Professor Shane Farritor
Date of this Version
Brown, Evan. “Ring and Peg Simulation for Minimally Invasive Surgical Robot .” University of Nebraska Lincoln, 2018.
Surgical procedures utilizing minimally invasive laparoscopic techniques have shown less complications, better cosmetic results, and less time in the hospital than conventional surgery. These advantages are partially offset by inherent difficulties of the procedures which include an inverted control scheme, instrument clashing, and loss of triangulation. Surgical robots have been designed to overcome the limitations, the Da Vinci being the most widely used. A dexterous in vivo, two-armed robot, designed to enter an insufflated abdomen with a limited insertion profile and expand to perform a variety of operations, has been created as a less expensive, versatile alternative to the Da Vinci. Various surgical simulators are currently marketed to help with the rigors of training and testing potential surgeons for the Da Vinci system, and have been proven to be effective at improving surgical skills. Using the existing simulators as a baseline, the goal of this thesis was to design, build, and test a ring and peg simulation that emulates the four degree of freedom minimally invasive surgical robot from UNL. The simulation was created in the virtual reality software platform Vizard using the python programming language. Featuring imported visual models and compound simple shape collision objects, the simulation monitors and generates a metric file that records the user’s time to task completion along with various errors. A preliminary study was done on the simulation that measured seven participant’s performance on the simulation over three consecutive attempts. The study showed that participant’s time to completion and amount of recorded errors decreased across the three trials, indicating improvement in the robot operation with use of the simulation. The validation study provided confidence in continued development and testing of the introductory surgical robot simulation trainer.
Adviser: Shane Farritor