Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-18T06:04:48.874Z Has data issue: false hasContentIssue false
  • English
  • Français

Using a Redundant User Interface in Teleoperated Surgical Systems for Task Performance Enhancement

Published online by Cambridge University Press:  20 May 2020

Ali Torabi Open the ORCID record for Ali Torabi [Opens in a new window] ,
Mohsen Khadem ,
Koroush Zareinia Open the ORCID record for Koroush Zareinia [Opens in a new window] ,
Garnette Roy Sutherland  and
Mahdi Tavakoli
Ali Torabi*
Affiliation:
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada, E-mails: ali.torabi@ualberta.ca, mahdi.tavakoli@ualberta.ca
Mohsen Khadem
Affiliation:
School of Informatics, University of Edinburgh, Edinburgh, UK, E-mail: mohsen.khadem@ed.ac.uk
Koroush Zareinia
Affiliation:
Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON, Canada, E-mail: kourosh.zareinia@ryerson.ca
Garnette Roy Sutherland
Affiliation:
Project neuroArm, Faculty of Medicine, University of Calgary, Calgary, AB, Canada, E-mail: garnette@ucalgary.ca
Mahdi Tavakoli
Affiliation:
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada, E-mails: ali.torabi@ualberta.ca, mahdi.tavakoli@ualberta.ca
*
*Corresponding author. E-mail: ali.torabi@ualberta.ca
Get access Rights & Permissions [Opens in a new window]

Summary

The enhanced dexterity and manipulability offered by master–slave teleoperated surgical systems have significantly improved the performance and safety of minimally invasive surgeries. However, effective manipulation of surgical robots is sometimes limited due to the mismatch between the slave and master robots’ kinematics and workspace. The purpose of this paper is first to formulate a quantifiable measure of the combined master–slave system manipulability. Next, we develop a null-space controller for the redundant master robot that employs the proposed manipulability index to enhance the performance of teleoperation tasks by matching the kinematics of the redundant master robot with the kinematics of the slave robot. The null-space controller modulates the redundant degrees of freedom of the master robot to reshape its manipulability ellipsoid (ME) towards the ME of the slave robot. The ME is the geometric interpretation of the kinematics of a robot. By reshaping the master robot’s manipulability, we match the master and slave robots’ kinematics. We demonstrate that by using a redundant master robot, we are able to enhance the master–slave system manipulability and more intuitively transfer the slave robot’s dexterity to the user. Simulation and experimental studies are performed to validate the performance of the proposed control strategy. Results demonstrate that by employing the proposed manipulability index, we can enhance the user’s control over the force/velocity of a surgical robot and minimize the user’s control effort for a teleoperated task.

