Chemical Source Localization Fusing Concentration Information in the Presence of Chemical Background Noise

Abstract

We present the estimation of a likelihood map for the location of the source of a chemical plume dispersed under atmospheric turbulence under uniform wind conditions. The main contribution of this work is to extend previous proposals based on Bayesian inference with binary detections to the use of concentration information while at the same time being robust against the presence of background chemical noise. For that, the algorithm builds a background model with robust statistics measurements to assess the posterior probability that a given chemical concentration reading comes from the background or from a source emitting at a distance with a specific release rate. In addition, our algorithm allows multiple mobile gas sensors to be used. Ten realistic simulations and ten real data experiments are used for evaluation purposes. For the simulations, we have supposed that sensors are mounted on cars which do not have among its main tasks navigating toward the source. To collect the real dataset, a special arena with induced wind is built, and an autonomous vehicle equipped with several sensors, including a photo ionization detector (PID) for sensing chemical concentration, is used. Simulation results show that our algorithm, provides a better estimation of the source location even for a low background level that benefits the performance of binary version. The improvement is clear for the synthetic data while for real data the estimation is only slightly better, probably because our exploration arena is not able to provide uniform wind conditions. Finally, an estimation of the computational cost of the algorithmic proposal is presented.

Application of an Array of Metal-Oxide Semiconductor Gas Sensors in an Assistant Personal Robot for Early Gas Leak Detection

“…Figure 1 shows details of the gas sensing array used in the APR-02. According to previous experiences in gas leakage detection experiments with mobile robots [39,40], the gas sensing array is placed in front of the mobile robot at a height of 500 mm. The gas sensing system is a heterogeneous array of 16 MOX sensors (e-nose) which is an evolution of the array of eight MOX sensors proposed to detect two gas sources in a wind tunnel by Fonollosa et al [41]; however, in this paper, the gas sensor array will be used to sample in open conditions.…”

Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping

“…Plume modelling algorithms [40,41,42,43] assume a mathematical model for the plume, such as Gaussian shaped plumes [44] or filament/particle based models [45,46,47], and use local measurements of concentration and wind to fit the model and estimate the source location, which is usually a parameter of the model. The practical applicability of plume modelling methods is limited because they tend to make overly simplifying assumptions (e.g., that the wind field is stable, spatially uniform and measurable), often require a-priori information such as the source release rate in Gaussian models [40], or are sensitive to meta-parameters such as the odor detection threshold in filament-based models [41] or the probability of particle encounter as a function of distance to the source in particle models [42].…”