A critical limitation in developing portable Fourier transform-infrared (FTIR) stand-off chemical detectors is the detection library optimization, which reduces the number of false alarms and maintains the detection performance. Ideally, a detection library should be established through high-resolution (0.5 cm-1) FTIR spectroscopy in the laboratory. However, owing to the weight requirement of a portable FTIR stand-off detector, its optical system and mirror-moving range have limitations. Consequently, most portable FTIR stand-off detectors have been developed with a low resolution of approximately 4–16 cm-1. In this study, we developed a portable FTIR stand-off detector with a dual library of SF6. Moreover, we investigated its detection efficiency effect depending on the single library condition through realistic long-range (3 km) SF6 spectrum data in a field test.
The unmanned aerial vehicle (UAV) is a promising platform for remote chemical sensing to minimize human contact with toxic chemicals. Most previous studies in this field used predefined paths to search for areas based on sensor measurements at fixed points. However, operations on a predefined flight path are inefficient because in real‐life scenarios, gas dispersion is stochastic and unpredictable. Thus, a model‐free reinforcement‐learning approach using a deep Q‐network for autonomous UAV control is proposed. The UAV automatically selects its trajectory based on sensor measurements and GPS location data to map the contaminated area in real time. Path planning is simulated using Gaussian model‐based gas dispersion data and its feasibility in outdoor experiments using gas simulants is proved. This study provides guidelines for developing a model that can perform autonomous chemical detection and source localization using UAV.
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