Evaluation of a pneumatic surgical robot with dynamic force feedback Original Article First Online: 25 September 2018 Abstract
Robot-assisted surgery is limited by the lack of haptic feedback and increased operating times. Force scaling adjusts feedback transmitted to the operator through the use of scaling factors. Herein, we investigate how force scaling affects forces exerted in robotic surgery during simple and complex tasks, using a pneumatic surgical robot, IBIS VI. Secondary objectives were to test the effects of force scaling on operating time, depth of needle insertion and user satisfaction. Two novice males performed simple (modified block transfer) and complex (needle insertion) tasks under four scaling factors: 0.0, 0.5, 1.0 and 2.0. Single-blind experiments were repeated five times, with alternating scaling factors in random order. Increasing the scaling factor from 0.0 to 2.0 reduces forces in block transfer (
p = 0.04). All feedback conditions reduce forces in needle insertion compared to baseline (0.5: p < 0.001, 1.0: p = 0.001, 2.0: p = 0.001). Time to complete block transfer is shorter for scaling factor 0.5 ( p = 0.02), but not for 1.0 ( p = 0.05) or 2.0 ( p = 0.48), compared to baseline. Depth of needle insertion decreases consistently with incremental scaling factors ( p < 0.001). Further reductions are observed upon augmenting feedback (0.5–2.0: p = 0.02). User satisfaction in block transfer is highest for intermediate scaling factors (0.0–1.0: p = 0.01), but no change is observed in needle insertion ( p = 0.99). Increments in scaling factor reduce forces exerted, particularly in tasks requiring precision. Depth of needle insertion follows a similar pattern, but operating time and user satisfaction are improved by intermediate scaling factors. In summary, dynamic adjustment of force feedback can improve operative outcomes and advance surgical automation. Keywords Robot-assisted surgery Force feedback Force scaling Pneumatic surgical robot Block transfer Needle insertion Electronic supplementary material
The online version of this article (
) contains supplementary material, which is available to authorized users. https://doi.org/10.1007/s11701-018-0878-2 Notes Acknowledgements
The authors thank Riverfield inc. for providing the infrastructure to conduct this research.
Part of this research is based on the Cooperative Research Project of the Research Centre for Biomedical Engineering. Author-DK was supported by the JASSO scholarship.
Compliance with ethical standards Conflict of interest
Authors DK, YK, RM, TK and KK declare that they have no conflict of interest.
Supplementary material 3 (MP4 2105 KB)
Supplementary material 4 (MP4 2294 KB)
Hashizume M, Tsugawa K (2004) Robotic surgery and cancer: the present state, problems and future vision. Jpn J Clin Oncol 34(5):227–237.
https://doi.org/10.1093/jjco/hyh053 CrossRef Google Scholar
Nio D, Bemelman WA, Busch OR, Vrouenraets BC, Gouma DJ (2004) Robot-assisted laparoscopic cholecystectomy versus conventional laparoscopic cholecystectomy: a comparative study. Surg Endosc 18(3):379–382
CrossRef Google Scholar
Roh HF, Nam SH, Kim JM (2018) Robot-assisted laparoscopic surgery versus conventional laparoscopic surgery in randomized controlled trials: a systematic review and meta-analysis. PLoS One 13(1):e0191628.
https://doi.org/10.1371/journal.pone.0191628 CrossRef Google Scholar
Breitenstein S, Nocito A, Puhan M, Held U, Weber M, Clavien PA (2008) Robotic-assisted versus laparoscopic cholecystectomy: outcome and cost analyses of a case-matched control study. Ann Surg 247(6):987–993.
https://doi.org/10.1097/SLA.0b013e318172501f CrossRef Google Scholar
Wright JD, Ananth CV, Lewin SN et al (2013) Robotically assisted vs laparoscopic hysterectomy among women with benign gynecologic disease. JAMA 309(7):689–698.
https://doi.org/10.1001/jama.2013.186 CrossRef Google Scholar
Enayati N, De Momi E, Ferrigno G (2016) Haptics in robot-assisted surgery: challenges and benefits. IEEE Rev Biomed Eng 9:49–65.
https://doi.org/10.1109/RBME.2016.2538080 CrossRef Google Scholar
Hannaford B, Okamura AM (2008) Haptics. In: Siciliano B, Khatib O (eds) Springer handbook of robotics. Springer, Berlin, pp 719–739
CrossRef Google Scholar
Puangmali P, Althoefer K, Seneviratne LD, Murphy D, Dasgupta P (2008) State-of-the-art in force and tactile sensing for minimally invasive surgery. IEEE Sens J 8(4):371–381.
https://doi.org/10.1109/JSEN.2008.917481 CrossRef Google Scholar
Johansson RS, Westling G (1984) Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects. Exp Brain Res 56(3):550–564.
https://doi.org/10.1007/BF00237997 CrossRef Google Scholar
King CH, Culjat MO, Franco ML et al (2009) Tactile feedback induces reduced grasping force in robot-assisted surgery. IEEE Trans Haptics 2(2):103–110.
https://doi.org/10.1109/TOH.2009.4 CrossRef Google Scholar
Wottawa CR, Genovese B, Nowroozi BN et al (2016) Evaluating tactile feedback in robotic surgery for potential clinical application using an animal model. Surg Endosc 30(8):3198–3209.
https://doi.org/10.1007/s00464-015-4602-2 CrossRef Google Scholar
Roy J, Rothbaum DL, Whitcomb LL (2002) Haptic feedback augmentation through position based adaptive force scaling: theory and experiment. IEEE RSJ Int Conf Intell Robots Syst 3:2911–2919.
