• DocumentCode
    3596228
  • Title

    An online method for tight-tolerance insertion tasks for string and rope

  • Author

    Weifu Wang ; Berenson, Dmitry ; Balkcom, Devin

  • Author_Institution
    Dartmouth Coll., Dartmouth, MA, USA
  • fYear
    2015
  • Firstpage
    2488
  • Lastpage
    2495
  • Abstract
    This paper presents a fast tight-tolerance threading technique for string and rope. Instead of relying on simulations of these deformable objects to plan a path or compute control actions, we control the movement of the string with a virtual magnetic vector field emanating from the narrow openings we wish to thread through. We compute an approximate Jacobian to move the tip of the string through the vector field and propose a method to promote alignment of the head of the string to the opening. We also propose a method for re-grasping the string based on the relationship between the string´s configuration, the orientation of the opening, and direction of gravity. This re-grasping method in conjunction with our controller can be used to thread the string through a sequence of openings. We evaluated our method in simulation (with simulated sensor noise) and on the Da Vinci surgical robot. Our results suggest that our method is quite robust to errors in sensing, and is capable of real-world threading tasks with the da Vinci robot, where the diameter of the string (3.5mm) and opening (4.9mm) differ by only 1.4 mm.
  • Keywords
    medical robotics; motion control; surgery; vectors; approximate Jacobian; da Vinci surgical robot; online method; rope; string movement control; string regrasping; tight-tolerance insertion task; tight-tolerance threading technique; virtual magnetic vector field; Computational modeling; Deformable models; Gravity; Grippers; Jacobian matrices; Robot sensing systems; Yarn;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Robotics and Automation (ICRA), 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/ICRA.2015.7139532
  • Filename
    7139532