Haptic Feedback in Surgical Robotics: Still a Challenge

  • Arturo Marbán
  • Alicia Casals
  • Josep Fernández
  • Josep Amat
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 252)

Abstract

Endowing current surgical robotic systems with haptic feedback to perform minimally invasive surgery (MIS), such as laparoscopy, is still a challenge. Haptic is a feature lost in surgical teleoperated systems limiting surgeons capabilities and ability. The availability of haptics would provide important advantages to the surgeon: Improved tissue manipulation, reducing the breaking of sutures and increase the feeling of telepresence, among others. To design and develop a haptic system, the measurement of forces can be implemented based on two approaches: Direct and indirect force sensing. MIS performed with surgical robots, imposes many technical constraints to measure forces, such as: Miniaturization, need of sterilization or materials compatibility, making it necessary to rely on indirect force sensing. Based on mathematical models of the components involved in an intervention and indirect force sensing techniques, a global perspective on how to address the problem of measurement of tool-tissue interaction forces is presented.

Keywords

surgical robotics haptic feedback indirect force sensing machine learning data fusion mathematical models 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Okamura, A.M.: Haptic feedback in robot-assisted minimally invasive surgery. Current Opinion in Urology 19(1), 102 (2009)CrossRefGoogle Scholar
  2. 2.
    van den Dobbelsteen, J.J., Lee, R.A., van Noorden, M.: Indirect measurement of pinch and pull forces at the shaft of laparoscopic graspers. Medical & Biological Engineering & Computing 50(3), 215–221 (2012)CrossRefGoogle Scholar
  3. 3.
    Sciavicco, L.: Robotics: modelling, planning and control. Springer (2009)Google Scholar
  4. 4.
    Picod, G., Jambon, A.C., Vinatier, D., Dubois, P.: What can the operator actually feel when performing a laparoscopy? Surgical Endoscopy and Other Interventional Techniques 19(1), 95–100 (2005)CrossRefGoogle Scholar
  5. 5.
    Van den Dobbelsteen, J.J., Schooleman, A., Dankelman, J.: Friction dynamics of trocars. Surgical Endoscopy 21(8), 1338–1343 (2007)CrossRefGoogle Scholar
  6. 6.
    Yang, T., Xiong, L., Zhang, J., Yang, L., Huang, W., Zhou, J., Liu, J., et al.: Modeling cutting force of laparoscopic scissors. In: 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI), pp. 1764–1768. IEEE (2010)Google Scholar
  7. 7.
    Meier, U., Lpez, O., Monserrat, C., Juan, M.C., Alcaniz, M.: Real-time deformable models for surgery simulation: a survey. Computer Methods and Programs in Biomedicine 77(3), 183–197 (2005)CrossRefGoogle Scholar
  8. 8.
    Rosen, J., Brown, J.D., Chang, L., Sinanan, M.N., Hannaford, B.: Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model. IEEE Transactions on Biomedical Engineering 53(3), 399–413 (2006)CrossRefGoogle Scholar
  9. 9.
    Kim, J., Janabi-Sharifi, F., Kim, J.: A haptic interaction method using visual information and physically based modeling. IEEE/ASME Transactions on Mechatronics 15(4), 636–645 (2010)CrossRefGoogle Scholar
  10. 10.
    Greminger, M.A., Nelson, B.J.: Modeling elastic objects with neural networks for vision-based force measurement. In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2003, vol. 2, pp. 1278–1283. IEEE (2003)Google Scholar
  11. 11.
    Frank, B., Schmedding, R., Stachniss, C., Teschner, M., Burgard, W.: Learning Deformable Object Models for Mobile Robot Navigation using Depth Cameras and a Manipulation Robot. In: Proc. of the IEEE Intl. Conf. on Robotics & Automation, ICRA (2010)Google Scholar
  12. 12.
    Bickel, B., Bcher, M., Otaduy, M.A., Matusik, W., Pfister, H., Gross, M.: Capture and modeling of non-linear heterogeneous soft tissue. ACM Transactions on Graphics (TOG) 28(3), 89 (2009)CrossRefGoogle Scholar
  13. 13.
    Fumagalli, M., Gijsberts, A., Ivaldi, S., Jamone, L., Metta, G., Natale, L., Nori, F., Sandini, G.: Learning to exploit proximal force sensing: a comparison approach. In: Sigaud, O., Peters, J. (eds.) From Motor Learning to Interaction Learning in Robots. SCI, vol. 264, pp. 149–167. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Jamali, N., Sammut, C.: Slip prediction using Hidden Markov models: Multidimensional sensor data to symbolic temporal pattern learning. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 215–222. IEEE (2012)Google Scholar
  15. 15.
    Sinapov, J., Sukhoy, V., Sahai, R., Stoytchev, A.: Vibrotactile recognition and categorization of surfaces by a humanoid robot. IEEE Transactions on Robotics 27(3), 488–497 (2011)CrossRefGoogle Scholar
  16. 16.
    Kuchenbecker, K.J., Gewirtz, J., McMahan, W., Standish, D., Martin, P., Bohren, J., Mendoza, P.J., Lee, D.I.: VerroTouch: high-frequency acceleration feedback for telerobotic surgery. In: Kappers, A.M.L., van Erp, J.B.F., Bergmann Tiest, W.M., van der Helm, F.C.T. (eds.) EuroHaptics 2010, Part I. LNCS, vol. 6191, pp. 189–196. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    McMahan, W., Gewirtz, J., Standish, D., Martin, P., Kunkel, J.A., Lilavois, M., Wedmid, A., Lee, D.I., Kuchenbecker, K.J.: Tool contact acceleration feedback for telerobotic surgery. IEEE Transactions on Haptics 4(3), 210–220 (2011)CrossRefGoogle Scholar
  18. 18.
    Reiley, C.E., Akinbiyi, T., Burschka, D., Chang, D.C., Okamura, A.M., Yuh, D.D.: Effects of visual force feedback on robot-assisted surgical task performance. The Journal of Thoracic and Cardiovascular Surgery 135(1), 196–202 (2008)CrossRefGoogle Scholar
  19. 19.
    Talasaz, A., Trejos, A.L., Patel, R.V.: Effect of force feedback on performance of robotics-assisted suturing. In: 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 823–828. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Arturo Marbán
    • 1
  • Alicia Casals
    • 1
    • 2
  • Josep Fernández
    • 1
  • Josep Amat
    • 1
  1. 1.Universitat Politècnica de Catalunya, BarcelonaTechBarcelonaSpain
  2. 2.Institute for Bioengineering of CataloniaBarcelonaSpain

Personalised recommendations