A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform

Unmanned Aerial Vehicle (UAV) is increasingly becoming an important tool used for a variety of tasks. In addition, Reinforcement Learning (RL) is a popular research topic. In this paper, these two fields are combined together and we apply the reinforcement learning into the UAV field, promote the application of reinforcement learning in our real life. We design a reinforcement learning framework named ROS-RL, this framework is based on the physical simulation platform Gazebo and it can address the problem of UAV motion in continuous action space. We can connect our algorithms into this framework through ROS and train the agent to control the drone to complete some tasks. We realize the autonomous landing task of UAV using three different reinforcement learning algorithms in this framework. The experiment results show the effectiveness of algorithm in controlling UAV which flights in a simulation environment close to the real world.