Deep Reinforcement Learning-Based Path Planning for Uncrewed Systems: A Survey
Mingfeng Fan, Guangzhi Wang, Hao Chen, Yifeng Zhang, Yibin Yang, Ling Wang (56577), Guohua Wu, Guillaume Sartoretti · Unmanned Systems · 2026
With the rapid advancements in artificial intelligence and control technologies in recent years, uncrewed systems have become increasingly prevalent across various fields. Path planning, a critical technology enabling autonomy in these systems, remains a challenging and active area of research. This review provides a comprehensive overview of the fundamentals of path planning and deep reinforcement learning (DRL), laying the foundation for understanding the potential and limitations of DRL in uncrewed system applications. It systematically reviews DRL methodologies and examines their applications across uncrewed aerial vehicles (UAVs), uncrewed ground vehicles (UGVs), uncrewed surface vehicles (USVs), and heterogeneous platforms, highlighting representative algorithms, real-world deployment scenarios, and diverse operational environments. The review also identifies major challenges in DRL-driven path planning, including real-time adaptability, robustness in complex environments, and scalability across domains. In addressing these challenges, it offers valuable insights and outlines future research directions, emphasizing the need for enhanced efficiency, safety, and generalization to meet the demands of next-generation autonomous systems.