A predictive model for haptic assistance in robot assisted trocar insertion

Abstract

In this work we propose a new Prediction from Expert Demonstration (PED) methodology to provide haptic assistance in robot assisted trocar surgery. Data was collected from expert (clinician) demonstrations for the procedure of trocar insertion. We encode a set of force, torque and penetration trajectories by using a Gaussian Mixture Model (GMM). A generalization of these profiles and associated parameters are retrieved by Gaussian Mixture Regression (GMR). A haptic assistance mode was devised to help novices perform the procedure based on the proposed PED model. We validated the methodology for surgical assistance on (n= 15) participants. The PED haptic model was tested for instrument deviation, penetration force and penetration depth. Preliminary study results showed that participants with PED haptic assistance performed the task with more consistency and exerted lesser penetration force than subjects without assistance.