of Robot Manipulators (Springer Science & Business Media, London, UK, 2012)Google Scholar
Buongiorno, D., Sotgiu, E., Leonardis, D., Marcheschi, S., Solazzi, M. and Frisoli, A., “WRES: A novel 3 DoF wrist exoskeleton with tendon-driven differential transmission for neuro-rehabilitation and teleoperation,” IEEE Rob. Autom. Lett. 3(3), 2152–2159 (2018). doi:10.1109/LRA.2018.2810943.CrossRefGoogle Scholar