Intelligent Control for Human-Robot Cooperation in Orthopedics Surgery

  • Shaolong KuangEmail author
  • Yucun Tang
  • Andi Lin
  • Shumei Yu
  • Lining SunEmail author
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1093)


Cooperation between surgeon and robot is one of the key technologies that limit the robot to be widely used in orthopedic clinics. In this study, the evolution of human-robot cooperation methods and the control strategies for typical human-robot cooperation in robot-assisted orthopedics surgery were reviewed at first. Then an intelligent admittance control method, which combines the fuzzy model reference learning control with the virtual constraint control, is proposed to solve the requirements of intuitive human-robot interaction during orthopedics surgery. That is, a variable damping parameter model of the admittance control based on fuzzy model learning control algorithm is introduced to make the robot move freely by using the reference model of surgeon’s motion equation with the minimum jerk trajectory. And the virtual constraint control method based on the principle of virtual fixture is adopted to make the robot move within the pre-defined area so as to perform more safe surgery. The basic principle and its realization of this intelligent control method are described in details. At last, a test platform is built based on our designed 6 DOF articulated robot. Experiments of safety and precision on acrylic model with this method show that the robot has the ability of better intuitive interaction and the high precision. And the pilot experiment of bone tumor resection on sawbone model shows the effectiveness of this method.


Human-robot intuitive interaction Admittance control Variable damping control Fuzzy model reference learning Virtual constraint 



This work has been supported by many individuals and organizations. In particular, we would like to thank Dr. HU Yan from Jinan University and Dr. GAN Minfeng and Dr. ZHOU Xiaofei from First Affiliated Hospital of Soochow University for their valuable suggestions during our research. This project was supported by the National Natural Science Foundation of China (No. 61375090, U1613224) and National High-tech R&D Program (863 Program) (No. 2015AA043204).


