Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO

New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator) from the University Carlos III of Madrid.The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I + D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Basic principles for the development of an application to bi-manipulate boxes with a humanoid robot

[EN] Logistics is a sector which is continuously growing, due to globalization, as well as the current situation caused by the Covid. In this article, an application to recognize boxes is described. The characteristics are extracted with the goal of identify the opening side of the box by using computer vision techniques. This goal has been achieved considering the dimensions, as well as, the position in the space of the box. Those characteristics were obtained processing 2D and 3D images. Then, this information has been classified by using a decision tree based on the human knowledge. The probability of each of the six faces to be the opening side is obtained. This article is a base to develop in the future an application in which the humanoid robot TEO is capable to learn the optimal way to find the opening of boxes and bimanipulate them to be opened in an automated system.[ES] La logística es un sector que está en continuo crecimiento, debido tanto a la globalización, como a la actual situación creada por el Covid. En este artículo se describe una aplicación para reconocer cajas, extrayendo sus características con el fin de identificar la cara de apertura por medio de un sistema de visión por computador. Este objetivo se ha conseguido teniendo en cuenta las dimensiones y la posición en el espacio de la misma, logrando estas características a través de técnicas de procesamiento de imagen en 2D y en 3D. Posteriormente, la información correspondiente a las caras de la caja es clasificada con un árbol de decisiones, obteniendo así la probabilidad de que cada una de las seis caras sea la de apertura. Este artículo sirve para establecer las bases para desarrollar en un futuro una aplicación en la que el robot humanoide TEO mediante aprendizaje encuentre la forma más óptima de bimanipular cajas y abrirlas, integrando este conocimiento en un sistema automatizado.Este trabajo ha sido realizado parcialmente gracias al apoyo de RoboCity2030-DIH-CM; Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, fundado por “Programas de Actividades I+D en la Comunidad de Madrid” y cofundado por los fondos estructurales de la UE.Hernandez-Vicen, J.; Martinez, S.; Balaguer, C. (2021). Principios básicos para el desarrollo de una aplicación de bi-manipulación de cajas por un robot humanoide. Revista Iberoamericana de Automática e Informática industrial. 18(2):129-137. https://doi.org/10.4995/riai.2020.13097OJS129137182Galeano, S. 2019. El ecommerce español se quedó a las puertas de la barrera psicológica de los 40.000 millones de euros en 2018. 2019, de marketing4ecommerce Sitio web: https://marketing4ecommerce.net/facturacion-anual-ecommerce-espanol-no-supera-40-000-mme-cnmc-2018/World Bank Group, 2019. Country Score Card: Spain 2018. Recuperado el 12/2019, de World Bank Group Sitio web: https://lpi.worldbank.org/international/scorecard/line/254/C/ESP/2018/C/ESP/2016/C/ESP/2014/C/ESP/2012/C/ESP/2010/C/ESP/2007#chartareaGarcía Juez, I. 2017. La logística y el transporte en España suponen el 8% del PIB y y emplean a 800.000 personas. Ok Diario. Recuperado el 12/2019 de https://okdiario.com/economia/sector-logistica-transporte-espana-supone-8-del-pib-emplea-800-000-personas-1368706Echelmeyer, W., Kirchheim, A., & Wellbrock, E. (2008, September). Robotics-logistics: Challenges for automation of logistic processes. In 2008 IEEE International Conference on Automation and Logistics (pp. 2099-2103). IEEE. https://doi.org/10.1109/ICAL.2008.4636510Martínez, S., Monje, C. A., Jardón, A., Pierro, P., Balaguer, C., & Munoz, D. (2012). Teo: Full-size humanoid robot design powered by a fuel cell system. Cybernetics and Systems, 43(3), 163-180. https://doi.org/10.1080/01969722.2012.659977C. A. Monje, S. Martínez, A. Jardón, P. Pierro, C. Balaguer and D. Muñoz, "Full-size humanoid robot TEO: Design attending mechanical robustness and energy consumption," 2011 11th IEEE-RAS International Conference on Humanoid Robots, Bled, 2011, pp. 325-330. https://doi.org/10.1109/Humanoids.2011.6100835Hernandez-Vicen, J., Martinez, S., Garcia-Haro, J., & Balaguer, C. (2018). Correction of visual perception based on neuro-fuzzy learning for the humanoid robot TEO. Sensors, 18(4), 972. https://doi.org/10.3390/s18040972Vázquez, E. (2015). Técnicas de visión artificial robustas en entornos no controlados. Tesis doctoral, Universidad de Vigo, Vigo, España. Recuperado el 13 de Enero de 2020, de https://dialnet.unirioja.es/servlet/dctes?codigo=124604Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. " O'Reilly Media, Inc.".Bradski, G., & Kaehler, A. (2000). OpenCV. Dr. Dobb's journal of software tools, 3.Brahmbhatt, S. (2013). Practical OpenCV. Apress. https://doi.org/10.1007/978-1-4302-6080-6Elfring, J. (2013). Image Processing Using OpenCV. Online (Feb 2018).OpenCV: Hough Line Transform. (2020). Recuperado 20 Enero 2020, de https://docs.opencv.org/3.4/d9/db0/tutorial_hough_lines.htmlde la Escalera, A., Armingol, J. M., Pech, J. L., & Gómez, J. J. (2010). Detección Automática de un Patrón para la Calibración de Cámaras. Revista Iberoamericana de Automática e Informática Industrial RIAI, 7(4), 83-94. https://doi.org/10.1016/S1697-7912(10)70063-7Wang, Y. M., Li, Y., & Zheng, J. B. (2010, June). A camera calibration technique based on OpenCV. In The 3rd International Conference on Information Sciences and Interaction Sciences (pp. 403-406). IEEE. https://doi.org/10.1109/ICICIS.2010.5534797Tesseract-ocr/tesseract. (2020). Recuperado el 20 Enero 2020, de https://github.com/tesseract-ocr/tesseract.Shah, P., Karamchandani, S., Nadkar, T., Gulechha, N., Koli, K., & Lad, K. (2009, November). OCR-based chassis-number recognition using artificial neural networks. In 2009 IEEE International Conference on Vehicular Electronics and Safety (ICVES) (pp. 31-34). IEEE. https://doi.org/10.1109/ICVES.2009.5400240Millán, D. B., Boticario, J. G., & Viñuela, P. I. (2006). Aprendizaje automático. Sanz y Torres.Sempere, J. (2014). Aprendizaje de árboles de decisión. Universidad Politécnica de Valencia, Valencia.Breiman, L. (2017). Classification and regression trees. Routledge. https://doi.org/10.1201/9781315139470Rebollo, F. F., & Barrojo, D. (2009). Aprendizaje por Refuerzo. Aprendizaje Automático, Departamento de Informática, Escuela Politécnica Superior

