Robot arm pose prediction method based on image gradient vector mapping

Abstract

ObjectivesIn order to solve the problems of the complexity of the existing robot arm pose prediction algorithm model and its over-reliance on the parameters of the camera and robot, a new robot arm pose prediction method based on RGB image gradient vector mapping is proposed. MethodsFirst, a series of robot arm image texture gradient features is calculated based on the Histogram of Oriented Gradient (HOG) algorithm. The mapping relationship between the image features and joint angles of the robot arm is then established by training Deep Neural Networks (DNNs). Finally, the pre-trained vector mapping model is used to quickly predict the pose of the robot arm in a motion frame image. The training and test datasets of the model are generated by synthetic data techniques. ResultsThe results show that the average error of the angle prediction of the three joints of the target robot arm is 2.92°, and the pose prediction time of a single image is about 0.08 s. ConclusionsThe results show that the proposed pose prediction method has better prediction speed and accuracy, and only uses RGB image information to achieve end-to-end pose prediction.

Keywords

• welding robot
• robot arm pose estimation
• machine vision
• vector mapping model
• oriented gradient
• deep neural network