Target Localization and Grasping of Parallel Robots with Multi-Vision Based on Improved RANSAC Algorithm

Abstract

Some traditional robots are based on offline programming reciprocal motion, and with the continuous upgrades in vision technology, more and more tasks are being replaced with machine vision. At present, the main method of target recognition used in palletizers is the traditional SURF algorithm, but this method of grasping leads to low accuracy due to the influence of too many mis-matched points. Due to the accuracy of robot target localization with binocular-based vision being low, an improved random sampling consistency algorithm for performing complete parallel robot target localization and grasping under the guidance of multi-vision is proposed. Firstly, the improved RANSAC algorithm, based on the SURF algorithm, was created based on the SURF algorithm; next, the parallax gradient method was applied to iterate the matched point pairs several times to further optimize the data; then, the 3D reconstruction was completed using the improved algorithm via the program technique; finally, the obtained data were input into the robot arm, and the camera’s internal and external parameters were obtained using the calibration method so that the robot could accurately locate and grasp objects. The experiments show that the improved algorithm shows better recognition accuracy and grasping success with the multi-vision approach.