Autonomous Unmanned Aerial Vehicle navigation using Reinforcement Learning: A systematic review

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 environ-ment, 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.

Automated summary

There is a growing demand for using drones in various applications, and autonomous UAV navigation is commonly achieved using reinforcement learning algorithms. Understanding the navigation environment and algorithm limitations is crucial in selecting the right algorithms to solve navigation problems effectively. This study identifies the main UAV navigation tasks, discusses navigation frameworks and simulation software, and classifies RL algorithms based on their environment, characteristics, abilities, and applications.