Signal Processing and Machine Learning for Gas Sensors : Gas Source Localization with a NanoDrone

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Chemical source localization (CSL) by autonomous robots has been a topic of research since the early 1990s and still today remains elusive beyond simple scenarios. It has numerous potential applications, such as the localization of toxic emissions, malodors, gas leaks and hazardous substances in general, without risking human lives. An intuitive CSL approach is to mimic the known chemo-orientation behaviour of some flying insects, such as moths and mosquitos, which effectively use odor plumes for mating and foraging. However, terrestrial robots are too slow to perform insect-like movements and the response timeand limit of detection (LOD) of current odor sensors for key compounds of biological relevance for plume navigation is orders of magnitude higher than in biological chemoreceptors. Instead of using a slow terrestrial robot equipped with complex instrumentation, in this thesis we address the CSL problem with a nano-drone, i.e. a miniaturized aerial robot, equipped with a simple metal oxide semiconductor (MOX) sensor. Improving key specifications of MOX sensors for this application is one of the core parts of this thesis. Specifically, we introduce novel signal processing methods for estimating and optimizing the LOD, reducing the power consumption and improving the response time. We propose a univariate LOD optimization method based on linearized calibration models and a multivariate approach based on orthogonal partial least squares (O-PLS). To improve the response time, we use high-frequency features extracted from the signal derivative, and optimize the algorithm for changing wind conditions and real-time operation. A novel setup consisting on a 3D grid of MOX sensors is proposed for 3D feature selection, and for real-time visualization of the gas distribution. Two CSL strategies, one based on the instantaneous response and the other one based on odor events, are finally evaluated using the nano-drone in experiments performed in a large indoor environment (160 m2). The experimental results demonstrate that the proposed platform can be used to quickly (<3 min) build a rough gas distribution map (3D) of the environment and localize the main chemical source within it with small errors of ~1 m.

Jul 9, 2019 11:00 AM — 12:00 PM
Sala Eduard Fontseré
Marti i Franques, 1, Barcelona, CAT 08028
Javier Burgués
Javier Burgués
Technical Lead

Developing the new generation of chemical sensors for automotive, industrial and home automation applications.