Robotic grasping method of bolster spring based on image-based visual servoing with YOLOv3 object detection algorithm

In this paper, to address the problem of automatic positioning and grasping of bolster spring with complex geometric features and cluttered background, a novel image-based visual servoing (IBVS) control method based on the corner points features of YOLOv3 object detection bounding box is proposed and applied to the robotic grasping system of bolster spring. The YOLOv3 object detection model is used to detect and position the bolster spring and then based on the corner points features of the bolster spring bounding box, the IBVS controller is designed to drive the end effector of the robot to the desired pose. This method adopts the approach-align-grasp control strategy to achieve the grasping of the bolster spring, which is very robust to the calibration parameter errors of the camera and the robot model. With the help of Robotics and Machine Vision Toolboxes in Matlab, IBVS simulation analysis based on feature points is carried out. The results show that it is reasonable to use the corner points of the object detection bounding box as image features under the initial pose where the image plane is parallel to the object plane. The positioning and grasping experiments are conducted on the robotic grasping platform of bolster spring. The results show that this method is effective for automatic positioning and grasping of bolster spring with complex geometric features and cluttered background, and it has strong robustness to the change of illumination.