Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-25T05:07:43.619Z Has data issue: false hasContentIssue false
  • English
  • Français

Virtual fixtures with autonomous error compensation for human–robot cooperative tasks

Published online by Cambridge University Press:  02 September 2009

Raúl A. Castillo-Cruces  and
Jürgen Wahrburg
Raúl A. Castillo-Cruces*
Affiliation:
Center for Sensor Systems (ZESS), University of Siegen, Germany
Jürgen Wahrburg
Affiliation:
Center for Sensor Systems (ZESS), University of Siegen, Germany
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a control strategy for surgical interventions, applied on a human–robot cooperative system, which facilitates the sharing of responsibilities between surgeon and robot. The controller utilizes virtual fixtures to constrain the movements of the end-effector into a predefined path or region. Possible deviation error can be compensated in two different ways: (a) manual compensation and (b) autonomous compensation. With manual compensation, the system defines both virtual fixtures and error compensation directions, but the surgeon must apply manual forces himself/herself in order to generate end-effector motion. With autonomous compensation, a clear distribution of responsibilities between surgeon and robotic system is present, meaning the surgeon has complete control of the end-effector along the preferred directions, while the robot autonomously compensates for any deviation along the non-preferred directions.

Type
Article
Information
Robotica , Volume 28 , Special Issue 2: Surgical Robotics: System Development, Application Study and Performance Analysis , March 2010 , pp. 267 - 277
Copyright
Copyright © Cambridge University Press 2009