Keywords

TeleoperationHaptic InterfacesMedical Robots and SystemsRedundant ManipulatorsNull-space Control
Type
Articles
Information
Robotica , Volume 38 , Special Issue 10: Human–Robot Interaction (HRI) , October 2020 , pp. 1880 - 1894
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Berkelman, P. and Ma, J., “A compact modular teleoperated robotic system for laparoscopic surgery,” Int. J. Robot. Res. 28(9), 1198–1215 (2009).CrossRefGoogle ScholarPubMed
Kolbari, H., Sadeghnejad, S., Bahrami, M. and Ali, K. E., “Adaptive control of a robot-assisted tele-surgery in interaction With hybrid tissues,” J. Dyn. Syst. Meas. Cont. 140(12), 121012 (2018).CrossRefGoogle Scholar
Hayward, V. and Astley, O. R., “Performance Measures for Haptic Interfaces,” In: Robotics Research (Giralt, G. and Hirzinger, G., eds.) (Springer London, London 1996) pp. 195–206.CrossRefGoogle Scholar
Licona, A. R., Liu, F., Pinzon, D., Torabi, A., Boulanger, P., Lelevé, A., Moreau, R., Pham, M. T. and Tavakoli, M., Applications of Haptics in Medicine (Springer International Publishing, Cham, 2020) pp. 183–214.Google Scholar
Ellis, R., Ismaeil, O. and Lipsett, M., “Design and evaluation of a high-performance haptic interface,” Robotica. 14(03), 321 (1996).CrossRefGoogle Scholar
Siciliano, B., “Kinematic control of redundant robot manipulators: A tutorial,” J. Intell. Robot. Syst. 3(3), 201–212 (1990).Google Scholar
Liu, Y.-C. and Chopra, N., “Controlled synchronization of heterogeneous robotic manipulators in the task space,” IEEE Trans. Robot. 28(1), 268–275 (2012).CrossRefGoogle Scholar
Hwang, D.-Y. and Hannaford, B., “Teleoperation performance with a kinematically redundant slave robot,” Int. J. Robot. Res. 17(6), 579–597 (1998).CrossRefGoogle Scholar
Nath, N., Tatlicioglu, E. and Dawson, D. M., “Teleoperation with kinematically redundant robot manipulators with sub-task objectives,” Robotica. 27(7), 1027–1038 (2009).CrossRefGoogle Scholar
Das, H., Sheridan, T. B. and Slotine, J. J. E., “Kinematic Control and Visual Display of Redundant Teleoperators,” Conference Proceedings, IEEE International Conference on Systems, Man and Cybernetics, Cambridge, MA, USA, vol. 3 (Nov 1989) pp. 1072–1077.CrossRefGoogle Scholar
Liu, Y.-C., “Task-space bilateral teleoperation systems for heterogeneous robots with time-varying delays,” Robotica. 33(10), 2065–2082 (2015).CrossRefGoogle Scholar
Ficuciello, F., Villani, L. and Siciliano, B., “Variable impedance control of redundant manipulators for intuitive human – robot physical interaction,” Trans. Robot. 31(4), 1–14 (2015).Google Scholar
Mihelj, M., “Human arm kinematics for robot based rehabilitation,” Robotica. 24(3), 377–383 (2006).CrossRefGoogle Scholar
Schaal, S. and Schweighofer, N., “Computational motor control in humans and robots,” Curr. Opin. Neurobiol. 15(6), 675–682 (2005), Motor systems/Neurobiology of behaviour.CrossRefGoogle ScholarPubMed
Nisky, I., Hsieh, M. H. and Okamura, A. M., “Uncontrolled manifold analysis of arm joint angle variability during robotic teleoperation and freehand movement of surgeons and novices,” IEEE Trans. Biomed. Eng. 61(12), 2869–2881 (2014).CrossRefGoogle ScholarPubMed
Ueberle, M., Mock, N. and Buss, M., “VISHARD10, A Novel Hyper-Redundant Haptic Interface,” Proceedings - 12th International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, HAPTICS, Chicago, IL, USA (2004) pp. 58–65.CrossRefGoogle Scholar
Salisbury, J. K. Jr., Madhani, A. J., Guthart, G. S., Niemeyer, G. D. and Duval, E. F., “Master having redundant degrees of freedom,” US Patent 6,684,129 (2004).Google Scholar
Yoshikawa, T., “Manipulability of robotic mechanisms,” Int. J. Robot. Res. 4(2), 3–9 (1985).CrossRefGoogle Scholar
Flores-Díaz, O., Juárez-Campos, I. and Carrera-Bolaños, J., “Procedures to determine the principal directions of kinematic performance in serial robots,” Robotica. 36(11), 1664–1679 (2018).CrossRefGoogle Scholar
Angeles, J. and Park, F. C., Performance Evaluation and Design Criteria (Springer Berlin Heidelberg, 2008) pp. 229–244.Google Scholar
Konietschke, R., Ortmaier, T., Weiss, H., Hirzinger, G. and Engelke, R., Manipulability and Accuracy Measures for a Medical Robot in Minimally Invasive Surgery (Springer Netherlands, 2004) pp. 191–198.Google Scholar
Li, Z., Milutinović, D. and Rosen, J., “Design of a multi-arm surgical robotic system for dexterous manipulation,” ASME J. Mech. Robot. 8(6), 061 017–061 027 (2016).CrossRefGoogle Scholar
Torabi, A., Khadem, M., Zareinia, K., Sutherland, G. R. and Tavakoli, M., “Manipulability of Teleoperated Surgical Robots with Application in Design of Master/Slave Manipulators,” 2018 International Symposium on Medical Robotics (ISMR), Atlanta, GA, USA (March 2018) pp. 1–6.Google Scholar
Maddahi, Y., Greene, M., Gan, L. S., Hirmer, T., L’Orsa, R., Lama, S., Sutherland, G. R. and Zareinia, K., “Performance Evaluation of a Surgical Telerobotic System Using Kinematic Indices of the Master Hand-Controller,” In:International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2014 (Auvray, M. and Duriez, C., eds.) (Springer Berlin Heidelberg, 2014) pp. 167–175.CrossRefGoogle Scholar
Yoshikawa, T., “Translational and Rotational Manipulability of Robotic Manipulators,” Proceedings of 1991 International Conference on Industrial Electronics, Control and Instrumentation, IECON 1991, Versailles, France, vol. 2 (Oct 1991) pp. 1170–1175.Google Scholar
Wampler, C. W., “Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods,” IEEE Trans. Syst. Man Cybern. 16(1), 93–101 (1986).CrossRefGoogle Scholar
Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics - Modelling, Planning and Control (Springer-Verlag London, 2009).Google Scholar
Cherian, A., Sra, S., Banerjee, A. and Papanikolopoulos, N., “Efficient Similarity Search for Covariance Matrices via the Jensen-Bregman LogDet Divergence,” 2011 International Conference on Computer Vision, Barcelona, Spain (Nov 2011) pp. 2399–2406.CrossRefGoogle Scholar
Khatib, O., “A unified approach for motion and force control of robot manipulators: The operational space formulation,” IEEE J. Robot. Autom. 3(1), 43–53 (1987).CrossRefGoogle Scholar
Colgate, J. E. and Hogan, N., “Robust control of dynamically interacting systems,” Int. J. Cont. 48(1), 65–88 (1988).CrossRefGoogle Scholar
Nakanishi, J., Cory, R., Mistry, M., Peters, J. and Schaal, S., “Operational space control: A theoretical and empirical comparison,” Int. J. Robot. Res. 27(6), 737–757 (2008).Google Scholar
Çavuşoğlu, M. C., Feygin, D. and Tendick, F., “A critical study of the mechanical and electrical properties of the phantom haptic interface and improvements for high performance control,” Presence. 11(6), 555–568 (2002).CrossRefGoogle Scholar
Dyck, M. D., “Measuring the Dynamic Impedance of the Human Arm,” M.Sc. Thesis (University of Alberta, 2013).CrossRefGoogle Scholar
Randell, R., Alvarado, N., Honey, S., Greenhalgh, J., Gardner, P., Gill, A., Jayne, D. C., Kotze, A., Pearman, A. and Dowding, D., “Impact of Robotic Surgery on Decision Making: Perspectives of Surgical Teams,” AMIA … Annual Symposium Proceedings. AMIA Symposium, San Francisco, CA, USA, vol. 2015 (2015) pp. 1057–1066.Google Scholar
Richard, C. and Cutkosky, M., “The Effects of Real and Computer Generated Friction on Human Performance in a Targeting Task,” Proceedings of the ASME Dynamic Systems and Control Division, Orlando, FL, USA, vol. 69(2) (2000) pp. 1101–1108.Google Scholar