Long-term cooperative path planning for stratospheric airships based on hierarchical multi-agent reinforcement learning

Stratospheric airships are increasingly used for long-term collaborative tasks, requiring efficient path planning for multiple airships. Traditional methods struggle with collaborative optimization and state space explosion in such tasks. To address these issues, this paper presents a hierarchical cooperative airship path planning (HiCAPP). This HiCAPP employs a dual-layer control architecture, with the high-level controller responsible for task allocation and the low-level controller concentrating on path planning. Experimental results show that HiCAPP outperforms traditional multi-agent reinforcement learning methods in two critical metrics: average remaining energy and average distance to the task center. Additionally, through experiments with varying numbers of agents, task durations, and disturbances, HiCAPP has demonstrated robustness and scalability. These results confirm its effectiveness in long-term cooperative monitoring tasks and highlight the advantages of hierarchical decision-making in multi-agent systems.