Da Vinci tool torque mapping over 50,000 grasps and its implications on grip force estimation accuracy

Abstract

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.