Hybrid Visual-Ranging Servoing for Positioning Based on Image and Measurement Features

In this article, a hybrid visual-ranging servoing method is proposed to realize high-precision positioning tasks with a 6-degree of freedom (DOF) manipulator. This method utilizes the image and measurement features directly in the control loop. Without the need of complex image feature design and attitude estimation, this method realizes the 6-DOF control of a robot. A vital challenge in traditional vision-based systems is avoiding local minima and singularity problems. To tackle this issue, a full-rank interaction matrix hybrid visual servo (FRHVS) design criterion is proposed, which guarantees that the hybrid interaction matrix and its pseudoinverse matrix are both full rank. Moreover, the interaction matrix for these hybrid strategies, which combines image features with other sensors features, is derived in an analytical form. Experiments on a 6-DOF manipulator show that the proposed method is effective and has global asymptotic stability and high precision.

I. Introduction

Visual servoing is one of the most important topics in intelligent robot control and is widely used in grasping [1], [2]; manufacturing [3]; quality inspection [4]; mobile robot motion control [5], [6]; and other tasks. There are two classical visual servo schemes: 1) position-based visual servo (PBVS) and 2) image-based visual servo (IBVS). For the PBVS scheme, visual information is used to calculate the position and orientation of the camera with respect to the reference coordinate frame of the target object. Then, the PBVS controller is designed to guide the robot to the desired Cartesian position. However, the PBVS scheme is particularly dependent on well-calibrated cameras and the known geometric model of the target. Hence, the control performance is largely influenced by the accuracy of the camera calibration and the target’s geometric model [7], [8].