Robotic hex-nut sorting system with deep learning

Abstract

<span>This article exposes the design and implementation of an automation system based on a robotic arm for hex-nut classification, using pattern recognition and image processing. The robotic arm work based on three servo motors and an electromagnetic end effector. The pattern recognition implemented allows classifying three different types of hex-nut through deep learning algorithms based on convolutional neural network architectures. The proposed methodology exposes four phases: the first is the design, implementation, and control of a robotic arm. The second is the capture, classification, and image treatment; the third allows gripping the nut through the robot’s inverse kinematic. The final phase is the re-localization of the hex-nut in the respective container. The automation system successfully classifies all the types of hex-nuts, where the convolutional network used is an efficient and recent pattern recognition method, with an accuracy of 100% in 150 iterations. This development allows for obtaining a novel algorithm for robotic applications in hex-nut sorting.</span>

Multifunctional Robot Grasping System Based on Deep Learning and Image Processing

“…In recent years, with the increase in GPU computing power and the rapid development of deep learning technol-ogy, deep neural networks driven by big data can learn the deep features corresponding to things or behaviours, especially for feature learning and expression of twodimensional data, such as in image classification, object detection, semantic segmentation, and behavioural recognition research fields; the effect achieved by convolutional neural networks has far surpassed the traditional detection algorithms and even surpass human recognition in some areas [4]. The introduction of deep learning algorithms in robot grasping research, where convolutional neural networks are used to extract feature information of grasping poses in a hierarchical manner, can solve important challenges that previously required human-designed grasping features [5].…”

Some Features of Classifiers Implementation for Object Recognition in Specialized Computer systems

“…With regard to object recognition, directly in field of robotics, then, pattern recognition and image processing is presented in [6]. Here, authors focused on three different types of objects -three types of hexagons that are classified using deep learning algorithms based on convolutional neural network architectures.…”

“…The work deals with a robotic arm, which can be controlled via Telegram application, and also provides ability to work from an Android or IOS cell phone. The method proposed in [6] includes four stages: the first is design, implementation, and control of robotic arm; the second is capture, classification and processing of images; the third allows nut to be clamped through back of robot. Kinematic; the fourth step is changeover of hex nut to appropriate container.…”