Blended shared control utilizing online identification

Regulating grasping forces of a surrogate surgical grasper
  • Trevor K. Stephens
  • Nathan J. Kong
  • Rodney L. Dockter
  • John J. O’Neill
  • Robert M. Sweet
  • Timothy M. Kowalewski
Original Article



Surgical robots are increasingly common, yet routine tasks such as tissue grasping remain potentially harmful with high occurrences of tissue crush injury due to the lack of force feedback from the grasper. This work aims to investigate whether a blended shared control framework which utilizes real-time identification of the object being grasped as part of the feedback may help address the prevalence of tissue crush injury in robotic surgeries.


This work tests the proposed shared control framework and tissue identification algorithm on a custom surrogate surgical robotic grasping setup. This scheme utilizes identification of the object being grasped as part of the feedback to regulate to a desired force. The blended shared control is arbitrated between human and an implicit force controller based on a computed confidence in the identification of the grasped object. The online identification is performed using least squares based on a nonlinear tissue model. Testing was performed on five silicone tissue surrogates. Twenty grasps were conducted, with half of the grasps performed under manual control and half of the grasps performed with the proposed blended shared control, to test the efficacy of the control scheme.


The identification method resulted in an average of 95% accuracy across all time samples of all tissue grasps using a full leave-grasp-out cross-validation. There was an average convergence time of \(8.1 \pm 6.3\) ms across all training grasps for all tissue surrogates. Additionally, there was a reduction in peak forces induced during grasping for all tissue surrogates when applying blended shared control online.


The blended shared control using online identification more successfully regulated grasping forces to the desired target force when compared with manual control. The preliminary work on this surrogate setup for surgical grasping merits further investigation on real surgical tools and with real human tissues.


Shared control Robotic surgery Tissue identification Tissue crush injury 



This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. 00039202. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Compliance with ethical standards

Conflict of interest

Robert Sweet is a consultant for Olympus-Advisory for endourologic applications. Robert Sweet is chief executive officer for Simagine Health-Distributing simulation training solutions.

Ethical standard

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This articles does not contain patient data.


  1. 1.
    De S, Rosen J, Dagan A, Hannaford B, Swanson P, Sinanan M (2007) Assessment of tissue damage due to mechanical stresses. Int J Robot Res 26(11–12):1159–1171CrossRefGoogle Scholar
  2. 2.
    Dockter R, O’Neill J, Stephens T, Kowalewski T (2016) Feasibility of tissue classification via da vinci endowrist surgical tool. In: Hamlyn symposium on medical robotics, pp 64–65Google Scholar
  3. 3.
    Dragan AD, Srinivasa SS (2013) A policy-blending formalism for shared control. Int J Robot Res 32(7):790–805CrossRefGoogle Scholar
  4. 4.
    Enes A, Book W (2010) Blended shared control of zermelo’s navigation problem. In: American control conference (ACC), 2010, IEEE, pp 4307–4312Google Scholar
  5. 5.
    Fung Y (1981) Biomechanics: mechanical properties of living tissues. Springer, New YorkCrossRefGoogle Scholar
  6. 6.
    Li Y, Hannaford B (2017) Gaussian process regression for sensorless grip force estimation of cable-driven elongated surgical instruments. IEEE Robot Autom Lett 2(3):1312–1319CrossRefPubMedGoogle Scholar
  7. 7.
    MacFarlane M, Rosen J, Hannaford B, Pellegrini C, Sinanan M (1999) Force-feedback grasper helps restore sense of touch in minimally invasive surgery. J Gastrointest Surg 3(3):278–285CrossRefPubMedGoogle Scholar
  8. 8.
    Marucci DD, Shakeshaft AJ, Cartmill JA, Cox MR, Adams SG, Martin CJ (2000) Grasper trauma during laparoscopic cholecystectomy. Aust N Z J Surg 70(8):578–581CrossRefPubMedGoogle Scholar
  9. 9.
    Mirheydar HS, Parsons JK (2013) Diffusion of robotics into clinical practice in the united states: process, patient safety, learning curves, and the public health. World J Urol 31(3):455–461CrossRefPubMedGoogle Scholar
  10. 10.
    Okamura AM (2004) Methods for haptic feedback in teleoperated robot-assisted surgery. Ind Robot Int J 31(6):499–508CrossRefGoogle Scholar
  11. 11.
    Peters JH, Gibbons G, Innes J, Nichols K, Roby S, Ellison E (1991) Complications of laparoscopic cholecystectomy. Surgery 110(4):769–77PubMedGoogle Scholar
  12. 12.
    Sakellariou P, Protopapas AG, Voulgaris Z, Kyritsis N, Rodolakis A, Vlachos G, Diakomanolis E, Michalas S (2002) Management of ureteric injuries during gynecological operations: 10 years experience. Eur J Obstet Gynecol Reprod Biol 101(2):179–184CrossRefPubMedGoogle Scholar
  13. 13.
    Sie A, Winek M, Kowalewski TM (2014) Online identification of abdominal tissues in vivo for tissue-aware and injury-avoiding surgical robots. In: 2014 IEEE/RSJ international conference on intelligent robots and systems (IROS 2014), IEEE, pp 2036–2042Google Scholar
  14. 14.
    Steele SR, Maykel JA, Champagne BJ, Orangio GR (2014) Complexities in colorectal surgery: decision-making and management. Springer, BerlinCrossRefGoogle Scholar
  15. 15.
    Tholey G, Desai JP, Castellanos AE (2005) Force feedback plays a significant role in minimally invasive surgery: results and analysis. Ann Surg 241(1):102–109PubMedPubMedCentralGoogle Scholar
  16. 16.
    Wagner CR, Stylopoulos N, Jackson PG, Howe RD (2007) The benefit of force feedback in surgery: examination of blunt dissection. Presence Teleoper Virtual Environ 16(3):252–262CrossRefGoogle Scholar
  17. 17.
    Winkler A, Suchỳ J (2015) Implicit force control of a position controlled robot—a comparison with explicit algorithms. World Acad Sci Eng Technol Int J Comput Electr Autom Control Inf Eng 9(6):1454–1460Google Scholar
  18. 18.
    Yu X, Chizeck HJ, Hannaford B (2007) Comparison of transient performance in the control of soft tissue grasping. In: IEEE/RSJ international conference on intelligent robots and systems, 2007. IROS 2007, IEEE, pp 1809–1814Google Scholar

Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Department of UrologyUniversity of WashingtonSeattleUSA

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