Efficient 2D Detection and Positioning of Complex Objects for Robotic Manipulation Using Fully Convolutional Neural Network

Abstract

Programming industrial robots in a real-life environment is a significant task necessary to be dealt with in modern facilities. The "pick up and place" task is undeniably one of the regular robot programming problems which needs to be solved. At the beginning of the "pick and place" task, the position determination and exact detection of the objects for picking must be performed. In this paper, an advanced approach to the detection and positioning of various objects is introduced. The approach is based on two consecutive steps. Firstly, the captured scene, containing attentive objects, is transformed using a segmentation neural network. The output of the segmentation process is a schematic image in which the types and positions of objects are represented by gradient circles of various colors. Secondly, these particular circle positions are determined by finding the local maxima in the schematic image. The proposed approach is tested on a complex detection and positioning problem by evaluation of total accuracy.