2026-05-27T05:42:28Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/138892026-02-03T11:31:11Zcom_10630_2254col_10630_37953

González-Monroy, Javier Jaimez, Mariano González-Jiménez, Antonio Javier 2017-06-15T06:31:05Z 2017-06-15T06:31:05Z 2017-06-15 http://hdl.handle.net/10630/13889 This work introduces a novel solution to approximate in real time the 2D wind flow present in a geometrically known environment. It is grounded on the probabilistic framework provided by a Markov random field and enables the estimation of the most probable wind field from a set of noisy observations, for the case of incompressible and steady wind flow. Our method delivers reasonably precise results without falling into common unrealistic assumptions like homogeneous wind flow, absence of obstacles, etc., and performs very efficiently (less than 0.5 seconds for an environment represented with a 100x100 cell grid). This approach is then quite suitable for applications that require real-time estimation of the wind flow, as for example, the localization of gas sources, prediction of the gas dispersion, or the mapping of the gas distribution of different chemicals released in a given scenario. eng open access by-nc-nd Circulación atmosférica Online Estimation of 2D Wind Maps for Olfactory Robots journal article