Deep Reinforcement Learning-Based Path Planning Method for Stratospheric Airships in Spatiotemporally Complex Environments

Stratospheric airships have significant potential for remote sensing and communication, but their complex spatiotemporal environment poses significant path planning challenges. Our approach models the environment as the Cartesian product of diverse meteorological field time series and airship states, establishing a framework that accommodates more complex constraints in path planning. Additionally, by considering the impact of cold cloud fields on airship pressure differentials, our method enhances structural stability and safety by guiding the airship away from cold cloud regions. The method integrates the Soft Actor-Critic algorithm with the Long Short-Term Memory network, refining the model's exploration capabilities and its ability to capture temporal dependencies in sequential data. Comparative analyses indicate that the proposed model consistently outperforms alternative models across all evaluated metrics.