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots

Over recent years, brain-computer interface (BCI) has emerged as an alternative communication system between the human brain and an output device. Deciphered intents, after detecting electrical signals from the human scalp, are translated into control commands used to operate external devices, computer displays and virtual objects in the real-time. BCI provides an augmentative communication by creating a muscle-free channel between the brain and the output devices, primarily for subjects having neuromotor disorders, or trauma to nervous system, notably spinal cord injuries (SCI), and subjects with unaffected sensorimotor functions but disarticulated or amputated residual limbs. This review identifies the potentials of electroencephalography (EEG) based BCI applications for locomotion and mobility rehabilitation. Patients could benefit from its advancements such as, wearable lower-limb (LL) exoskeletons, orthosis, prosthesis, wheelchairs, and assistive-robot devices. The EEG communication signals employed by the aforementioned applications that also provide feasibility for future development in the field are sensorimotor rhythms (SMR), event-related potentials (ERP) and visual evoked potentials (VEP). The review is an effort to progress the development of user's mental task related to LL for BCI reliability and confidence measures. As a novel contribution, the reviewed BCI control paradigms for wearable LL and assistive-robots are presented by a general control framework fitting in hierarchical layers. It reflects informatic interactions, between the user, the BCI operator, the shared controller, the robotic device and the environment. Each sub layer of the BCI operator is discussed in detail, highlighting the feature extraction, classification and execution methods employed by the various systems. All applications' key features and their interaction with the environment are reviewed for the EEG-based activity mode recognition, and presented in form of a table. It i

