Autonomous navigation of UAV by using real-time model-based reinforcement learning

Autonomous navigation in an unknown or uncertain environment is one of the challenging tasks for unmanned aerial vehicles (UAVs). In order to address this challenge, it is necessary to have sophisticated high level control methods that can learn and adapt themselves to changing conditions. One of the most promising frameworks for such a purpose is reinforcement learning. In this paper, a novel model-based reinforcement learning algorithm, TEXPLORE, is developed as a high level control method for autonomous navigation of UAVs. The developed approach has been extensively tested with a quadcopter UAV in ROS-Gazebo environment. The experimental results show that our method is able to learn an efficient trajectory in a few iterations and perform actions in real-time. Moreover, we show that our approach significantly outperforms Q-learning based method. To the best of our knowledge, this is the first time that TEXPLORE has been developed to achieve autonomous navigation of UAVs.