Position control of medical cable-driven flexible instruments by combining machine learning and kinematic analysis

Abstract

Non-linearities in cable transmissions are important limitations for the accurate control of flexible instruments used in medical endoscopic systems. Hysteresis effects greatly impact the accuracy of conventional kinematic models. This is especially critical for implementing automatic motions in flexible medical robotic systems. In this paper, we propose a method for improving open-loop accuracy of flexible instruments by implementing a Position Inverse Kinematic Model which is able to take into account hysteresis effects. In order to avoid complex physical modeling, the method relies on the off-line learning of the behavior of the instruments. Basic knowledge of the kinematic is also incorporated in the learning process in order to make it fast. The validity of the approach is demonstrated by the execution of 2D and 3D trajectories with the instruments of the STRAS medical robot. The accuracy is shown to be significantly improved with respect to other learning-based methods.