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Titre: | Deep Reinforcement Learning based mapless navigation and control of mobile robots. |
Autre(s) titre(s): | Navigation autonome sans carte basée sur l’Apprentissage par Renforcement Profond et commande des robots mobiles |
Auteur(s): | Raibia, Khalil, Khelfaoui, Abdelkader Achour, Hakim, Directeur de thèse |
Mots-clés: | mapless navigation Deep Reinforcement Learning artificial neural networks fuzzy T-S controller trajectory tracking mobile robots |
Date de publication: | 2025 |
Résumé: | This thesis presents a pipeline for mapless navigation of mobile robots, where decision- making and control are handled in separate stages. A Deep Reinforcement Learning (DRL) agent, trained with artificial neural networks, generates velocity commands that allow the robot to reach a goal while avoiding obstacles, using only onboard sensor data. These commands are then passed to a fuzzy Takagi-Sugeno (T-S) controller, which ensures accurate and robust trajectory tracking. In the single-agent case, the DRL-based navigation is compared with a classical navigation approach. The framework is further extended to a multi-robot setup, demonstrating decentralized coordination in shared environments. Simulation results validate the effectiveness and adaptability of the proposed pipeline. |
Description: | Mémoire de Projet de Fin d’Études :Automatique : Alger, École Nationale Polytechnique |
URI/URL: | http://repository.enp.edu.dz/jspui/handle/123456789/11191 |
Collection(s) : | Département Automatique |
Fichier(s) constituant ce document :
Fichier | Description | Taille | Format | |
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pfe.2025.aut.RABIA.Khalil_KHELFAOUI.Abderaouf.pdf | PA00325 | 12.43 MB | Adobe PDF | Voir/Ouvrir |
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