Autonomous UAV Navigation in Unfamiliar Indoor Environments Using Deep Reinforcement Learning - Morgan, James T.; Sanders, Olivia K.; Chen, Ethan L.; Bennett, Sophia R.; Foster, Ryan D. (Journal of Artificial Intelligence and Information, 2025)
Efficient and collision-free navigation in GPS-denied indoor environments remains a fundamental challenge in the development of intelligent Unmanned Aerial Vehicles (UAVs). This study introduces DRAL-UAV, a Deep Reinforcement Adaptive Learning framework tailored for path planning in partially observable environments. The proposed model features a hierarchical policy network that integrates a visual encoder, a recurrent memory module, and an adaptive reward adjustment mechanism. Training and evaluation are conducted using the AirSim 3D simulation platform, incorporating complex conditions such as dynamic obstacles and narrow corridors. Experimental results demonstrate that DRAL-UAV achieves an average navigation success rate of 92.6% over 5,000 trials across 100 scenarios, outperforming traditional A* and DDPG methods. Furthermore, the model reduces the average path length by 28.4% and maintains a low failure rate of 5% in challenging narrow corridor scenarios (corridor width ratio = 1.2). The finding highlight DRAL-UAV’s robust decision-making ability, improved generalization in unstructured environments and high applicability in real-world UAV tasks, including search and rescue, warehouse automation and industrial inspections.