Gaussian Process Regression for Sensorless Grip Force Estimation of Cable-Driven Elongated Surgical Instruments

摘要

Haptic feedback is a critical but a clinically missing component in robotic minimally invasive surgeries. This paper proposes a Gaussian process regression (GPR) based scheme to address the gripping force estimation problem for clinically commonly used elongated cable-driven surgical instruments. Based on the cable-driven mechanism property studies, and surgical robotic system properties, four different GPR filters were designed and analyzed, including one GPR filter with two-dimensional inputs, one GPR filter with three-dimensional inputs, one GPR unscented Kalman filter (UKF) with two-dimensional inputs, and one GPR UKF with three-dimensional inputs. The four proposed methods were compared with the dynamic model based UKF filter on a 10 mm gripper on the Raven II surgical robot platform. The experimental results demonstrated that the four proposed methods outperformed the dynamic model based method on precision and reliability without parameter tuning. And surprisingly, among the four methods, the simplest GPR Filter with two-dimensional inputs has the best performance.