Miniaturized Optical Force Sensor for Minimally Invasive Surgery With Learning-Based Nonlinear Calibration

Abstract

A simple and miniaturized optical tactile sensor for integrating with robotic and manual minimally invasive surgery graspers is proposed in this study. For better miniaturization, the sensing principle of constant-bending-radius light intensity modulation was replaced with a variable-bending-radius modulation principle, and the pertinent theoretical formulation was derived. Afterward, a finite element model of the sensor was optimized using response surface optimization technique. The optimized sensor design was 14.0 mm long, 1.8 mm wide and 4 mm high. Next, the sensor was prototyped using SLA 3D printing technique. Also, the sensor was calibrated using a rate-dependent learning-based support-vector-regression algorithm. Calibration was 96% linear with a goodness-of-fit of 93% and mean absolute error of 0.085±0.096 N. Furthermore, the sensor was tested under cyclic triangular compression with a 3 sec pause between loading and unloading as well as manual grasping. Mean absolute error of 0.12±0.08 N, the minimum force of 0.14 N, and repeatability of 0.07 N showed the acceptable performance of the proposed sensor for surgical applications. Moreover, the sensor showed the capability of working under combined dynamic and static loading conditions with low hysteresis, i.e., 0.057 N/cycle.