Humanoid robot control of complex postural tasks based on learning from demostration

Mención Internacional en el título de doctorThis thesis addresses the problem of planning and controlling complex tasks in a humanoid robot from a postural point of view. It is motivated by the growth of robotics in our current society, where simple robots are being integrated. Its objective is to make an advancement in the development of complex behaviors in humanoid robots, in order to allow them to share our environment in the future. The work presents different contributions in the areas of humanoid robot postural control, behavior planning, non-linear control, learning from demonstration and reinforcement learning. First, as an introduction of the thesis, a group of methods and mathematical formulations are presented, describing concepts such as humanoid robot modelling, generation of locomotion trajectories and generation of whole-body trajectories. Next, the process of human learning is studied in order to develop a novel method of postural task transference between a human and a robot. It uses the demonstrated action goal as a metrics of comparison, which is codified using the reward associated to the task execution. As an evolution of the previous study, this process is generalized to a set of sequential behaviors, which are executed by the robot based on human demonstrations. Afterwards, the execution of postural movements using a robust control approach is proposed. This method allows to control the desired trajectory even with mismatches in the robot model. Finally, an architecture that encompasses all methods of postural planning and control is presented. It is complemented by an environment recognition module that identifies the free space in order to perform path planning and generate safe movements for the robot. The experimental justification of this thesis was developed using the humanoid robot HOAP-3. Tasks such as walking, standing up from a chair, dancing or opening a door have been implemented using the techniques proposed in this work.Esta tesis aborda el problema de la planificación y control de tareas complejas de un robot humanoide desde el punto de vista postural. Viene motivada por el auge de la robótica en la sociedad actual, donde ya se están incorporando robots sencillos y su objetivo es avanzar en el desarrollo de comportamientos complejos en robots humanoides, para que en el futuro sean capaces de compartir nuestro entorno. El trabajo presenta diferentes contribuciones en las áreas de control postural de robots humanoides, planificación de comportamientos, control no lineal, aprendizaje por demostración y aprendizaje por refuerzo. En primer lugar se desarrollan un conjunto de métodos y formulaciones matemáticas sobre los que se sustenta la tesis, describiendo conceptos de modelado de robots humanoides, generación de trayectorias de locomoción y generación de trayectorias del cuerpo completo. A continuación se estudia el proceso de aprendizaje humano, para desarrollar un novedoso método de transferencia de una tarea postural de un humano a un robot, usando como métrica de comparación el objetivo de la acción demostrada, que es codificada a través del refuerzo asociado a la ejecución de dicha tarea. Como evolución del trabajo anterior, se generaliza este proceso para la realización de un conjunto de comportamientos secuenciales, que son de nuevo realizados por el robot basándose en las demostraciones de un ser humano. Seguidamente se estudia la ejecución de movimientos posturales utilizando un método de control robusto ante imprecisiones en el modelado del robot. Para analizar, se presenta una arquitectura que aglutina los métodos de planificación y el control postural desarrollados en los capítulos anteriores. Esto se complementa con un módulo de reconocimiento del entorno y extracción del espacio libre para poder planificar y generar movimientos seguros en dicho entorno. La justificación experimental de la tesis se ha desarrollado con el robot humanoide HOAP-3. En este robot se han implementado tareas como caminar, levantarse de una silla, bailar o abrir una puerta. Todo ello haciendo uso de las técnicas propuestas en este trabajo.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Manuel Ángel Armada Rodríguez.- Secretario: Luis Santiago Garrido Bullón.- Vocal: Sylvain Calino

Visual Neuroscience of Robotic Grasping

Supporting Informatio

Daftar Ebook Engineering Science Terbitan Springer Tahun 2018

Artikel ini memuat daftar judul ebook bidang ilmu teknik yang diterbitkan oleh Springer pada tahun 2018 yang dimiliki oleh Unand