🤖 AI Summary
This study addresses the challenge of accurately locating survivors within the critical 72-hour window following building collapse, where limited knowledge of internal rubble structure impedes rescue efforts. The authors propose an active sensing approach employing a drone-mounted array of quantum magnetometers, integrating quantum-grade magnetic sensing with Bayesian active learning for the first time. They develop an end-to-end simulation framework that encompasses physical collapse modeling (based on Unreal Engine), dipole magnetic field approximation via triangular surface elements, and Gaussian process regression–driven spatial magnetic field reconstruction. Experimental results demonstrate effective recovery of magnetic signals ranging from sub-picotesla to sub-nanotesla levels at approximately one meter above the rubble surface. A three-sensor array achieves optimal structural correlation within fewer than 100 sampling iterations, validating the method’s feasibility and efficiency for void detection in disaster scenarios.
📝 Abstract
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.