Advertisement

Manipulation Using the “Utah” Prosthetic Hand: The Role of Stiffness in Manipulation

  • Radhen Patel
  • Jacob Segil
  • Nikolaus Correll
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 816)

Abstract

We describe our approach to the IROS “Hand-in-Hand” manipulation challenge using a simple one degree-of-freedom prehensor, which is known to be highly effective in prosthetic applications. The claw consists of two prongs of which only one is mobile, requiring the user to first make contact with the immobile prong to create a constraint and then use the second prong to exert force on the object. Despite its simplicity, this design is able to grasp a wide variety of objects and reliably manipulate them. In particular, stiffness is advantageous both when manipulating very small objects, where force needs to be applied precisely, as well as heavy ones, where forces needs to be exerted without deforming the claw itself. This approach reaches its limitations during tasks that require more degrees of freedom, for example grasping and subsequently actuating scissors. These tasks instead highlight the benefits of compliance and underactuation, stimulating a discussion about trade-offs in hand designs.

Notes

Acknowledgments

This research has been supported by the Airforce Office of Scientific Research (AFOSR) and the Korean government, we are grateful for this support.

References

  1. 1.
    Baxter Robot Grippers, Rethink Robotics. http://www.rethinkrobotics.com/accessories/
  2. 2.
  3. 3.
    Electric Terminal Device (ETD), Motion Control. http://www.utaharm.com/ETD-Sales-Sheet.pdf
  4. 4.
    ILIMB User Manual, Touch Bionics. http://www.touchbionics.com
  5. 5.
  6. 6.
  7. 7.
  8. 8.
    Belter, J.T., Segil, J.L.: Mechanical design and performance specifications of anthropomorphic prosthetic hands: a review. J. Rehabil. Res. Dev. 50(5), 599 (2013)CrossRefGoogle Scholar
  9. 9.
    Biagiotti, L., Lotti, F., Melchiorri, C., Vassura, G.: How Far is the Human Hand. A Review on Anthropomorphic Robotic end Effectors. University of Bologna, Bologna (2008)Google Scholar
  10. 10.
    Calli, B., Walsman, A., Singh, A., Srinivasa, S., Abbeel, P., Dollar, A.M.: Benchmarking in manipulation research: The YCB object and model set and benchmarking protocols. arXiv preprint arXiv:1502.03143 (2015)
  11. 11.
    Catalano, M.G., Grioli, G., Farnioli, E., Serio, A., Piazza, C., Bicchi, A.: Adaptive synergies for the design and control of the Pisa/IIT softhand. Int. J. Robot. Res. 33(5), 768–782 (2014)CrossRefGoogle Scholar
  12. 12.
    Correll, N., Bekris, K.E., Berenson, D., Brock, O., Causo, A., Hauser, K., Okada, K., Rodriguez, A., Romano, J.M., Wurman, P.R.: Analysis and observations from the first Amazon picking challenge. IEEE Trans. Autom. Sci. Eng. (2016). (to appear)Google Scholar
  13. 13.
    Deimel, R., Brock, O.: A novel type of compliant and underactuated robotic hand for dexterous grasping. Int. J. Robot. Res. 35(1–3), 161–185 (2015).  https://doi.org/10.1177/0278364915592961CrossRefGoogle Scholar
  14. 14.
    Dollar, A.M., Howe, R.D.: The highly adaptive sdm hand: design and performance evaluation. Int. J. Robot. Res. 29(5), 585–597 (2010)CrossRefGoogle Scholar
  15. 15.
    Farinha, A., Lima, P.U.: A novel underactuated hand suitable for human-oriented domestic environments. In: 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 106–111. IEEE (2016)Google Scholar
  16. 16.
    Farrow, N., Li, Y., Correll, N.: Morphological and embedded computation in a self-contained soft robotic hand. arXiv preprint arXiv:1605.00354 (2016)
  17. 17.
    Fearing, R.: Simplified grasping and manipulation with dextrous robot hands. IEEE J. Robot. Autom. 2(4), 188–195 (1986)CrossRefGoogle Scholar
  18. 18.
    McEvoy, M.A., Correll, N.: Thermoplastic variable stiffness composites with embedded, networked sensing, actuation, and control. J. Compos. Mater. 49(15), 1799–1808 (2015)CrossRefGoogle Scholar
  19. 19.
    Patel, R., Alastuey, J.C., Correll, N.: Improving grasp performance using in-hand proximity and force sensing. In: International Symposium on Experimental Robotics (ISER), Tokyo, Japan (2016)Google Scholar
  20. 20.
    Rossini, P.M., Micera, S., Benvenuto, A., Carpaneto, J., Cavallo, G., Citi, L., Cipriani, C., Denaro, L., Denaro, V., Di Pino, G., et al.: Double nerve intraneural interface implant on a human amputee for robotic hand control. Clin. Neurophysiol. 121(5), 777–783 (2010)CrossRefGoogle Scholar
  21. 21.
    Weir, R.: The great divide-the human-machine interface. Issues in the control of prostheses, manipulators, and other human machine systems. In: Proceedings of the 2003 IEEE 29th Annual Bioengineering Conference, pp. 275–276. IEEE (2003)Google Scholar
  22. 22.
    Weiss, E.J., Flanders, M.: Muscular and postural synergies of the human hand. J. Neurophysiol. 92(1), 523–535 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Colorado BoulderBoulderUSA

Personalised recommendations