Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-23T02:24:29.907Z Has data issue: false hasContentIssue false
  • English
  • Français

Cooperative force control of a hybrid Cartesian parallel manipulator for bone slicing

Published online by Cambridge University Press:  30 April 2012

Ping-Lang Yen  and
Shuo-Suei Hung
Ping-Lang Yen*
Affiliation:
Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan
Shuo-Suei Hung
Affiliation:
Department of Orthopedic Surgery, Buddhist Taipei Tzu Chi General Hospital, New Taipei City 23142, Taiwan
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Over the past two decades, robots have been increasingly used in biomedical applications such as bone cutting. Traditional automated manufacturing processes are often unable to meet the safety and accuracy requirements for such applications, particularly for cutting inhomogeneous constitutions of bone. In this case, human–robot cooperation may prove to be an effective approach. In this paper, we demonstrate that a hybrid parallel manipulator under cooperative force control can achieve accurate bone cutting with sufficient safety guaranteed. First, a hybrid parallel manipulator was constructed to provide the required rigidity for bone cutting. Then a two-loop controller was designed to implement the human–robot cooperation in bone cutting. The position control loop of adaptive fuzzy control is responsible for achieving high-tracking performance by overcoming varying friction forces from the mechanism. The force control loop of the cooperative force control adjusts the feed rate of the cutter according to the bone slicing conditions and operator's supervisory commands. The experimental results show that the proposed controller can effectively achieve the required accuracy in bone cutting with required safety.

Keywords

Robot controlCooperative force controlBone cuttingSurgical robot
Type
Articles
Information
Robotica , Volume 31 , Issue 2 , March 2013 , pp. 173 - 182
Copyright
Copyright © Cambridge University Press 2012

