Autonomous UAV Navigation Using Reinforcement Learning
This enables UAVs to operate in critical applications like wildfire monitoring or search and rescue, though it appears incremental as it applies existing reinforcement learning methods to UAV systems.
The paper tackled autonomous UAV navigation in unknown environments by developing a reinforcement learning framework, demonstrating successful navigation in simulations and real implementations.
Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of the environment may not be available. This paper provides a framework for using reinforcement learning to allow the UAV to navigate successfully in such environments. We conducted our simulation and real implementation to show how the UAVs can successfully learn to navigate through an unknown environment. Technical aspects regarding to applying reinforcement learning algorithm to a UAV system and UAV flight control were also addressed. This will enable continuing research using a UAV with learning capabilities in more important applications, such as wildfire monitoring, or search and rescue missions.