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Auteur Zakaria Miloudia Moncef
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| Titre : | Q-learning based path planning and predictive control for the navigation of a mobile robot | | Type de document : | document multimédia | | Auteurs : | Oussama Guettaf, Auteur ; Zakaria Miloudia Moncef, Auteur ; Fatima Chouireb, Directeur de thèse | | Editeur : | Laghouat : Université Amar Telidji - Département d'électronique | | Année de publication : | 2024 | | Importance : | 60 p. | | Note générale : | Option : Automatic and industrial informatic | | Langues : | Anglais | | Résumé : | Our work aims to find the optimal path to enable a mobile robot to navigate from a starting point to a destination in a known environment, while avoiding obstacles. To achieve this goal, we started by studying and implementing the Model Predictive Control (MPC) framework in the first phase. Then, in a second phase, we explored various state-of-the-art planning algorithms, including Reinforcement Learning approaches. Among the latter, we studied and implemented the Q-Learning algorithm to perform the path planning according to the simulated scenarios. Ours simulations were conducted both using the Matlab environment and the MATLAB-ROS interface along with the Gazebo simulator. The results we obtained were highly reliable. | | note de thèses : | Mémoire de master en électronique |
Q-learning based path planning and predictive control for the navigation of a mobile robot [document multimédia] / Oussama Guettaf, Auteur ; Zakaria Miloudia Moncef, Auteur ; Fatima Chouireb, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'électronique, 2024 . - 60 p. Option : Automatic and industrial informatic Langues : Anglais | Résumé : | Our work aims to find the optimal path to enable a mobile robot to navigate from a starting point to a destination in a known environment, while avoiding obstacles. To achieve this goal, we started by studying and implementing the Model Predictive Control (MPC) framework in the first phase. Then, in a second phase, we explored various state-of-the-art planning algorithms, including Reinforcement Learning approaches. Among the latter, we studied and implemented the Q-Learning algorithm to perform the path planning according to the simulated scenarios. Ours simulations were conducted both using the Matlab environment and the MATLAB-ROS interface along with the Gazebo simulator. The results we obtained were highly reliable. | | note de thèses : | Mémoire de master en électronique |
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