用图注意力网络优化多无人机编队，兼顾覆盖范围与续航时间
Graph Attention-based Decentralized Actor-Critic for Dual-Objective Control of Multi-UAV Swarms
- 基于图注意力机制处理局部观测，降低环境状态维度
- 双评论家网络协同优化覆盖与续航，实现双目标平衡
- 在真实信道环境下验证，性能优于现有方法
本研究针对多无人机系统提出双目标优化：以最大化服务覆盖为首要目标，延长电池寿命为次要目标。提出图注意力驱动的去中心化演员-评论家（GADC）方法，利用图注意力网络处理无人机有限的局部观测信息，降低环境状态维度；随后设计演员-双评论家网络，联合优化双重策略。通过引入Kullback-Leibler（KL）散度因子，动态平衡覆盖性能与电池寿命之间的权衡。在理论与实验层面进行全面基准测试，涵盖理想场景与NVIDIA Sionna真实射线追踪环境下的大量测试，结果表明GADC具备优异的可扩展性与效率。
原文摘要 · Abstract (English)
This research focuses on optimizing multi-UAV systems with dual objectives: maximizing service coverage as the primary goal while extending battery lifetime as the secondary objective. We propose a Graph Attention-based Decentralized Actor-Critic (GADC) to optimize the dual objectives. The proposed approach leverages a graph attention network to process UAVs' limited local observation and reduce the dimension of the environment states. Subsequently, an actor-double-critic network is developed to manage dual policies for joint objective optimization. The proposed GADC uses a Kullback-Leibler (KL) divergence factor to balance the tradeoff between coverage performance and battery lifetime in the multi-UAV system. We assess the scalability and efficiency of GADC through comprehensive benchmarking against state-of-the-art methods, considering both theory and experimental aspects. Extensive testing in both ideal settings and NVIDIA Sionna's realistic ray tracing environment demonstrates GADC's superior performance.
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