Accuracy Improvement of Straight Take-off, Flying Forward and Landing of a Drone with Reinforcement Learning

Abstract

Nowadays, drones are expected to be used in several fields, especially flying indoors to explore the surroundings. In this paper, we present a novel method that uses ArUco markers as a reference to improve the accuracy of a drone on straight takeoff, flying forward, and landing based on Reinforcement Learning (RL). Particularly, the drone first detects a specific marker with its onboard cameras. Then, it calculates the position and orientation relative to the marker so as to adjust its actions for achieving better accuracy with an RL method. We perform several simulation experiments by using the Robot Operating System (ROS) and its Gazebo simulator. Simulation results show that our proposed method can efficiently improve the accuracy of the considered actions.