Abstract
A spatial odor distribution in an environment can be used for navigation, goal search, localization and mapping, like by video, ultrasonic, temperature and other sensors. Modern e-noses can perform the selective detection of different gases with an extremely low concentration but the source localization algorithms of a selected gas against the background of other odors are still underinvestigated. This paper studies an odor field representation in terms of an e-nose based on an array of low-selective sensors. Using a simulation model, we show how the vector measurements of a field of several odor sources can be processed to navigate for reaching a selected odor source. In addition, we demonstrate that the source having a high level of odor intensity can interfere with the search of another odor source of a low intensity. The well-known class of matching receivers does not solve this problem. However, a solution can be obtained by distributed measurements. As shown below, the spatial structure of an odor field allows to implement vector selection. Using deep learning machines, we may reach a high resolution of odor sources in the space. Our future research will be focused on augmented odor reality and autonomous mobile e-nose (e-dog) design.
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Original Russian Text © V.V. Krylov, 2016, published in Datchiki i Sistemy, 2016, No. 6, pp. 3–13.
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Krylov, V.V. Odor Space Navigation Using Multisensory E-Nose. Autom Remote Control 79, 167–179 (2018). https://doi.org/10.1134/S0005117918010149
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DOI: https://doi.org/10.1134/S0005117918010149