Vision-Based Cutting Control of Deformable Objects With Surface Tracking

Abstract

Automatic cutting is an essential task in the field ofrobot-assisted surgery. In this article, a novel vision-based cutting control algorithm is proposed to cut a deformable object along the predesigned path with specified cutting depth. The method to model soft tissue considering viscoelasticity is developed, and unknown parameters of the deformation model are estimated online by introducing the visual feedback of feature points. According to the position of trajectory points after deformation, we illustrate how to generate the desired pose of the knife that avoids large deformation. In this way, the cutting task has been recast into a visual tracking problem, that is, controlling the knife's projection to track a target surface in the image plane. To cope with this problem, we choose two parallel linear segments (including the direction and the length) extracted from the edge of the knife's projection as image features, which has a one-to-one mapping with the pose of the knife, and design a dynamic-based controller based on the combined feature. The asymptotic stability of the closed-loop system is proved by Lyapunov analysis. A series of experiments using different materials are conducted to validate the effectiveness of the proposed cutting control algorithm.