Knot tying is a fundamental dexterous surgical subtask that is a key step in suturing. One challenge to robot augmentation is limited depth perception due to the small baseline of surgical endoscopic cameras. In this work, we present Surgical D-Knot: an augmented dexterity pipeline combining learned perception with model-based methods to perform surgical double knots using only one monocular RGB camera. This pipeline includes 2D grasp point identification, 3D suture thread grasping using local feature servoing, suture thread wrapping using relative motion and 2D re-grasp point identification. Human dexterity is required for initial thread setup and thread cutting after each double knot. Physical experiments with 120 double knot trials result in a success rate of 80.83% for the initial knot and 55.83% for the second knot. Translation of surgical knot tying to chicken skin results in success rates of 73.75% for the initial knot and 40% for the second knot. Each double knot requires on average 70 seconds. This is the first work to our knowledge that augments human dexterity for double knot tying. https://sites.google.com/view/surgicaldknot/

The paper briefly describes daVinci/sup TM/, a surgical telerobot designed to provide enhanced dexterity to doctors performing minimally invasive surgical procedures. The rationale for a full 7-degree-of-freedom master-slave system is presented along with a discussion of the resulting computational architecture and recent clinical applications.