An acceleration-level visual servoing scheme for robot manipulator with IoT and sensors using recurrent neural network

Abstract

With the rapid evolution of Internet of Things (IoT) and sensor technology, robot manipulators are able to acquire a great deal of necessary information to fulfill various tricky tasks. Based on the recurrent neural network method, this paper investigates the robot manipulator with visual sensors for the image-based visual servoing (IBVS) control at the acceleration level. In consideration of multiple-level joint constraints, the newly proposed acceleration-level IBVS scheme can drive the corresponding feature of the end-effector to the desired fixed feature with theoretical convergence proved. In order to verify the correctness of the theoretical analyses, simulations are conducted on different robot manipulators for the visual servoing task with excellent performance. In addition, comparison results between the proposed acceleration-level IBVS scheme and the existing method highlight the efficiency and the superiority of the proposed method. In short, the proposed method extends the application scenarios of the traditional visual servoing schemes by using acceleration commands, which also improves the safety of the visual servoing control of the redundant robot manipulator.