Autonomous Drone Navigation Using Reinforcement Learning Techniques

Abstract

This article presents a novel approach to autonomous drone navigation by implementing reinforcement learning techniques. We detail the development of algorithms capable of improving navigation efficiency and obstacle detection in dynamic environments. The study includes experimental results showcasing significant advancements in drone autonomy, addressing both technical challenges and potential applications in various sectors such as agriculture, disaster response, and logistics. Our findings suggest that reinforcement learning can significantly optimize drone operations.