  1. 1.
    Baena F, Davies B (2010) Robotic surgery: from autonomous systems to intelligent tools. Robotica 28(2):163–170CrossRefGoogle Scholar
  2. 2.
    Banks SA (2009) Haptic robotics enable a systems approach to design of a minimally invasive modular knee arthroplasty. Am J Orthop 38(2):23–27PubMedPubMedCentralGoogle Scholar
  3. 3.
    Cinquin P (2011) How today’s robots work and perspectives for the future. J Visc Surg 148(5):12–18CrossRefGoogle Scholar
  4. 4.
    Cruces RA, Wahrburg J (2007) Improving robot arm control for safe and robust haptic cooperation in orthopaedic procedures. Int J Med Robot Comp Assist Surg 3(4):316–322CrossRefGoogle Scholar
  5. 5.
    Dimeas F, Aspragathos N (2014) Fuzzy learning variable admittance control for human-robot cooperation. Paper presented at the international conference on the intelligent robots and systems (IROS), 4770–4775 September 2014Google Scholar
  6. 6.
    Duchaine V, Gosselin CM (2007) General model of human-robot cooperation using a novel velocity based variable impedance control. Paper presented at the euro haptics symposium on haptic interfaces for virtual environment and Teleoperator systems, 446–451 March 2007Google Scholar
  7. 7.
    Haidegger T, Benyó B, Kovács L et al (2009) Force sensing and force control for surgical robots. IFAC Proc 42(12):401–406CrossRefGoogle Scholar
  8. 8.
    Hancock PA, Billings DR, Schaefer KE et al (2011) A meta-analysis of factors affecting trust in human-robot interaction. Hum Factors 53(5):517–527CrossRefPubMedCentralGoogle Scholar
  9. 9.
    Ho SC, Hibberd RD, Davies BL (1995) Robot assisted knee surgery. IEEE Eng Med Biol Mag 14(3):292–300CrossRefGoogle Scholar
  10. 10.
    Ikeura R, Inooka H (1995). Variable impedance control of a robot for cooperation with a human. Paper presented at the international conference on the Robotics and Automation (ICRA), 3097–3102 May 1995Google Scholar
  11. 11.
    Jakopec M, Baena FR, Harris SJ et al (2003) The hands-on Orthopaedic robot “Acrobot”: early clinical trials of Total knee replacement surgery. Trans Robot Autom 19(5):902–911CrossRefGoogle Scholar
  12. 12.
    Kapoor A, Li M, Taylor RH (2006) Constrained control for surgical assistant robots. Paper presented at the international conference on the Robotics and Automation (ICRA), 231-236 May 2006Google Scholar
  13. 13.
    Kazanzides P, Zuhars J, Mittelstadt B, et al (1992) Force sensing and control for a surgical robot. Paper presented at the international c on the robotics and automation, 612–617 May 1992Google Scholar
  14. 14.
    Khan F, Pearle A, Lightcap C et al (2013) Haptic robot-assisted surgery improves accuracy of wide resection of bone tumors: a pilot study. Clin Orthop Relat Res 471(3):851–859CrossRefPubMedCentralGoogle Scholar
  15. 15.
    Kwon DS, Yoon YS, Lee JJ et al (2001) ARTHROBOT: a new surgical robot system for total hip arthroplasty. Intell Robot Syst 2:1123–1128Google Scholar
  16. 16.
    Langlotz F, Nolte LP (2004) Technical approaches to computer-assisted orthopedic surgery. Eur J Trauma 30(1):1–11CrossRefGoogle Scholar
  17. 17.
    Layne JR, Passino KM (1993) Fuzzy model reference learning control for cargo ship steering. IEEE Control Syst 13(6):23–34CrossRefGoogle Scholar
  18. 18.
    Layne JR, Passino KM (1996) Fuzzy model reference learning control. J Intell Fuzzy Syst 4(1):33–47Google Scholar
  19. 19.
    Lecours A, Mayer-St-Onge B, Gosselin C (2012) Variable admittance control of a four-degree-of-freedom intelligent assist device. Paper presented at the international conference on the Robotics and Automation (ICRA), 3903-3908 May 2012Google Scholar
  20. 20.
    Leung KS, Ning TANG, Cheung LW et al (2008) Robotic arm in orthopaedic trauma surgery–early clinical experience and a review. Spinal Surg 1(2): 3–4Google Scholar
  21. 21.
    Maillet P, Nahum B, Blondel L, et al (2005) BRIGIT, a robotized tool guide for orthopedic surgery. Paper presented at the international conference on the Robotics and Automation, 211–216 April 2005Google Scholar
  22. 22.
    Marayong P, Li M, Okamura AM., Hager GD (2003) Spatial motion constraints: theory and demonstrations for robot guidance using virtual fixtures. Paper presented at the 3rd international conference on the Robotics and Automation, 1954–1959 September 2003Google Scholar
  23. 23.
    Miller L (2011) Robotics in orthopedic surgery: 6 points on the present and future. In: Becker’s orthopedic, Spine & Pain Management Review. spine-device-mplant-news/item/4201-robotics-in-orthopedic-surgery-6-points-on-the-present-and-future. Accessed 1 Jan 2013
  24. 24.
    Newman WS (1992) Stability and performance limits of interaction controllers. J Dyn Syst Meas Control 114(4):563–570CrossRefGoogle Scholar
  25. 25.
    Ott C, Mukherjee R, Nakamura Y (2010) Unified impedance and admittance control. Paper presented at the international conference on the robotics and automation (ICRA), 554–561 May 2010Google Scholar
  26. 26.
    Quaid AE, Kang H, Moses D et al (2014) Haptic guidance method. United State Patent No. 8911499B2Google Scholar
  27. 27.
    Rahman MM, Ikeura R., Mizutani K (1999) Investigating the impedance characteristic of human arm for development of robots to co-operate with human operators. Paper presented at the 99th international conference on the Systems, Man, and Cybernetics, 676–681 1999Google Scholar
  28. 28.
    Robotics VO (2013) A roadmap for US robotics: from internet to robotics. Robotics Virtual OrganizationGoogle Scholar
  29. 29.
    Sugano N (2003) Computer-assisted orthopedic surgery. J Orthop Sci 8(3):442–448CrossRefPubMedCentralGoogle Scholar
  30. 30.
    Taylor RH (2006) A perspective on medical robotics. Proc IEEE 94(9):1652–1664CrossRefGoogle Scholar
  31. 31.
    Taylor RH, Mittelstadt BD, Paul HA et al (1994) An image-directed robotic system for precise orthopaedic surgery. Trans Robot Autom 10(3): 261–275CrossRefGoogle Scholar
  32. 32.
    Troccaz J, Delnondedieu Y (1996) Semi-active guiding systems in surgery. A two-dof prototype of the passive arm with dynamic constraints (PADyC). Mechatronics 6(4):399–421CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Robotics and Micro-Systems CenterSoochow UniversitySuzhou CityPeople’s Republic of China

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