用注意力机制优化无人机充电路径，提升大规模物联网系统能效。
Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systems
- 基于图Transformer的注意力模型，动态计算设备间关联性
- 多无人机轨迹规划效率提升，硬件实测验证可行性
- 适合大规模物联网场景下的智能能源调度
在无线能量传输辅助的物联网系统中，无人机面临资源有限和轨迹规划不佳的问题。基于强化学习的轨迹规划方法在大规模系统中存在搜索效率低、学习不稳定的缺陷。为此，本文提出一种基于注意力机制的无人机轨迹优化框架AUTO，包含注意力轨迹优化模型ATOM与基于演员-评论家的轨迹学习方法TENMA。ATOM通过图编码器计算所有物联网设备的自注意力特征，并设计轨迹解码器以优化无人机数量与飞行路径；TENMA采用改进的演员-评论家方法训练ATOM，以系统实际奖励作为基线，降低评论家网络方差。该方法适用于高质量、大规模多无人机轨迹规划。最后，我们开展多项实验，包括实地硬件测试，验证了AUTO框架的可行性和高效性。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAVs) in Wireless Power Transfer (WPT)-assisted Internet of Things (IoT) systems face the following challenges: limited resources and suboptimal trajectory planning. Reinforcement learning-based trajectory planning schemes face issues of low search efficiency and learning instability when optimizing large-scale systems. To address these issues, we present an Attention-based UAV Trajectory Optimization (AUTO) framework based on the graph transformer, which consists of an Attention Trajectory Optimization Model (ATOM) and a Trajectory lEarNing Method based on Actor-critic (TENMA). In ATOM, a graph encoder is used to calculate the self-attention characteristics of all IoTDs, and a trajectory decoder is developed to optimize the number and trajectories of UAVs. TENMA then trains the ATOM using an improved Actor-Critic method, in which the real reward of the system is applied as the baseline to reduce variances in the critic network. This method is suitable for high-quality and large-scale multi-UAV trajectory planning. Finally, we develop numerous experiments, including a hardware experiment in the field case, to verify the feasibility and efficiency of the AUTO framework.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。