[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A grip force model for the da Vinci end-effector to predict a compensation force

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

A torque transfer system (TTS) that measures grip forces is developed to resolve a potential drawback of the current da Vinci robot system whose grip forces vary according to the different postures of its EndoWrist. A preliminary model of EndoWrist Inner Mechanism Model (EIMM) is also developed and validated with real grip force measurements. EndoWrist’s grip forces, posture angles, and transferred torque are measured by using TTS. The mean measured grip forces of three different EndoWrist for 27 different postures were very diverse. The EndoWrist exerted different grip forces, with a minimum of 1.84-times more and a maximum of 3.37-times more in specific posture even if the surgeon exerted the same amount of force. Using the posture angles as input and the grip forces as output, the EIMM is constructed. Then, expected grip force values obtained from EIMM are compared with actual measurements of da Vinci EndoWrist to validate the proposed model. From these results, surgeons will be beneficial with the understandings of actual grip force being applied to tissue and mechanical properties of robotic system. The EIMM could provide a baseline in designing a force-feedback system for surgical robot. These are significantly important to prevent serious injury by maintaining a proper force to tissue.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

TTS:

Torque transfer system

EIMM:

EndoWrist Inner Mechanism Model

NI:

National instruments

PF:

Prograsp forceps

PDF:

PK dissecting forceps

LND:

Large needle driver

SD:

Standard deviation

CT:

Coupled terms

HOT:

High-order term

MGF:

Mean grip force

SEM:

Standard error of measurement

References

  1. AERA, APA, NCME (1985) Standard for educational and psychological testing. American Psychological Association, Washington

    Google Scholar 

  2. Boissy P, Bourbonnais D, Carlotti MM, Gravel D, Arsenault BA (1999) Maximal grip force in chronic stroke subjects and its relationship to global upper extremity function. Clin Rehabil 13:354–362

    Article  CAS  PubMed  Google Scholar 

  3. Dobbelsteen JJ, Lee RA, Noorden MV, Dankelman J (2012) Indirect measurement of pinch and pull forces at the shaft of laparoscopic graspers. Med Biol Eng Comput 50:215–221

    Article  PubMed Central  PubMed  Google Scholar 

  4. George D, Mallery P (2003) SPSS for Windows step by step: a simple guide and reference. Allyn and Bacon, Boston

    Google Scholar 

  5. Greenish S, Hayward V, Chial V, Okamura A, Steffen T (2002) Measurement, analysis, and display of haptic signals during surgical cutting. Presence: Teleoper Virtual Environ 11:626–651

    Article  Google Scholar 

  6. Hashizume M, Konishi K, Tsutsumi N, Yamaguchi S, Shimabukuro R (2002) A new era of robotic surgery assisted by a computer-enhanced surgical system. Surgery 131:330–333

    Article  Google Scholar 

  7. Inc. Intuitive Surgical (2013) The da Vinci surgery experience: over the past decade, more than 1.5 million surgeries have been performed worldwide using the da Vinci Surgical System. Intuitive Surgical. http://www.davincisurgery.com/assets/docs/da-vinci-surgeryfact-sheet-en-1005195.pdf?location=1&version=b. Accessed 19 Sept 2014

  8. Inc. maxon motor. Maxon DC motor: Maxon motor Inc. http://www.engr.ucsb.edu/~mdnip/me170c/datasheets/25mm_Motors.pdf. Accessed 19 Sept 2014

  9. Johnson PJ, Schmidt DE, Duvvuri U (2014) Output control of da Vinci surgical system’s surgical graspers. J Surg Res I86(186):56–62

    Article  Google Scholar 

  10. King CH, Culjat MO, Franco ML, Lewis CE, Dutson EP, Grundfest WS, Bisley JW (2009) Tactile feedback induces reduced grasping force in robot-assisted surgery. IEEE T Haptics 2:103–110

    Article  Google Scholar 

  11. Mosse CA, Mills TN, Bell GD, Swain CP (1998) Device for measuring the forces exerted on the shaft of an endoscope during colonoscopy. Med Biol Eng Comput 36:186–190

    Article  CAS  PubMed  Google Scholar 

  12. Mucksavage P, Kerbl DC, Pick DL, Lee JY, McDougall EM, Louie MK (2011) Differences in grip forces among various robotic instruments and da Vinci surgical platforms. J Endourol Mar 25:523–528

    Article  Google Scholar 

  13. Ortmaier T, Hirzinger G (2000) Cartesian control issues for minimally invasive robot surgery. Intell Robots Syst (IROS 2000). Proceedings 2000 IEEE/RSJ international conference on; 2000:565–572

  14. Park SY, Cho KS, Lee SW, Soh BH, Rha KH (2008) Intraoperative breakage of needle driver jaw during robotic-assisted laparoscopic radical prostatectomy. Urology 71:168-e5–168-e6

    Google Scholar 

  15. Ruegg M (2010) Application note medical technology: surgical robots for minimally invasive procedures. Maxon motor ag. http://www.maxonmotor.com.au/downloads/Servo_motor_and_gearhead_in_Surgical_Robotics.pdf. Accessed 19 Sept 2014

  16. Seibold U, Kubler B, Hirzinger G (2005) Prototype of instrument for minimally invasive surgery with 6-axis force sensing capability. Robotics and automation, 2005 ICRA 2005 proceedings of the 2005 IEEE international conference on 2005:18–22

  17. Sung GT, Gill IS (2001) Robotic laparoscopic surgery: a comparison of the da Vinci and Zeus systems. Urology 58:893–898

    Article  CAS  PubMed  Google Scholar 

  18. Tavakoli M, Patel RV, Moallem M (2005) Haptic interaction in robot-assisted endoscopic surgery: a sensorized end-effector. Int J Med Robot Comput Assist Surg 1:53–63

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the Seoul National University Foundation Research Expense (Grant Number: 800-20100525) and a National Research Foundation of Korea (NRF) Grant funded by the Korean Government (Grant Number: 2012-0001638).

Author information

Authors and Affiliations

  1. The Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University, Seoul, 110-744, Korea

    Chiwon Lee, Chiyul Yoon, Seungwoo Noh & Choonghee Lee

  2. Department of Urology, Seoul National University Hospital, Seoul, 110-744, Korea

    Yong Hyun Park & Hyeon Hoe Kim

  3. Department of Mechanical and Aerospace Engineering, Seoul National University College of Engineering, Seoul, 151-742, Korea

    Youdan Kim

  4. Department of Biomedical Engineering, Institute of Medical and Biological Engineering, Seoul National University, Seoul, 110-799, Korea

    Hee Chan Kim & Sungwan Kim

Authors
  1. Chiwon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Hyun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chiyul Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seungwoo Noh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Choonghee Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Youdan Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hee Chan Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hyeon Hoe Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Sungwan Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sungwan Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, C., Park, Y.H., Yoon, C. et al. A grip force model for the da Vinci end-effector to predict a compensation force. Med Biol Eng Comput 53, 253–261 (2015). https://doi.org/10.1007/s11517-014-1230-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-014-1230-2

Keywords