Development of robotic bin picking platform with cluttered objects using human guidance and convolutional neural network (CNN)

Abstract

Industrial robots have been utilized for factory automation due to their high repeatability. Along with the development of visual servo and machine learning techniques, various vision-based autonomous pick and place methods have been presented. However, unlike parts in a bin handled in manufacturing environment, the technique was studied in less cluttered environment and huge dataset for the training. This research suggests a robot bin picking platform that uses an initial Convolutional Neural Network (CNN) model trained by human data, then increases the accuracy of the model by continuously training by the robot itself. In the human part, a user determines the pickable or non-pickable parts from depth image obtained by a lidar sensor and referenced 3D partial point cloud of a block for Iterative Closest Point (ICP) algorithm is generated. Next, the autonomous part generates a CNN model with the initial human data, tries to perform pick operation autonomously, and repeats training of the CNN model through the collected data by itself. Through the experiments, 74% success rate, which appeared only with initial human data, increased up to 87% with 2000 dataset. This platform is expected to build an autonomous robotic bin picking system without CAD models and less efforts to prepare the labeled dataset for training deep learning models.