Estimation and Anticipation of Dynamic Parameters

il y a 2 semaines


Metz, France CNRS Temps plein

Cette offre est disponible dans les langues suivantes:

- Français
- Anglais

Date Limite Candidature : mercredi 5 juin 2024

**Informations générales**:
**Intitulé de l'offre **:Doctorant en Estimation et Anticipation des paramètres dynamiques d'un robot mobile par des sensors exteroceptifs H/F**
Référence : IRL2958-CRICOR-011
Nombre de Postes : 1
Lieu de travail : METZ
Date de publication : mercredi 15 mai 2024
Type de contrat : CDD Doctorant/Contrat doctoral
Durée du contrat : 36 mois
Date de début de la thèse : 16 septembre 2024
Quotité de travail : Temps complet
Rémunération : La rémunération est d'un minimum de 2135,00 € mensuel
Section(s) CN : Interactions, particules, noyaux du laboratoire au cosmos

**Description du sujet de thèse**:
Cette thèse de doctorat sera menée dans le cadre du projet Muscaa, un projet ANR axé sur le comportement adaptatif des robots mobiles en environnements agricoles. L'objectif de Muscaa est de lier des paramètres gouvernant la dynamique du robot (comme l'adhérence, les vibrations) qui peuvent être observés mais pas prédits, à des éléments de perception de haut niveau (comme la texture de l'image, l'intensité du laser) afin d'anticiper une variation du comportement du robot. Dans ce projet, l'IRL 2958 GT-CNRS est chargée de lier directement les données de perception à la prédiction des paramètres de mouvement à l'aide de réseaux neuronaux profonds. Dans ce contexte, cette thèse abordera trois problèmes d'apprentissage automatique en utilisant des données extéroceptives (Lidar 3D, imagerie) : 1- Classification : reconnaissance des classes de terrain associées à certains paramètres dynamiques pré-identifiés. 2- Régression : prédiction directe des paramètres dynamiques. 3- Identification de système : prédiction directe de la dynamique du véhicule.

**Contexte de travail**:
Cette these sera réalisée dans le contexte de l'IRL 2958 GT-CNRS, un laboratoire commun entre le Georgia Institute of Technology et le CNRS, sous la direction du prof. C. Pradalier, spécialiste en traitement d'image, robotique et machine learning. Comme tout projet ANR, ce projet est une collaboration avec un ensemble de partenaire nationaux venant de domaines différents (INRAE, Institut Pascal...) et demandera donc une certaine quantité de travail collaboratif et de communication. Des déplacements seront a prévoir pour des essais sur le terrain (en France).

Le poste se situe dans un secteur relevant de la protection du potentiel scientifique et technique (PPST), et nécessite donc, conformément à la réglementation, que votre arrivée soit autorisée par l'autorité compétente du MESR.

**Contraintes et risques**:
Travail dans un environnement bilingue, nécessité de pouvoir participer à des essais sur le terrain.
Travail en informatique et machine learning : une partie importante du temps de travail sera faite sur écran, et une partie de la thèse demandera de passer du temps à labeliser des données manuellement.



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