Mobile multi-robot systems are well suited for gas leak localization in challenging environments. They offer inherent advantages such as redundancy, scalability, and resilience to hazardous environments, all while enabling autonomous operation, which is key to efficient swarm exploration. To efficiently localize gas sources using concentration measurements, robots need to seek out informative sampling locations. For this, domain knowledge needs to be incorporated into their exploration strategy. We achieve this by means of partial differential equations incorporated into a probabilistic gas dispersion model that is used to generate a spatial uncertainty map of process parameters. Previously, we presented a potential-field-control approach for navigation based on this map. We build upon this work by considering a more realistic gas dispersion model, now taking into account the mechanism of advection, and dynamics of the gas concentration field. The proposed extension is evaluated through extensive simulations. We find that introducing fluctuations in the wind direction makes source localization a fundamentally harder problem to solve. Nevertheless, the proposed approach can recover the gas source distribution and compete with a systematic sampling strategy. The estimator we present in this work is able to robustly recover source candidates within only a few seconds. Larger swarms are able to reduce total uncertainty faster. Our findings emphasize the applicability and robustness of robotic swarm exploration in dynamic and challenging environments for tasks such as gas source localization.

Gas leaks in industry and nature can cause harmful effects on the environment and human health. Knowing how much gas is emitted over time helps to assess potential damage, track pollution sources, and develop effective mitigation strategies. To address this challenge, we propose to estimate the source's release rate based on measured gas concentrations in a cross section of the gas plume in the down-wind regions of the source. By combining wind information and the 2-D gas distribution in the cross section plane, we can infer the flow of gas through this plane, which is equal to the release rate of the source. We propose a Tunable Diode Laser Absorption Spectroscopy sensor (TDLAS) for remote, open-path gas sensing. By combining multiple TDLAS measurements with a gas tomography reconstruction algorithm, we obtain a 2-D map of gas distribution. This letter introduces an improved novel approach for gas tomography by incorporating prior model assumptions into the algorithm. Our method significantly enhances the accuracy and robustness of release rate estimates. We validate our approach through wind tunnel experiments, demonstrating that our novel estimation method produces precise and reliable release rate estimations for methane gas. The results further encourage exploring how 3-D gas tomography can improve our release rate estimations in the future.

Accurate estimation of the number and locations of dispersed material sources is critical for optimal disaster response in Chemical, Biological, Radiological, or Nuclear accidents. This paper introduces a novel approach to Gas Source Localization that uses sparse Bayesian learning adapted to models based on Partial Differential Equations for modeling gas dynamics. Using the method of Green's functions and the adjoint state method, a gradient-based optimization with respect to source location is derived, allowing superresolving (arbitrary) source locations. By combing the latter with sparse Bayesian learning, a sparse source support can be identified, thus indirectly assessing the number of sources. Simulation results and comparisons with classical sparse estimators for linear models demonstrate the effectiveness of the proposed approach. The proposed sparsity-constrained gas source localization method offers thus a flexible solution for disaster response and robotic exploration in hazardous environments.

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.