Abstract Monitoring airborne pollutant emissions and discovering their sources is an important task that mobile robots and Unmanned Aerial Vehicles (UAVs) are well suited for. However, it requires suitable gas sensors compatible with these platforms, alongside sampling strategies that direct them to optimal sampling locations. This article presents a dataset comprising numerous wind tunnel experiments where a synthetic gas plume was systematically scanned using various gas and environmental sensors in a dense 3D grid pattern, complemented by additional sampling trajectories. The dataset allows direct comparison between low-cost metal oxide semiconductor gas sensors (MiCS-5524, MiCS-6814) and advanced photoionization detectors (PID-AH2), examining both their static and dynamic responses to the plume. Additionally, a scale model industrial facility enables the evaluation of gas dispersion models in complex settings under controlled wind conditions. The data support the comparison and evaluation of novel sampling strategies for mobile robotic sensor systems for gas distribution mapping and source localization, providing more realistic experimental data compared to computational fluid dynamics simulations. Meeting the need for objective comparability of approaches in robotic gas sensing, this experimental dataset can serve as a standard corpus and benchmark.
Hinsen et al. (Fri,) studied this question.