Deep Reinforcement Learning-based Path Planning for Uncrewed Systems: A Survey
Mingfeng Fan·Guillaume Sartoretti·Ling Wang (56577)·Hao Chen·Yibin Yang·Yifeng Zhang·Guangzhi Wang·Guohua Wu
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 un
