Comparison of Deep Learning Models in Position Based Visual Servoing

Abstract

In this paper a Position Based Visual Servoing (PBVS) algorithm using deep neural network is applied to a UR10 cobot. A pre-trained Convolutional Neural Network (CNN) will be re-purposed and fine-tuned in an offline stage. In order implement and validate the CNN for a visual servoing application, a dataset was created in a simulated environment by moving a simulated UR10 robot to various positions and capturing an image with the corresponding relative pose to the target object. The obtained dataset was validated with ground truth dataset collected using the real robot. To control the motion of the cobot (simulated/real) a meta-operating system and a vision based control law was designed in ROS. The visual servoing task is defined as a repositioning task whereby the performance of the visual servoing is evaluated by the convergence to one desired pose from various, arbitrarily selected starting poses. Three different network architectures were implemented and tested. The obtained results reveal that all network architecture can be successfully applied to visual servoing systems.