As applications of Unmanned Aerial Vehicles (UAVs) continue to expand across sectors such as logistics, surveillance, and environmental monitoring, the development of safe and robust autonomous navigation capabilities has become increasingly important. Mapless autonomous UAV navigation refers to a UAV’s ability to reach a designated destination along a collision-free and efficient path without human intervention and without relying on a global map. Achieving this capability is particularly challenging in complex environments, especially in cluttered spaces, in the presence of dynamic obstacles, or under wind disturbances. Deep reinforcement learning (DRL) has emerged as one of the leading approaches for modeling UAVs as intelligent agents that learn effective control policies through interaction with the environment. However, the performance of these methods depends strongly on both the characteristics of the environment and the limitations of the underlying algorithms. A deep understanding of algorithmic trade-offs is essential for selecting and tailoring the appropriate RL framework to ensure safe, robust and efficient UAV navigation in complex environments. Within this context, this study provides a structured overview of recent advances in state-of-the-art DRL approaches for Mapless UAV navigation and reviews their application across various UAV-environment dynamics and constraints. In addition, the review identifies key challenges in deploying DRL in highly complex environments. It also outlines open research questions and proposes future research directions to advance this field.