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.Lepratti, R., “Advanced human–machine system for intelligent manufacturing: Some issues in employing ontologies for natural language processing,” J. Intell. Manuf. 17 (6), 653–666 (2006).CrossRefGoogle Scholar
2.Stopp, A., Horstmann, S., Kristensen, S. and Lohnert, F., “Towards interactive learning for manufacturing assistants,” IEEE Trans. Ind. Electron. 50 (4), 705–707 (2003).CrossRefGoogle Scholar
3.Akella, P., Peshkin, M., Colgate, E., Wannasuphoprasit, W., Nagesh, N., Wells, J., Holland, S., Pearson, T. and Peacock, B., “Cobots for the Automotive Assembly Line,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, MI, USA, (1999) pp. 728–733.Google Scholar
4.Hagele, M., Schaaf, W., Helms, E., “Robot Assistants at Manual Workplaces: Effective Co-operation and Safety Aspects,” Proceedings of the 33rd ISR (International Symposium on Robotics) (Oct. 7–11, 2002).Google Scholar
5.Lee, S. Y., Lee, K. Y., Lee, S. H., Kim, J. W. and Han, C. S., “Human-robot cooperation control for installing heavy construction materials,” Auton. Robot 22, 305–319 (2007).CrossRefGoogle Scholar
6.Castillo-Cruces, R. A. and Wahrburg, J., “Virtual fixtures with autonomous error compensation for human–robot cooperative tasks,” Robotica 28 (2), 267–277 (2010).CrossRefGoogle Scholar
7.Taylor, R., Jensen, P., Whitcomb, L., Barnes, A., Kumar, R., Stoianovici, D., Gupta, P., Wang, Z. X., Juan, E. and Kavoussi, L., “A steady-hand robotic system for microsurgical augmentation,” Int. J. Robot. Res. 18 (12), 1201–1210 (1999).CrossRefGoogle Scholar
8.Kragic, D., Marayong, P., Li, M., Okamura, A. M. and Hager, G. D., “Human–machine collaborative systems for microsurgical applications,” Int. J. Robot. Res. 24 (9), 731–741 (2005).CrossRefGoogle Scholar
9.Jakopec, M., Baena, F., Harris, S., Gomes, P., Cobb, J. and Davies, B. L., “The hands-on orthopaedic robot ‘acrobot’: Early clinical trials of total knee replacement surgery,” IEEE Trans. Robot. Autom. 19 (5), 902–911 (2003).CrossRefGoogle Scholar
10.Baena, F. R. and Davies, B., “Robotic surgery: From autonomous systems to intelligent tools,” Robotica 28 (2), 163–170 (2010).CrossRefGoogle Scholar
11.Blankenstein, A., “Dynamic Registration and High-Speed Visual Servoing in Robot-Assisted Surgery” Ph.D. Thesis (Leuven, Belgium: Katholieke Universiteit, 2008).Google Scholar
12.Liu, K., John, M., Fitzgerald, J. M., Frank, L. and Lewis, F. L. “Kinematic analysis of a stewart platform manipulator,” IEEE Trans. Ind. Electron. 40 (2), 632–637 (1993).CrossRefGoogle Scholar
13.Honegger, M., Codourey, A. and Burdet, E. “Adaptive control of the hexaglide, a 6-DOF parallel manipulator,” IEEE Int. Conf. Robot. Autom. 1, 543–548 (Apr. 20–25, 1997).Google Scholar
14.Kim, H. S. and Tasi, L. W., “Design optimization of a Cartesian parallel maniplator,” J. Mech. Des. 125, 43–51 (2003).CrossRefGoogle Scholar
15.Li, Y. and Xu, Q., “Kinematic analysis and design of a new 3-DOF translational parallel manipulator” (Transactions of the ASME series), J. Mech. Des. 128, 729–738 (July 2006).CrossRefGoogle Scholar
16.Gregorio, R. D. and Zanforlin, R. “Workspace analytic determination of two similar translational parallel manipulators,” Robotica 21, 555–566 (2003).CrossRefGoogle Scholar
17.Carricato, M. and Parenti-Castelli, V. “Singularity-free fully isotropic translational parallel mechanisms,” Int. J. Robot. Res. 21 (2), 161–174 (2002).CrossRefGoogle Scholar
18.Fattah, A. and Ghasemi, A. M. “Isotropic design of spatial parallel manipulators,” Int. J. Robot. Res. 21 (9), 811–824 (2002).CrossRefGoogle Scholar
19.Qu, H., Fang, Y. and Guo, S., “A new method for isotropic analysis of limited DOF parallel manipulators with terminal constraints,” Robotica 29 (4), 563–569 (2011).CrossRefGoogle Scholar
20.Bruzzone, L. E., Molfino, R. M. and Zoppi, M., “An impedance-controlled parallel robot for high-speed assembly of white goods,” Ind. Robot: Int. J. 32 (3), 226–233 (2005).CrossRefGoogle Scholar
21.Lopes, A. M. and Almeida, F. G., “Acceleration-based force-impedance control of a six-DOF parallel manipulator,” Ind. Robot: Int. J. 34 (5), 386–399 (2007).CrossRefGoogle Scholar
22.Yen, P.-L. “A two-loop robust controller for compensation of the variant friction force in an over-constrained parallel kinematic machine,” Int. J. Mach. Tools Manuf. 48 (12–13), 1354–1365 (2008).CrossRefGoogle Scholar
23.Siebert, W., Mai, S., Kober, R. and Heeckt, P. F., “Technique and first clinical results of robot-assisted total knee replacement,” The Knee 9, 173–180 (2002).CrossRefGoogle ScholarPubMed
24.Malvisi, A., Vendruscolo, P., Morici, F., Martelli, S. and Marcacci, M., “Milling versus Sawing: Comparison of Temperature Elevation and Clinical Performance During Bone Cutting,” In: Medical Image Computing and Computer-Assisted Intervention, Delp, S. L., DiGioia, A. M., and Jaramaz, B. (Eds) (Springer, Berlin, Germany, 2000) pp. 1238–1244.Google Scholar
25.Lee, S. Y., Lee, K. Y., Lee, S. H., Kim, J. W. and Han, C. S., “Human–robot cooperation control for installing heavy construction materials,” Auton. Robot 22, 305–319 (2007).CrossRefGoogle Scholar
26.Damdinsuren, E., Kosuge, K. and Wang, Z. D., “Motion Control of Soil Removing Operation for Teleoperation based Demining System,” In: IEEE International Workshop on Safety, Security and Rescue Robotics (Jun. 6–9, 2005) pp. 13–18.CrossRefGoogle Scholar
27.Sugita, N., Osa, T. and Mitsuishi, M., “Analysis and estimation of cutting-temperature distribution during end milling in relation to orthopedic surgery,” J. Med. Eng. Phys. 31 (1), 101–107 (2009).CrossRefGoogle ScholarPubMed
28.Siciliano, L. V., Robot Force Control (Kluwer, Boston, MA, 1999) 540 pp.CrossRefGoogle Scholar
29.Chiaverini, S., Siciliano, B. and Villani, L., “A survey of robot interaction control schemes with experimental comparison,” IEEE/ASME Trans. Mechatronics 4, 273–285 (1999).CrossRefGoogle Scholar
30.Moore, K. L. and Dalley, A. F., Clinically Oriented Anatomy, 5th ed. (Lippincott Williams & Wilkins, Baltimore, MD, 2006).Google Scholar
31.Krause, W. R., “Orthogonal bone cutting: Saw design and operating characteristics,” J Biomech. Eng. 109 (3), 263–271 (1987).CrossRefGoogle ScholarPubMed
32.Hogan, N., “Impedance control: An approach to manipulation,” ASME J. Dyn. Syst. Meas. Control 107, 1–24 (1985).CrossRefGoogle Scholar
33.Roy, J. and Whitcomb, L., “Adaptive force control of position/velocity controlled robots: Theory and experiment,” IEEE Trans. Robot. Autom. 18 (2), 121–137 (2002).CrossRefGoogle Scholar
34.Wang, L. X., Adaptive Fuzzy Systems and Control: Design and Stability Analysis (Prentice-Hall, Englewood Cliffs, NJ, 1994).Google Scholar
35.Duchemin, G., Poignet, Ph., Dombre, E. and Pierrot, F., “Medically safe and sound: The challenge of designing and manufacturing actuated medical robots for safe human interaction,” IEEE Robot. Autom. Mag. (special issue on Dependability) 11 (2), 46–55 (2004).CrossRefGoogle Scholar
36.Mitsubishi, M., Warisawa, S. and Sugita, N., “Determination of the machining characteristics of a biomaterial using a machine tool designed for total knee arthroplasty,” Annals CIRP 53 (1), 107–112 (2004).CrossRefGoogle Scholar
37.Sugitaa, N., Nakanoa, T., Nakajimaa, Y., Fujiwarab, K., Abeb, N. O., Ozakib, T., Suzukic, M. and Mitsuishia, M., “Dynamic controlled milling process for bone machining,” J. Mater. Process. Technol. 209, 5777–5784 (2009).CrossRefGoogle Scholar
38.Yen, P.-L. and Davies, B. L., “Active constraint control for image-guided robotic surgery,” Proc. Inst. Mech. Eng. H: J. Eng. Med. 224 (5), 623–631 (2010).CrossRefGoogle ScholarPubMed