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Titre: Reinforcement and deep learning-based optimization of pose estimation techniques in single and multi-agent systems : application to aerial and mobile robots
Auteur(s): Kobbi, Islem
Benamirouche, Abdelhak
Tadjine, Mohamed, Directeur de thèse
Mots-clés: Reinforcement Learning
Deep Pose estimation
Multi-agent system
Date de publication: 2023
Résumé: In this work, we will focus on optimizing pose estimation techniques for aerial and mobile robots in both single-agent and multi-agent systems. Novel approaches based on Deep Learning and Reinforcement Learning will be proposed to enhance accuracy and robustness. The thesis includes a comprehensive literature review, introducing software tools used in the research. Two approaches for single agent pose estimation, will be presented : the QR estimator for an adaptive version of the Extended Kalman Filter and the KalmanNet approach for a direct estimation of the Filter gain. The effectiveness of these approaches will be demonstrated through simulations. The investigation will then be extended to collaborative pose estimation in multi-agent systems. A novel approach will be also proposed which aims to improve accuracy and robustness by leveraging information from neighboring agents. The findings will be validated in a real-world simulation environment using ROS and Gazebo.
Description: Mémoire de Projet de Fin d’Études : Automatique : Alger, École Nationale Polytechnique : 2023
Collection(s) :Département Automatique

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