Designing a framework to give perception capabilities to an industrial robot for waste separation tasks

El objetivo principal de esta investigación es realizar una primera aproximación para analizar la tarea de preclasificación de residuos de botellas plásticas en Bogotá, que se realiza en el Centro de recolección de La Alquería, uno de los pocos centros de recolección que forman parte de la Unidad Administrativa Especial De Servicios públicos (UASP) y proponer su automatización en una estación de trabajo de coexistencia humano-robot. El proceso es simulado en el Centro Tecnológico de Automatización Industrial (CTAI) de la Universidad Javeriana. El banco de pruebas consiste en una estación de trabajo de preclasificación emulada en la que se clasifican tres tipos de materiales reciclables termoplásticos de los cuales se obtiene su estimación de posición en 3D utilizando el sensor de profundidad IR (infrarrojo) del kinect. Además, el robot Motoman de doble brazo SDA10F equipado con una pinza Robotiq se utiliza para realizar la tarea de Pick and Place, llevando las botellas al contenedor apropiado. Para distribuir el sistema diseñado, se usan dos computadoras portátiles; uno a cargo de ejecutar el algoritmo de procesamiento de imágenes, y otro a cargo de la simulación del entorno y del control del robot.The main purpose of this research is to make a first approach to analyze the waste preclassification task of plastic bottles in Bogota ,that occurs at the Centro de recoleccion La Alqueria one of the few collection centers that are part of the Unidad Administrativa Especial De Servicios Publicos (UASP) and propose its automation in an emulated human-robot co-existence workstation. The process is simulated at the Centro Tecnologico de Automatizacion Industrial (CTAI) from PUJ. The testbed consists on an emulated pre-classification workstation where three types of thermoplastic recyclable materials, are classified and their 3D pose estimation is obtained using the IR (infrared) depth sensor of the Microsoft kinect. Additionally, the SDA10F dual-arm Motoman robot equipped with a Robotiq gripper is used to perform the picking and place task, carrying the bottles to the appropriate bin. To distribute the designed system two laptops are used; one in charge of running the image processing algorithm, and another one in charge of the environment simulation and of the control of the robot.Ingeniero (a) IndustrialPregrad

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications