This work presents a point-to-point trajectory planning framework for UAVs based on Q-Learning, integrated into a ROS2–Gazebo simulation environment. The problem is formulated as a discrete Markov Decision Process (MDP) over a two dimensional discretization of the space, with execution at constant altitude. Cylindrical obstacles are randomly generated in each experiment and represented using an inflated occupancy grid, which incorporates an explicit geometric safety margin during planning (2 m). Learning incorporates reward shaping based on potential to accelerate convergence while maintaining policy optimality. After training, the resulting trajectory is simplified, densified, and geometrically validated (by removing collinear points, interpolating intermediate waypoints, and sampling along segments on the inflated occupancy grid) before its execution in physical simulation. The results show stable convergence, high success rates, and spatial coherence between the learned value function and the geometry of the environment. The comparison between planned and executed trajectories confirms the transferability of the discrete model to a continuous dynamic system.