ViO-Com: Feed-Forward Compensation Using Vision-Based Optimization for High-Precision Surgical Manipulation

Abstract

Tendon-sheath mechanisms offer a means for a flexible surgical robot to be operated efficiently in restricted environments, for example, the long and narrow paths inside human organs. However, nonlinear hysteresis interferes with the precise motion control of a flexible robot. Generally, the characteristics of hysteresis can change due to changes in cable-related effects and robot shapes, which increase the difficulty associated with achieving precise control of surgical robots. Although several methods have been proposed to solve this problem, most of these methods offer limited performance and are unstable in realistic situations. In this letter, we present a vision-based optimized feedforward compensation (ViO-Com) scheme for a cable-driven surgical robot that reduces hysteresis as a practical approach, regardless of any unmeasured hysteresis while maintaining stable motion. A CycleGAN and a Siamese CNN were used to estimate the actual joint angle of a surgical manipulator, and the Bouc-Wen model with optimized parameters was used for feed-forward compensation. The results obtained using a real surgical robot platform K-FLEX suggest that the performance of ViO-Com is superior to that of vision-based feedback compensation under various hysteresis condition, and its accuracy is improved by 67%.