Electrical and Computer Engineering Publications
Document Type
Article
Publication Date
8-24-2022
Volume
115
Journal
Engineering Applications of Artificial Intelligence
URL with Digital Object Identifier
https://doi.org/10.1016/j.engappai.2022.105321
Abstract
There is an increasing demand for using Unmanned Aerial Vehicle (UAV), known as drones, in different applications such as packages delivery, traffic monitoring, search and rescue operations, and military combat engagements. In all of these applications, the UAV is used to navigate the environment autonomously --- without human interaction, perform specific tasks and avoid obstacles. Autonomous UAV navigation is commonly accomplished using Reinforcement Learning (RL), where agents act as experts in a domain to navigate the environment while avoiding obstacles. Understanding the navigation environment and algorithmic limitations plays an essential role in choosing the appropriate RL algorithm to solve the navigation problem effectively. Consequently, this study first identifies the main UAV navigation tasks and discusses navigation frameworks and simulation software. Next, RL algorithms are classified and discussed based on the environment, algorithm characteristics, abilities, and applications in different UAV navigation problems, which will help the practitioners and researchers select the appropriate RL algorithms for their UAV navigation use cases. Moreover, identified gaps and opportunities will drive UAV navigation research.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Citation of this paper:
@article{ALMAHAMID2022105321, title = {Autonomous Unmanned Aerial Vehicle navigation using Reinforcement Learning: A systematic review}, journal = {Engineering Applications of Artificial Intelligence}, volume = {115}, pages = {105321}, year = {2022}, issn = {0952-1976}, doi = {https://doi.org/10.1016/j.engappai.2022.105321}, url = {https://www.sciencedirect.com/science/article/pii/S095219762200358X}, author = {Fadi AlMahamid and Katarina Grolinger}, keywords = {Reinforcement Learning, Autonomous UAV navigation, UAV, Systematic review} }
Notes
DOI: 10.1016/j.engappai.2022.105321