[en] Robots traditionally have a wide array of sensors that allow them to react to the environment and make appropriate decisions. These sensors can give incorrect or imprecise data due to malfunctioning or noise. Sensor fusion methods try to overcome some of these issues by using the data coming from different sensors and combining it. However, they often don’t take sensor malfunctioning and a priori knowledge about the sensors and the environment into account, which can produce conflicting information for the robot to work with. In this paper, we present an architecture and process in order to overcome some of these limitations based on a proactive rule-based system.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
NEYENS, Gilles ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
ZAMPUNIERIS, Denis ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Proactive Middleware for Fault Detection and Advanced Conflict Handling in Sensor Fusion
Date de publication/diffusion :
2019
Nom de la manifestation :
18th International Conference, Artificial Intelligence and Soft Computing (ICAISC) 2019 Zakopane, Poland, June 16–20, 2019
Organisateur de la manifestation :
IEEE Poland Section Computational Intelligence Society Chapter
Lieu de la manifestation :
Zakopane, Pologne
Date de la manifestation :
16 – 20 June, 2019
Manifestation à portée :
International
Titre de l'ouvrage principal :
Proceedings of the 18th International Conference, ICAISC 2019 Zakopane, Poland, June 16–20, 2019, Part I & II
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