Humanoid robot NAO: Review of control and motion exploration

Shamsuddin, Syamimi; Ismail, Luthffi Idzhar; Yussof, Hanafiah; Zahari, Nur Ismarrubie; Bahari, Saiful; Hashim, Haslina; Jaffar, Ahmed

doi:10.1109/iccsce.2011.6190579

Cited by 87 publications

(38 citation statements)

References 10 publications

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“…Autonomous recovery after a fall is thus highly desirable. One possibility is to represent recovery behaviors as well-tuned joint trajectories that can simply be replayed: an approach that has been taken in some commercial systems [47]. Such trajectories have required laborious manual tuning.…”

Section: Recovery From a Fallmentioning

confidence: 99%

Learning agile and dynamic motor skills for legged robots

et al. 2019

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Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. However, so far, reinforcement learning research for legged robots is mainly limited to simulation, and only few and comparably simple examples have been deployed on real systems. The primary reason is that training with real robots, particularly with dynamically balancing systems, is complicated and expensive. In the present work, we report a new method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby we leverage fast, automated, and cost-effective data generation schemes. The approach is applied to the ANYmal robot, a sophisticated medium-dog-sized quadrupedal system. Using policies trained in simulation, the quadrupedal machine achieves locomotion skills that go beyond what had been achieved with prior methods: ANYmal is capable of precisely and energy-efficiently following high-level body velocity commands, running faster than ever before, and recovering from falling even in complex configurations.

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Section: Recovery From a Fallmentioning

confidence: 99%

Learning agile and dynamic motor skills for legged robots

et al. 2019

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“…(3), (4), (5), (6), (7) and "GUI for Denavit and Hartenberg" is possible to make an analysis in a simulation process.…”

Section: Analysis and Simulationsmentioning

confidence: 99%

“…The NAO is the most complete, programmable and autonomous robot. This ability is possible through it has 25 motors, 2 cameras, touch sensors and microphones [7].…”

mentioning

confidence: 99%

Study and design of biped robot of ten DOF for stability analysis in walking process

Cordova-Villarreal

Montesdeoca

Avila-Campoverde

et al. 2015

2015 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)

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In the world, the human robots being a important part in the relationship between human-machine. In this context, a study and design to implementation of a biped robot that is focused in a mathematical modeling and analysis of direct and kinematic, using matrix techniques proposed by Denavit and Hartenberg (DH). In the first part only legs are implemented using digital servos motors without feedback signals. Is necessary to use a high performance driver to control of biped robot, in this case an Arduino DUE are used for this propose. The results show a high performance of mathematical modeling when the walking sUrface is flat, but when the walking surface change the performance is no good.

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“…La plataforma NAO, utilizada en este proyecto, ha sido usada en visual servoing [9], fútbol robótico [10], aprendizaje de movimientos por imitación [11], navegación en ambientes [12], técnicas de caminata bípeda [13], además en estudios sobre la interacción humanorobot, donde el sistema de visión permite la grabación de la actividad, y la detección de rostros, movimientos de asentimiento y negación con la cabeza [14], [15], [16], entre otras.…”

Section: Plataforma Robóticaunclassified

Screw and wrench orientation estimation using the artificial vision in the NAO humanoid robot

Pena

Hoyos

Prieto

et al. 2013

2013 II International Congress of Engineering Mechatronics and Automation (CIIMA)

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Resumen-Industrial robots are having major involvement in assembly lines, where they are able to identify, classify and even grasp objects and place them in non-default locations; furthermore, assembling pieces autonomously. We describe step by step the image processing to determine the objects orientation located in the field of view of the NAO robot, initial step in posterior developments in task execution using those objects. The processing stages described are: RGB color space segmentation, interest points detection and objects orientation estimation with respect to a reference frame. The results are compared with measures obtained using a commercial image processing software. The absolute error is less than 1.5 o in the objects orientation angles, which is considered acceptable due to the size of the processed objects. The introduced methodology is a first step in the execution of assembling tasks or handling of tools by a robotic platform, where the artificial vision has a relevant place in the identification, location and orientation of the objects.Index Terms-Image processing, objects detection, image segmentation, humanoid robots, robotic vision systems I. INTRODUCCIÓNLa programación por demostración (PbD por sus siglas en inglés) es una técnica donde, al contrario de la programación detallada que convencionalmente se hace al robot, este aprende movimientos parciales o incluso una tarea completa, con sólo ver una o varias demostraciones por parte de un humano.1 Docente Universidad del Quindío Entre las tareas que se espera desarrollen los robots, es común que se requiera la interacción con herramientas para llevar a cabo una determinada acción. El manejo de herramientas puede dividirse en: reconocimiento, agarre y posicionado, y reproducción de los movimientos para realizar la tarea. La plataforma utilizada, denominada NAO [1], es un robot humanoide programable de 58 centímetros de alto desarrollado por la compañía francesa Aldebaran. Entre sus principales características se encuentran: un cuerpo con 25 grados de libertad, una red de sensores, incluyendo dos cámaras, 4 micrófonos, sensor de distancia basado en ultrasonido, dos emisores y receptores infrarrojos, sensor de inercia, 9 sensores táctiles, y 8 sensores de presión. El autómata contiene una CPU Intel ATOM a 1,6 GHz que ejecuta un kernel Linux soportando el middleware propietario de Aldebaran (NAOqi). En este proyecto los programas se escribieron en lenguaje C++, accediendo desde un portátil a la cámara del robot y obteniendo el video en tiempo real.El trabajo presentado hace parte de un proyecto que pretende desarrollar técnicas que integren visión, programación por demostración y medida de fuerzas para realizar la tarea de apretar una tuerca. El robot, a partir de la demostración, aprende el movimiento de apretar, con la visión se logra flexibilidad en la tarea demostrada ya que permite variaciones de posición y orientación de la tuerca, con lo que se puede alinear la llave con la cabeza del tornillo, porúltimo se hace una medida de la corrient...

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Humanoid robot NAO: Review of control and motion exploration

Cited by 87 publications

References 10 publications

Learning agile and dynamic motor skills for legged robots

Learning agile and dynamic motor skills for legged robots

Study and design of biped robot of ten DOF for stability analysis in walking process

Screw and wrench orientation estimation using the artificial vision in the NAO humanoid robot

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