Large Language Model Enhanced Multi-UAV Direct Cross-boundary Maritime Data Collection Scheme

The cross-boundary communication between unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) constitutes a pivotal component in achieving full coverage of the space-air-ground-sea integrated network of 6G. In such hybrid networks, it is imperative to intelligently plan the trajectory of UAVs by exploiting the deep reinforcement learning (DRL) technique and direct optical wireless communication (OWC) technology to ensure timely data collection. However, the traditional DRL technique suffers from issues such as sparse rewards and low sampling efficiency, which leads to a decrease in the freshness of data. To address these issues, in this paper, we investigate the trajectory planning problem for swarms of UAVs conducting data collection with age of information awareness. Leveraging the prior knowledge of large language models (LLMs) and multi-agent reinforcement learning technique, we propose a novel multi-UAV maritime data collection scheme. Firstly, we extract the prior knowledge strategies of LLMs in the form of expert trajectories through structured prompts. Then, initial strategy models are rapidly generated for UAVs through multi-agent behavior cloning method, which reduce ineffective exploration and accelerates learning. Finally, the MATD3 algorithm is used to fine-tune these strategy models, enhancing their policy learning capability in sparse reward environments. We also conduct simulations to validate the effectiveness of the proposed algorithms.