Titre :

Decision Tree Based Diagnosis for Hybrid Model-Based/Data-Driven Fault Detection and Exclusion of a Decentralized Multi-Vehicle Cooperative Localization System

Auteur(s) :

El Mawas, Zaynab [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille
Cappelle, Cindy [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille
El Badaoui El Najjar, Maan [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille

Titre de la manifestation scientifique :

IFAC World Congress

Ville :

Yokohama

Pays :

Japon

Date de début de la manifestation scientifique :

2023-07-09

Discipline(s) HAL :

Sciences de l'ingénieur [physics]
Informatique [cs]

Résumé en anglais : [en]

Cooperative navigation systems are one of the main topics of interest in multi- robot systems emerging nowadays, where the question of safety remains a very critical one preventing the actual integration of the technology. ...
Lire la suite >Cooperative navigation systems are one of the main topics of interest in multi- robot systems emerging nowadays, where the question of safety remains a very critical one preventing the actual integration of the technology. In this article, a multi-sensor multi-vehicle Cooperative Positioning System (CPS) is presented, with a hybrid fault detection method under a decentralized architecture, and that is tolerant to simultaneous sensor faults. In order to detect and isolate faults, a set of fault sensitive residuals are generated based on the divergence of Jensen Shannon (DJS ) between the probability distributions predicted by the encoder based evolution model and the various observations obtained by sensors. Then, in order to detect a fault, a data-driven approach is applied, where the classification of faults is done by a pre-trained detection decision tree (D-DT) and isolation random forest (I-RF). The testing and evaluation of the approach is done on real data acquired by three Turtlebot3 equipped with wheel encoders (for odometry), a gyroscope (for the yaw angle) and a Marvelmind localization system (for the global position), and a ground truth is recorded using optitrack system.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Solution de localisation sûre et précise pour les véhicules autonomes circulant en milieu contraint – route / rail

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL