Reinforcement and deep learning-based optimization of pose estimation techniques in single and multi-agent systems : application to aerial and mobile robots [document électronique] /
Islem Kobbi, Auteur ;
Abdelhak Benamirouche, Auteur ;
Mohamed Tadjine, Directeur de thèse . -
[S.l.] : [s.n.], 2023 . - 1 fichier PDF (18.8 Mo) : ill.
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Automatique : Alger, École Nationale Polytechnique : 2023
Bibliogr. P. 143 - 145. - Annexes P. 146 - 150.
Langues : Anglais (
eng)
| Mots-clés : |
Deep Pose estimation
Multi-agent system
Reinforcement Learning |
| Index. décimale : |
PA01023 |
| 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. |
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