https://doi.org/10.1109/IRDS.2002.1041714 Google Scholar
Rizun P, Gunn D, Cox B, Sutherland G (2006) Mechatronic design of haptic forceps for robotic surgery. Int J Med Robot 2(4):341–349.
https://doi.org/10.1002/rcs.110 CrossRef Google Scholar
Tadano K, Kawashima K, Kojima K, Tanaka N (2009) Development of a pneumatically driven forceps manipulator IBIS IV. 2009 ICCAS-SICE conference. Fukuoka, Japan, pp 179–188
Kasahara Y, Kawana H, Usuda S, Ohnishi K (2012) Telerobotic-assisted bone-drilling system using bilateral control with feed operation scaling and cutting force scaling. Int J Med Robot 8(2):221–229.
https://doi.org/10.1002/rcs.457 CrossRef Google Scholar
Sariyildiz E, Ohnishi K (2014) An adaptive reaction force observer design. IEEE ASME Trans Mechatron 20(2):750–760.
https://doi.org/10.1109/TMECH.2014.2321014 CrossRef Google Scholar
Peddamatham S, Peine W, Tan H (2008) Assessment of vibrotactile feedback in a needle-insertion task using a surgical robot. 2008 symposium on haptic interfaces for virtual environment and teleoperator systems. Reno, Nevada, pp 93–99.
https://doi.org/10.1109/HAPTICS.2008.4479920 CrossRef Google Scholar
Gwilliam JC, Mahvash M, Vagvolgyi B, Vacharat A, Yuh DD, Okamura AM (2009) Effects of haptic and graphical force feedback on teleoperated palpation. 2009 IEEE international conference on robotics and automation. Kobe, Japan, pp 677–682.
https://doi.org/10.1109/ROBOT.2009.5152705 CrossRef Google Scholar
Deml B, Ortmaier T, Weiss H (2004) Minimally invasive surgery: empirical comparison of manual and robot assisted force feedback surgery. EuroHaptics, Munich, Germany, pp 403–406
Wagner C, Howe R (2007) Force feedback benefit depends on experience in multiple degree of freedom robotic surgery task. IEEE Trans Robot 23(6):1235–1240.
https://doi.org/10.1109/TRO.2007.904891 CrossRef Google Scholar
Yaxley JW, Coughlin GD, Chambers SK et al (2016) Robot-assisted laparoscopic prostatectomy versus open radical retropubic prostatectomy: early outcomes from a randomised controlled phase 3 study. Lancet 388(10049):1057–1066.
https://doi.org/10.1016/S0140-6736(16)30592-X CrossRef Google Scholar
Cundy TP, Harling L, Hughes-Hallett A et al (2014) Meta-analysis of robot-assisted vs conventional laparoscopic and open pyeloplasty in children. BJU Int 114(4):582–594.
https://doi.org/10.1111/bju.12683 CrossRef Google Scholar
Wagner C, Stylopoulos N, Howe R (2002) The role of force feedback in surgery: analysis of blunt dissection. Haptics.
https://doi.org/10.1109/HAPTIC.2002.998943 Google Scholar
Van der Schatte Olivier RH, Van’t Hullenaar CD, Ruurda JP, Broeders IA (2009) Ergonomics, user comfort, and performance in standard and robot-assisted laparoscopic surgery. Surg Endosc 23(6):1365–1371.
https://doi.org/10.1007/s00464-008-0184-6 CrossRef Google Scholar
Nakagawa S (2004) A farewell to the Bonferroni: the problems of low statistical power and publication bias. Behav Ecol 15(6):1044.
https://doi.org/10.1093/beheco/arh107 CrossRef Google Scholar
Shademan A, Decker RS, Opfermann JD, Leonard S, Krieger A, Kim PC (2016) Supervised autonomous robotic soft tissue surgery. Sci Transl Med 8(337):337ra64.
https://doi.org/10.1126/scitranslmed.aad9398 CrossRef Google Scholar
Haidegger T, Benyó B, Kovács L, Benyó Z (2009) Force sensing and force control for surgical robots. IFAC Proc Vol. 42(12):401–406
https://doi.org/10.3182/20090812-3-DK-2006.0035 CrossRef Google Scholar
Modi HN, Singh H, Orihuela-Espina F et al (2018) Temporal stress in the operating room: brain engagement promotes “coping” and disengagement prompts “choking”. Ann Surg 267(4):683–691.
https://doi.org/10.1097/SLA.0000000000002289 CrossRef Google Scholar
Davies BL, Harris SJ, Lin WJ, Hibberd RD, Middleton R, Cobb JC (1997) Active compliance in robotic surgery—the use of force control as a dynamic constraint. Proc Inst Mech Eng H 211(4):285–292.
https://doi.org/10.1243/0954411971534403 CrossRef Google Scholar
Cobb J, Henckel J, Gomes P et al (2006) Hands-on robotic unicompartmental knee replacement: a prospective, randomised controlled study of the acrobot system. J Bone Joint Surg Br 88(2):188–197.
https://doi.org/10.1302/0301-620X.88B2.17220 CrossRef Google Scholar
Meccariello G, Faedi F, AlGhamdi S et al (2016) An experimental study about haptic feedback in robotic surgery: may visual feedback substitute tactile feedback? J Robot Surg 10(1):57–61.
https://doi.org/10.1007/s11701-015-0541-0 CrossRef Google Scholar Copyright information
© Springer-Verlag London Ltd., part of Springer Nature 2018