Rapid 2D Positioning of Multiple Complex Objects for Pick and Place Application Using Convolutional Neural Network

Abstract

Robot guidance in an industrial environment is an important task to be solved in modern production facilities. A pick and place task is definitely one of the most common robot guidance issues to solve. In the beginning of the pick and place task, we need to perform a precise positioning of the objects of interest. In this contribution, an innovative engineering approach to multiple object positioning is proposed. The approach consists of two consecutive steps. At first, the original scene with objects of interest is transformed using a neural network. The output of this transformation is a schematic image, which represents the positions of the objects with gradient circles of various colors. Then, the positions of the gradient circles are determined by finding local maxima in the transformed image. The proposed approach is tested on a legitimate positioning problem with more than 99.8 % accuracy.