Incipient Slip Sensing for Improved Grasping in Robot Assisted Surgery

Abstract

The limited grasping control available in Robot Assisted Surgery is considered a significant limitation of the technology. Traditionally the integration of haptic feedback has been proposed to resolve this issue but has found limited adoption. Here we investigate an alternate approach based on the concept of detecting localised slips caused by the intrinsic elastic properties of soft tissues. This method allows for the early detection of slip so that mitigating actions can be taken before gross slip can occur, allowing the grasper to minimise the force required to maintain stable grasp control. In this paper we detail the design of a sensor developed to detect incipient slip by monitoring the relative difference in tissue movement at the front and back of the grasper, caused by tissue slip. We then demonstrate the sensor’s efficacy for the early detection of slip, as well as its ability to automate grasping under representative surgical conditions, with the automated case providing comparable performance to one which uses the maximum allowable grasp force. This work provides evidence that the slip detection methodology developed is consistently able to detect incipient slip before macro slip occurs, thus offering a strong basis for its use in automating surgical grasping tasks to avoid tissue trauma and slip.