基于图注意力去中心化 Actor-Critic 的多无人机网络双目标控制方法 GADC
Graph Attention-based Decentralized Actor-Critic for Dual-Objective Control of Multi-UAV Networks
AI 导读
研究者提出图注意力去中心化 Actor-Critic 方法 GADC，用于多无人机系统的双目标优化，在最大化服务覆盖的同时延长电池寿命。该方法用图注意力网络处理无人机有限局部观测，并通过 actor-double-critic 网络与 KL 散度因子平衡覆盖性能与电池寿命的权衡。在理想环境和 NVIDIA Sionna 真实射线追踪环境中的测试显示 GADC 优于现有最先进方法。
正文 · 原文
Abstract: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.
| Subjects: | Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2506.09195 [eess.SP] |
| (or arXiv:2506.09195v2 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.2506.09195 arXiv-issued DOI via DataCite |
Submission history
From: Haoran Peng [view email]
[v1]
Tue, 10 Jun 2025 19:30:53 UTC (9,157 KB)
[v2]
Mon, 9 Nov 2026 20:46:18 UTC (23,063 KB)
来源：arXiv cs.AI · arxiv.org