Joint-level force sensing for indirect hybrid force/position control of continuum robots with friction

Abstract

Continuum robots offer the dexterity and obstacle circumvention capabilities necessary to enable surgery in deep surgical sites. They also can enable joint-level ex situ force sensing (JEFS), which provides an estimate of end-effector wrenches given joint-level forces. Prior works on JEFS relied on a restrictive embodiment with minimal actuation line friction and captured model and frictional actuation transmission uncertainties using a configuration space formulation. In this work, we overcome these limitations. First, frictional losses are canceled using a feed-forward term based on support vector regression in joint space. Then, regression maps and their interpolation are used to account for actuation hysteresis. The residual joint-force error is then further minimized using a least-squares model parameter update. An indirect hybrid force/position controller using JEFS is presented with evaluation carried out on a realistic pre-clinically deployable insertable robotic effectors platform (IREP) for single-port access surgery. Automated mock force-controlled ablation, exploration, and knot tightening are evaluated. A user study involving the daVinci Research Kit surgeon console and the IREP as a surgical slave was carried out to compare the performance of users with and without force feedback based on JEFS for force-controlled ablation and knot tightening. Results in automated experiments and a user study of telemanipulated experiments suggest that intrinsic force-sensing can achieve levels of force uncertainty and force regulation errors of the order of 0.2 N. Using JEFS and automated task execution, repeatability, and force regulation accuracy is shown to be comparable to using a commercial force sensor for human-in-the-loop feedback.