Servomoteur

Résumé

Un servomoteur (souvent abrégé en « servo », provenant du latin servus qui signifie « esclave ») est un moteur capable de maintenir une opposition à un effort statique et dont la position est vérifiée en continu et corrigée en fonction de la mesure. C'est donc un système asservi. Le servomoteur intègre dans un même boitier, la mécanique (moteur et engrenage), et l’électronique, pour la commande et l'asservissement du moteur. La position est définie avec une limite de débattement d’angle de , mais également disponible en rotation continue. Les servomoteurs sont répandus pour les vannes industrielles et le modélisme. Le terme servomoteur désigne également de manière abusive le préactionneur, la plupart du temps pneumatique (système membrane/ressort), plus rarement hydraulique ou électrique d'une vanne de régulation. Dans ce cas l'asservissement de position est toujours effectué par un organe distinct nommé positionneur. Définition Un servomoteur est un système motorisé

Source officielle

https://fr.wikipedia.org/wiki/Servomoteur

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This project was carried out at Overview Ltd., a London company which specialises in high precision video-surveillance equipment. Overview produces devices that include a camera driven by two stepper motors, and an image-processing unit. Magnetic encoders are used to detect external disturbances and failures. Overview is willing to develop an algorithm capable of detecting lost steps, and in the longer term, capable of giving precise position related information without using encoders.

2007

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Robots are increasingly used as scientific tools to investigate animal locomotion. However, designing a robot that properly emulates the kinematic and dynamic properties of an animal is difficult because of the complexity of musculoskeletal systems and the limitations of current robotics technology. Here we propose a design process that combines high-speed cineradiography, optimization, dynamic scaling, 3D printing, high-end servomotors, and a tailored dry-suit to construct Pleurobot: a salamander-like robot that closely mimics its biological counterpart, Pleurodeles waltl. Our previous robots helped us test and confirm hypotheses on the interaction between the locomotor neuronal networks of the limbs and the spine to generate basic swimming and walking gaits. With Pleurobot, we demonstrate a design process that will enable studies of richer motor skills in salamanders. In particular, we are interested in how these richer motor skills can be obtained by extending our spinal cord models with the addition of more descending pathways and more detailed limb central pattern generators (CPG) networks. Pleurobot is a dynamically-scaled amphibious salamander robot with a large number of actuated degrees of freedom (27 in total). Because of our design process, the robot can capture most of the animal’s degrees of freedom and range of motion, especially at the limbs. We demonstrate the robot’s abilities by imposing raw kinematic data, extracted from X-ray videos, to the robot’s joints for basic locomotor behaviors in water and on land. The robot closely matches the behavior of the animal in terms of relative forward speeds and lateral displacements. Ground reaction forces during walking also resemble those of the animal. Based on our results we anticipate that future studies on richer motor skills in salamanders will highly benefit from Pleurobot’s design.

Royal Soc2016

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Augmented-SVM: Automatic space partitioning for combining multiple non-linear dynamics

Ashwini Shukla

Non-linear dynamical systems (DS) have been used extensively for building generative models of human behavior. Their applications range from modeling brain dynamics to encoding motor commands. Many schemes have been proposed for encoding robot motions using dynamical systems with a single attractor placed at a predefined target in state space. Although these enable the robots to react against sudden perturbations without any re-planning, the motions are always directed towards a single target. In this work, we focus on combining several such DS with distinct attractors, resulting in a multi-stable DS. We show its applicability in reach-to-grasp tasks where the attractors represent several grasping points on the target object. While exploiting multiple attractors provides more flexibility in recovering from unseen perturbations, it also increases the complexity of the underlying learning problem. Here we present the \emph{Augmented-SVM} (A-SVM) model which inherits region partitioning ability of the well known SVM classifier and is augmented with novel constraints derived from the individual DS. The new constraints modify the original SVM dual whose optimal solution then results in a new class of support vectors (SV). These new SV ensure that the resulting multi-stable DS incurs minimum deviation from the original dynamics and is stable at each of the attractors within a finite region of attraction. We show, via implementations on a simulated 10 degrees of freedom mobile robotic platform, that the model is capable of real-time motion generation and is able to adapt on-the-fly to perturbations.

Curran Associates, Inc.2012

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