Rutkauskas Marius, Asenov Martin, Ramamoorthy Subramanian, Reid Derryck T
Opt Express. 2019 Apr 1;27(7):9578-9587. doi: 10.1364/OE.27.009578.
Unmanned aerial vehicles (UAVs)-or drones-present compelling new opportunities for airborne gas sensing in applications such as environmental monitoring, hazardous scene assessment, and facilities' inspection. Instrumenting a UAV for this purpose encounters trade-offs between sensor size, weight, power, and performance, which drives the adoption of lightweight electrochemical and photo-ionisation detectors. However, this occurs at the expense of speed, selectivity, sensitivity, accuracy, resolution, and traceability. Here, we report on the design and integration of a broadband Fourier-transform infrared spectrometer with an autonomous UAV, providing ro-vibrational spectroscopy throughout the molecular fingerprint region from 3 - 11 µm (3333 - 909 cm) and enabling rapid, quantitative aerial surveys of multiple species simultaneously with an estimated noise-limited performance of 18 ppm (propane). Bayesian interpolation of the acquired gas concentrations is shown to provide both localization of a point source with approximately one meter accuracy, and distribution mapping of a gas cloud, with accompanying uncertainty quantification.
无人驾驶飞行器(UAV)——即无人机——为环境监测、危险场景评估和设施检查等应用中的机载气体传感带来了引人注目的新机遇。为此为无人机配备仪器时,需要在传感器尺寸、重量、功率和性能之间进行权衡,这推动了轻质电化学和光离子化探测器的采用。然而,这样做是以牺牲速度、选择性、灵敏度、准确性、分辨率和可追溯性为代价的。在此,我们报告了一种宽带傅里叶变换红外光谱仪与自主无人机的设计与集成,该光谱仪可在3至11微米(3333至909厘米)的整个分子指纹区域提供转动-振动光谱,能够同时对多种物质进行快速、定量的空中勘测,估计噪声限制性能为18 ppm(丙烷)。结果表明,对采集到的气体浓度进行贝叶斯插值既能以约一米的精度定位点源,又能绘制气体云的分布图,并对不确定性进行量化。