Image-Based Visual-Admittance Control With Prescribed Performance of Manipulators in Feature Space

Abstract

Visual servoing systems provide higher safety and autonomy when dealing with unstructured environments. Visual and force sensing are essential for accurate and reliable robotic manipulation. In this article, a novel image-based visual-admittance control with prescribed performance of manipulator is presented. The method couples visual and force information in feature space, which avoids control limitations due to inconsistent driver layers and improves system flexibility. Thanks to the prescribed performance and the tan-type barrier Lyapunov function, the image feature error is satisfied under the finite field-of-view constraint, which improves the transient and steady-state performance of the visual servoing system. The proposed image-based visual-admittance control with prescribed performance is experimentally validated. The results show that the control framework improves the transient/steady-state response and task success of the system.