用不确定性感知的图推理，让无人机更智能高效地建无线地图。
Bayesian-Driven Graph Reasoning for Active Radio Map Construction
- 基于贝叶斯神经网络实时估算空间不确定性
- 图推理使轨迹规划准确率提升34%以上
- 适合无人机自主测绘与低空经济应用
随着低空经济兴起，无线地图对保障空中平台可靠连接至关重要。传统基于航点导航的自主飞行器受限于电池容量，难以高效覆盖。为此，我们提出不确定性感知无线地图（URAM）重建框架，结合图推理与贝叶斯深度学习：首先通过贝叶斯神经网络实时估计空间不确定性；再利用注意力增强的强化学习策略，在概率路网中进行全局推理，以不确定性为依据规划高信息量且节能的飞行路径。该方法实现非短视的智能路径规划，引导飞行器前往最需探测区域，同时满足安全约束。实验表明，相较于现有基线，URAM在重建精度上最高提升34%。
原文摘要 · Abstract (English)
With the emergence of the low-altitude economy, radio maps have become essential for ensuring reliable wireless connectivity to aerial platforms. Autonomous aerial agents are commonly deployed for data collection using waypoint-based navigation; however, their limited battery capacity significantly constrains coverage and efficiency. To address this, we propose an uncertainty-aware radio map (URAM) reconstruction framework that explicitly leverages graph-based reasoning tailored for waypoint navigation. Our approach integrates two key deep learning components: (1) a Bayesian neural network that estimates spatial uncertainty in real time, and (2) an attention-based reinforcement learning policy that performs global reasoning over a probabilistic roadmap, using uncertainty estimates to plan informative and energy-efficient trajectories. This graph-based reasoning enables intelligent, non-myopic trajectory planning, guiding agents toward the most informative regions while satisfying safety constraints. Experimental results show that URAM improves reconstruction accuracy by up to 34% over existing baselines.
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