Mapping multiple gas/odor sources in an uncontrolled indoor environment using a Bayesian occupancy grid mapping based method

Abstract

-In this paper we address the problem of autonomously localizing multiple gas/odor sources in an indoor environment without a strong airflow. To do this, a robot iteratively creates an occupancy grid map. The produced map shows the probability each discrete cell contains a source. Our approach is based on a recent adaptation [15] to traditional Bayesian occupancy grid mapping for chemical source localization problems. The approach is less sensitive, in the considered scenario, to the choice of the algorithm parameters. We present experimental results with a robot in an indoor uncontrolled corridor in the presence of different ejecting sources proving the method is able to build reliable maps quickly (5.5 minutes in a 6 m x 2.1 m area) and in real time.

Information Theoretic Source Seeking Strategies for Multiagent Plume Tracking in Turbulent Fields

“…Existing works in the second category leverage the spatial distribution of observations that can be obtained using a team of mobile sensors to build a local flow model. The model is then used to predict changes in the material concentrations by integrating locally estimated flow functions [9,10] or building a map of the contamination distribution [11,12]. The third category consists of search strategies primarily concerned with the accuracy and robustness of the measurements when employing a gradient-based search strategy in a turbulent medium.…”

Autonomous Micro-Air-Vehicle Control Based on Visual Sensing for Odor Source Localization

“…Odor source localization has been studied widely for detecting explosives, for narcotics control, and for detecting gas leaks to ensure social safety and security [5][6][7][8][9][10][11][12]. Odor source localization using MAVs is a challenging task due to the non-uniform dispersion of odor, low sensitivity of commercially-available-gas sensors, and constraints of MAVs.…”

“…The first algorithm uses geometric control such as spiral patterns [6]. The second one is based on Bayesian inference theory [8]. The third mimics biological models such as cells [14], bacteria [9], insects [15], moths [10,11], and ants [12].…”

Uma abordagem distribuída e bio-inspirada para mapeamento de ambientes internos utilizando múltiplos robôs móveis

“…A construção autônoma de uma grade de ocupação cujas células identificam múltiplas fontes de gás, ou odor, em um ambiente interno sem um adequado fluxo de ar é proposta por Ferri et al (FERRI et al, 2011). Esta abordagem é baseada em uma adaptação do tradicional método de mapeamento Bayesiano de grades de ocupação aplicado ao problema de localização de fontes químicas.…”

“…Embora a concretização da questão do feromônio na estratégia IAS-SS possa ser realizada com dispositivos físicos reais, utilizando-se princípios similares aos apresentados por Ferri et al (FERRI et al, 2011), a aplicação do mesmo pode se demonstrar desvantajosa para o cenário do presente trabalho. Uma vez que a estratégia IAS-SS visa tratar do problema de vigilância e exploração de ambientes internos, a emissão de substâncias por um robô durante a sua navegação pode ser considerada inadequada dependendo do ambiente no qual o mesmo se encontra, por exemplo, um escritório.…”