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 (eng)
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 (eng)

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