Manipulator (device)

Résumé

In robotics, a manipulator is a device used to manipulate materials without direct physical contact by the operator. The applications were originally for dealing with radioactive or biohazardous materials, using robotic arms, or they were used in inaccessible places. In more recent developments they have been used in diverse range of applications including welding automation, robotic surgery and in space. It is an arm-like mechanism that consists of a series of segments, usually sliding or jointed called cross-slides, which grasp and move objects with a number of degrees of freedom. In industrial ergonomics a manipulator is a lift-assist device used to help workers lift, maneuver and place articles in process that are too heavy, too hot, too large or otherwise too difficult for a single worker to manually handle. As opposed to simply vertical lift assists (cranes, hoists, etc.) manipulators have the ability to reach in to tight spaces and remove workpieces. A good example would

Source officielle

https://en.wikipedia.org/wiki/Manipulator_(device)

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (13)

Publications associées

Chargement

Personnes associées (1)

Personnes associées

Chargement

Unités associées (1)

Unités associées

Chargement

Concepts associés (5)

Concepts associés

Chargement

Cours associés (1)

This course is a real contact with industrial robotic applications. Components and mechanisms are reminded. The fields of microtechnical assembly and packaging are treated. CTOs from established companies (BlueBotics, Adept, Maxon motors and UniTechnologies) are involved in this course.

Cours associés

Chargement

Séances de cours associées (1)

Séances de cours associées

Chargement

Cinegen: a Rapid Prototyping Tool for Robot Manipulators

Charles Baur, Reymond Clavel, Roland Siegwart

1998

Chargement

Efficient Configuration Exploration in Inverse Dynamics Acquisition of Robotic Manipulators

Walid Amanhoud, Aude Billard, Sthithpragya Gupta, Farshad Khadivar

The inverse dynamics of a robotic manipulator is instrumental in precise robot control and manipulation. However, acquiring such a model is challenging, not only due to unmodelled non-linearities such as joint friction, but also from a machine learning perspective (e.g., input space dimension, amount of data needed). The accuracy of such models, regardless of the learning techniques, relies on proper excitation and exploration of the robot's configuration space, in order to collect a rich dataset. This study aims to provide rich data in learning the inverse dynamics of a serial robotic manipulator using supervised machine learning techniques. We propose a method, called Max-Information Configuration Exploration (MICE), to incrementally explore and generate information-rich data via computing parameters of a trajectory set. We also introduce a new set of excitation trajectories that explores robot's configuration through imposed stable limit cycles in robot joints' phase space while satisfying feasibility constraints and physical bounds. We benchmark MICE against state-of-the-art in terms of data quality and learning accuracy. The proposed methodology for data collection, model learning, and evaluation, is validated with a KUKA IIWA14 robotic arm where the results prove significant improvement over traditional approaches.

IEEE2021

Chargement

Implicit Distance Functions: Learning and Applications in Control

Aude Billard, Nadia Barbara Figueroa Fernandez, Mikhail Koptev

This paper describes a novel approach to learn an implicit, differentiable distance function for arbitrary configurations of a robotic manipulator used for reactive control. By exploiting GPU processing, we efficiently query the learned collision representation and obtain an implicit distance between the robot and the environment. The differentiable nature of the learned function allows for calculating valid gradients wrt. any robot configuration, providing a repulsive vector field in joint space that can be injected in various control methods to improve collision avoidance. We present preliminary results on solving collision avoidance for a 7DoF robot with a reactive inverse kinematics solution, as well as improving performance of a sampling-based model-predictive controller.

2022

Chargement