Abstract—Over the last few years, UAV applications have grown immensely from delivery services to military use. Major goal of UAV applications is to be able to operate and implement various tasks without any human aid. To best of our knowledge, in the existing works for autonomous navigation for UAV’s, ideal environments (e.g., 2D) are considered instead of realistic or special hardware are used (e.g., nine range sensors) to navigate through an ideal environment. Therefore, in this thesis, we aim to overcome the limitations of the existing works by proposing a model for navigating a drone in an unknown environment without any human help or aid. The goal of this research is to navigate from location A to location B in unknown terrain without having any prior knowledge about the terrain using default drone sensors only. We present a model which is compatible with almost every off-the-shelf drone available in the market. Our methodology utilizes only standard drone sensors which are attached to almost every drone. These include a camera, GPS, IMU, magnetometer, and barometer. Our methodology uses 3D, POMDP, and continuous environment. It also takes into account environmental factors such as winds and rain. Our main contributions are: 1) We are using realistic environment model including factors like rain and wind. 2) We are only using onboard computing resources to run our model instead of some external server. 3) We were able to fly the achieve complete autonomous flight using only standard drone sensors.
Index Terms—Unmanned arial vehicle, global positioning system, inertial measurement unit, partially observable Makarovian decision process.
Mudassar Liaq and Yungcheol Byun are with the Department of Computer Engineering, Jeju National University, Jeju, South Korea (email: firstname.lastname@example.org, email@example.com).
Cite: Mudassar Liaq and Yungcheol Byun, "Autonomous UAV Navigation Using Reinforcement Learning," International Journal of Machine Learning and Computing vol. 9, no. 6, pp. 756-761, 2019.Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).