用无人机量子磁感探测废墟下结构，提升搜救效率。
From Rubble Simulation to Active Magnetic Mapping: Quantum Sensing for Disaster Response
- 用无人机搭载量子磁力计，结合仿真与主动采样重建废墟磁场。
- 1米高处可检测亚皮特斯拉至纳特斯拉级磁场变化。
- 3个传感器+主动采样约100次即可实现高相关性结构重建。
在灾害响应中，72小时内定位建筑物坍塌幸存者是关键挑战，现有传感手段仅提供部分地下结构信息。本文提出基于无人机的量子磁测作为补充模态，并构建涵盖碎块物理、传感器部署与主动空间重构的仿真流程。利用Unreal Engine生成钢筋混凝土停车场坍塌场景，通过三角面片偶极子近似计算诱导磁场，证明在距屋顶约1米高度处，亚皮特斯拉至纳特斯拉量级的磁结构仍可被恢复。随后将稀疏多传感器样本输入由贝叶斯主动采样驱动的高斯过程回归后端，在多个独立坍塌实例上验证该流程；三传感器阵列在梯度分辨率与无人机载荷间取得最优平衡，主动采样约100次即达峰值结构相关性。结果表明，量子级传感有望成为无人机辅助结构分析及空洞探测的有效工具。
原文摘要 · Abstract (English)
Locating survivors of building collapses within the first 72 hours is a critical challenge in disaster response, and existing sensing modalities provide only partial information about the structure beneath the rubble. This paper proposes drone-based quantum magnetometry as a complementary modality and develops a simulation pipeline spanning rubble physics, sensor-array deployment, and active spatial reconstruction. We use Unreal Engine to generate a steel-reinforced concrete parking-garage collapse and compute the induced magnetic field via a per-triangle dipole approximation, establishing that meaningful magnetic structure is recoverable in the sub-pT to sub-nT range from roughly 1 m above the roofline. Then, we feed sparse multi-sensor samples into a Gaussian Process Regression back-end driven by Bayesian active sampling and validate the pipeline across multiple independent collapse realizations; a three-sensor array optimizes the trade-off between gradient resolution and UAV payload constraints, and active sampling reaches peak structural correlation in roughly $100$ samples. Together, these results indicate that quantum-grade sensing could become a useful tool for drone-based structural analysis and potentially void detection in collapsed buildings.
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