Despite the increasing use of the da Vinci surgical robot, clinicians often claim that the inclusion of force measurement at the grasper could enhance the use of these robots in surgery. Many methods have been proposed to accurately estimate this force using already-existing sensors on the da Vinci robot. However, a key weakness in these methods is that they rely on a training dataset which was likely obtained at the beginning of a tool's life, and does not accurately represent the state of the tool throughout use. This work aims to address this problem by assessing the grip force estimation error over the lifetime of a single da Vinci tool, and to propose a method to maintain this estimation error at less than 2 mNm. We found that the most significant changes in the tool were seen in the first 1,000 grasps. Despite these changes, our method to periodically retrain our algorithm maintained the error under 2 mNm. An accurate estimation error has implications in haptics as well as obtaining in-vivo tissue properties during surgical procedures.
|Original language||English (US)|
|Title of host publication||2018 International Symposium on Medical Robotics, ISMR 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Apr 6 2018|
|Event||2018 International Symposium on Medical Robotics, ISMR 2018 - Atlanta, United States|
Duration: Mar 1 2018 → Mar 3 2018
|Name||2018 International Symposium on Medical Robotics, ISMR 2018|
|Other||2018 International Symposium on Medical Robotics, ISMR 2018|
|Period||3/1/18 → 3/3/18|
Bibliographical noteFunding Information:
Research was sponsored in part by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-14-2-0035. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. Additionally, this material is based upon work supported in part by the National Science Foundation Graduate Research Fellowship under Grant No. 00039202. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
© 2018 IEEE.