Commande des robots

Résumé

La commande des robots est un système qui contribue au mouvement des robots. Elle implique les aspects mécaniques et les systèmes programmables. En se basant sur le modèle du robot élaboré avec la modélisation des robots, il s'agit de commander électriquement les actionneurs du robot afin d'obtenir le mouvement requis : lui faire rejoindre une position ou lui faire suivre une trajectoire. Notes et références Voir aussi

Commande prédictive
Régulateur PID

Catégorie:Robotique

Source officielle

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

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vignette|redresse=0.8|Les assistants personnels intelligents sont l'une des applications concrètes de l'intelligence artificielle dans les années 2010. L'intelligence artificielle (IA) est un ensembl

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Publications associées (29)

Demo: Pointing Gestures for Proximity Interaction

We demonstrate a system to control robots in the users proximity with pointing gestures-a natural device that people use all the time to communicate with each other. Our setup consists of a miniature quadrotor Crazyflie 2.0, a wearable inertial measurement unit MetaWearR+ mounted on the user's wrist, and a laptop as the ground control station. The video of this demo is available at https://youtu.be/yafy-HZMk_U [1].

2019

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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

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Co-evolution of Morphology and Control of Virtual Legged Robots for the Steering Task

Auke Ijspeert, Ken Larpin, Soha Pouya, Jesse van den Kieboom

Co-evolution of morphology and control is a powerful approach in robotics to study performance on a particular task, considering mechanical structure and control as a whole to accomplish a certain goal. Co-evolutionary methods have been used to study the effect of these two fundamental components, in particular when applied to locomotion and reaching tasks. The resulting robots are usually evolved in simulation for a particular static task, making it difficult to transfer the results to a real robotic system, where a certain controllability (beyond the scope of the particular task) is desired. In this work, we are interested in evolving a robot with an externally controllable behavior, in particular considering locomotion tasks. We investigate to what extent co-evolution can outperform the more traditional approach of evolving only the robot control. The locomotion behavior is evolved for a simulated quadruped robot, using a multi-objective evolutionary algorithm, considering speed and energy efficiency as the main objectives. Additionally, we introduce a third objective where we provide the robot with an external command indicating the robot locomotion direction, allowing us to steer the robot once it is evolved. The resulting quadrupedal robots show improved performance on the steering task when compared to robots where only control is evolved.

2011

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