Deep reinforcement learning for target-driven robot navigationDobrevski, Matej (Avtor) Skočaj, Danijel (Mentor) deep learningreinforcement learningmobile roboticsnavigationobstacle avoidance.Mobile robots that operate in real-world environments need to be able to safely navigate their surroundings. Obstacle avoidance and path planning are crucial capabilities for achieving autonomy in such systems. However, for new or dynamic environments, navigation methods that rely on an explicit map of the environment can be impractical or impossible to use. The resurgence of neural networks has enabled great progress in reinforcement learning methods. In this thesis, we propose local navigation methods, that do not rely on a map, modeled by deep neural networks and trained using reinforcement learning in simulation. We combine the power of data-driven learning and the dynamic model of the robot, enabling adaptation to the current environment as well as guaranteeing collision-free movement and smooth trajectories of the mobile robot. We evaluate and compare our navigation approaches with related work and a standard map-based approach to navigation scenarios in simulation and demonstrate that our methods are able to navigate the robot when the standard approaches fail and outperform the related work. We also show that our policy can be transferred to a real robot.20242024-01-19 13:00:02Doktorsko delo/naloga154022VisID: 23168COBISS_ID: 182594819sl