Second-Order Position-Based Visual Servoing of a Robot Manipulator

Visual Servoing is an established approach for controlling robots using visual feedback. Most controllers in this domain generate velocity control signals to guide the cameras to desired positions and orientations. However, the dynamic characteristics of conventional visual servoing controllers may be unsatisfactory, and the velocity signal itself hinders the connection between the feature velocity model and the robot's dynamics. Consequently, research has explored models incorporating the second-order derivative of features and the robot's acceleration. The current state-of-the-art techniques mainly focus on image-based visual servoing, which deals with feature errors in the image domain. In this work, we propose an acceleration-based controller for the position-based visual servoing framework, which models the error in Cartesian space. Our approach involves extracting an acceleration control signal from the traditional velocity-based controller. To achieve this, we redefine the camera orientation using quaternions, generate new interaction matrices, and conduct comprehensive comparative experiments in simulated and real robot scenarios. We show that our method provides better dynamic properties in both image and Cartesian spaces, superior tracking performance, and less sensitivity to noise compared to velocity controllers.