Collaborative Localization for Multi-Robot ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Collaborative Localization for Multi-Robot System with Fault Detection and Exclusion based on the Kullback-Leibler Divergence
Author(s) :
Al Hage, Joelle [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
El Badaoui El Najjar, Maan [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Pomorski, Denis [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
El Badaoui El Najjar, Maan [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Pomorski, Denis [Auteur]

Journal title :
Journal of Intelligent and Robotic Systems
Publisher :
Springer Verlag
Publication date :
2016-12-03
ISSN :
0921-0296
English keyword(s) :
Collaborative localization
Fault detection and exclusion
Kullback-Leiber Divergence
Information Filter
ROC curve
Thresholding
Fault detection and exclusion
Kullback-Leiber Divergence
Information Filter
ROC curve
Thresholding
HAL domain(s) :
Sciences de l'ingénieur [physics]/Automatique / Robotique
English abstract : [en]
Multi-robot system attracted attention in various applications in order to replace the human operators. To achieve the intended goal, one of the main challenges of this system is to ensure the integrity of localization by ...
Show more >Multi-robot system attracted attention in various applications in order to replace the human operators. To achieve the intended goal, one of the main challenges of this system is to ensure the integrity of localization by adding a sensor fault diagnosis step to the localization task. In this paper, we present a framework able, in addition of localizing a group of robots, to detect and exclude the faulty sensors from the group with an optimized thresholding method. The estimator has the informational form of the Kalman Filter (KF) namely Information Filter (IF). A residual test based on the Kullback-Leibler divergence (KLD) between the predicted and the corrected distributions of the IF is developed. It is generated from two tests: the first acts on the means and the second deals with the covariance matrices. Thresholding using entropy based criterion and Receiver Operating Characteristics (ROC) curve are discussed. Finally, the validation of this framework is studied on real experimental data from a group of robots.Show less >
Show more >Multi-robot system attracted attention in various applications in order to replace the human operators. To achieve the intended goal, one of the main challenges of this system is to ensure the integrity of localization by adding a sensor fault diagnosis step to the localization task. In this paper, we present a framework able, in addition of localizing a group of robots, to detect and exclude the faulty sensors from the group with an optimized thresholding method. The estimator has the informational form of the Kalman Filter (KF) namely Information Filter (IF). A residual test based on the Kullback-Leibler divergence (KLD) between the predicted and the corrected distributions of the IF is developed. It is generated from two tests: the first acts on the means and the second deals with the covariance matrices. Thresholding using entropy based criterion and Receiver Operating Characteristics (ROC) curve are discussed. Finally, the validation of this framework is studied on real experimental data from a group of robots.Show less >
Language :
Anglais
Popular science :
Non
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