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

1. Abbott, J. J., Marayong, P. and Okamura, A. M., “Haptic Virtual Fixtures for Robot-Assisted Manipulator, in Robotics Research,” In: Proceedings of the 12th International Symposium of Robotics Research, 2005 (Thrun, S., Durrant-Whyte, H. and Brooks, R., eds.), Springer Tracts in Advanced Robotics, vol. 28 (Springer, 2007) pp. 49–64.Google Scholar
2. Bettini, A., Lang, S., Okamura, A. and Hager, G., “Vision Assisted Control for Manipulation Using Virtual Fixtures: Experiments at Macro and Micro Scales,” Proceedings of the IEEE International Conference on Robotics and Automation, Washington, DC (11–15 May 2002) pp. 3354–3361.Google Scholar
3. Bettini, A., Marayong, P., Lang, S., Okamura, A. M. and Hager, G. D., “Vision assisted control for manipulation using virtual fixtures,” IEEE Int. Trans. Rob. Automat. 20 (6), 953–966 (Dec. 2004).Google Scholar
4. Kapoor, A., Li, M. and Taylor, R. H., “Spatial Motion Constraints for Robot Assisted Suturing Using Virtual Fixtures,” IEEE International Conference on Robotics & Automation, Taiwan (Sept. 2003) pp. 1954–1959.Google Scholar
5. Davies, B. L., Harris, S. J. and Rodriguez y Baena, F., “Hands-On Robotic Surgery: Is This The Future?” Medical Imaging and Augmented Reality, Proceedings of the 2nd International Workshop, MIAR 2004, Beijing, China (Aug. 2004) pp. 27–37.Google Scholar
6. Hager, G. D., “Human–Machine Cooperative Manipulation with Vision-Based Motion Constraints,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Workshop on Visual Servoing, EPFL, Lausanne, Switzerland (30 Sept. to 4 Oct. 2002).Google Scholar
7. Wahrburg, J., Pieck, S., Gross, I., Knappe, P., Kuenzler, S. and Kerschbaumer, F., “A navigated mechatronic system with haptic features to assist in surgical interventions,” J. Comp. Aid. Surg. 8, 292–299 (2003).Google Scholar
8. Kumar, R., Berkelman, P., Gupta, P., Barnes, A., Jensen, P. S., Whitcomb, L. L. and Taylor, R. H., “Preliminary Experiments in Cooperative Human/Robot Force Control for Robot Assisted Microsurgical Manipulation,” Proceedings of the IEEE International Conference Robotics and Automation, vol. 1, San Francisco, CA, USA (24–28 April 2000) pp. 610–617.Google Scholar
9. Li, M. and Taylor, R. H., “Spatial Motion Constraints in Medical Robot Using Virtual Fixtures Generated by Anatomy,” Proceedings of IEEE International Conference on Robotics & Automation, New Orleans, LA (26 April to 1 May 2004 ICRA) pp. 1270–1275.Google Scholar
10. Li, M., Kapoor, A. and Taylor, R. H., “A Constrained Optimization Approach to Virtual Fixtures,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada (02–06 August 2005) pp. 1408–1413.Google Scholar
11. Peshkin, M. A. et al. , “COBOT architecture,” IEEE Trans. Rob. Automat. 17 (4), 3777 (Aug. 2001).CrossRefGoogle Scholar
12. Schneider, O. and Troccaz, J., “A six-degree-of-freedom passive arm with dynamic constraints (PADyC) for cardiac surgery application: Preliminary experiments,” Comp. Aid. Surg. 6, 340–351 (2001).Google Scholar
13. Marayong, P., Li, M., Okamura, A. M. and Hager, G. D., “Spatial Motion Constraints: Theory and Demonstrations for Robot Guidance Using Virtual Fixtures,” Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan (Sept. 2003) pp.14–19.Google Scholar
14. Starkie, S. and Davies, B. L., “Advances in Active Constraints and Their Application to Minimally Invasive Surgery,” Fourth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'01), Lecture Notes in Computer Science, vol. 2208, Utrecht (Oct. 2001) pp. 1316–1321.Google Scholar
15. Knappe, P., Pieck, S. and Wahrburg, J., “Komponenten und Architektur eines navigierten Assistenzroboters für chirurgische Anwendungen,” Automatisierungstechnik 53 (12), 615–626 (Dec. 2005).Google Scholar
16. Kazerooni, H., “Human/Robot Interaction Via the Transfer of Power and Information Signals Part I: Dynamics and Control Analysis,” IEEE Conference on Robotics and Automation, vol. 3, Scottsdale, Arizona (May 1989) pp. 908–909.Google Scholar
17. Kazerooni, H., “Human–robot interaction via the transfer of power and information signals,” IEEE Trans. Syst. Man Cybernet. 20 (2), 450–463 (Mar./Apr. 1990).Google Scholar
18. Kosuge, K., Yoshida, H. and Fukuda, T., “Dynamic Control for Robot–Human Collaboration,” Proceedings of the 2nd IEEE International Workshop on Robot and Human Communication, Tokyo, Japan (Nov. 03-05, 1993) pp. 398–399.Google Scholar
19. Kosuge, K., Fujisawa, Y. and Fukunda, T. “Control of Robot Directly Maneuvered by Operator,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Yokohama, Japan (26–30 Jul. 1993) pp. 49–54.Google Scholar
20. Kosuge, K. and Kayamura, N., “Control of a Robot Handling an Object in Cooperation with a Human,” Proceedings of the 6th IEEE International Workshop on Robot and Human Communication, Sendai, Japan (29 Sept. to 1 Oct. 1997) pp. 142–147.Google Scholar
21. Tsumugiwa, T., Yokogawa, R. and Hara, K., “Variable Impedance Control with Virtual Stiffness for Human–Robot Cooperative Task,” Proceedings of the 41st SICE Annual Conference, vol. 4, Osaka, Japan (5–7 August 2002) pp. 2329–2334.Google Scholar
22. Mukundan, R., “Quaternions: From Classical Mechanics to Computer Graphics, and Beyond,” Proceedings of the 7th Asian Technology conference in Mathematics, Melaka, Malaysia (18–21 December 2002) pp. 97–106.Google Scholar
23. Sayers, C., Remote Control Robotics (Springer, New York, 1999). ISBN 0387985972.Google Scholar
24. Mayorga, R. V. and Wong, A. C. K., “A Singularities Avoidance Approach for the Optimal Local Path Generation of Redundant Manipulators,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, Philadelphia, Pennsylvania (24–29 April 1988) pp. 49–54.Google Scholar
25. Prada, R. and Payandeh, S., “A Study on Design and Analysis of Virtual Fixtures for Cutting in a Training Environment,” Proceedings of World Haptics, Pisa, Italy (18–20 March 2005).Google Scholar