Towards Odor-Sensitive Mobile Robots

J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment

Gas recognition based on the physicochemical parameters determined by monitoring diffusion rates in microfluidic channels

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.Monitoring the diffusion progress rates of different gases in a microfluidic channel affords their discrimination by the comparison of their temporal profiles in a high-dimensional feature space. Here, we demonstrate gas recognition by determination of their three important physicochemical parameters via a model-based examination of the experimentally determined diffusion rates in two different cross-section channels. The system utilized comprises two channels with respective cross-sectional diameters of 1000 μm and 50 μm. The open end of both channels are simultaneously exposed to the analyte, and the temporal profiles of the diffusion rates are recorded by continuous resistance measurements on the chemoresistive sensors spliced to the channels at their other ends. Fitting the solutions of the diffusion equation to the experimental profiles obtained from the large cross-section channel results in the diffusivity of the analyte. The results of small cross-section channel, however, fit the solutions of a modified diffusion equation which accounts for the adsorption of the analyte molecules to the channel walls, as well. The latter fitting process results in the adsorption parameter for the analyte-channel wall interactions and the population of the effective adsorption sites on the unit area of the walls. The allocation of these three meaningful parameters to an unknown gaseous analyte affords its recognition

Environmental engineering applications of electronic nose systems based on MOX gas sensors

Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors’ applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications

Applications and Advances in Electronic-Nose Technologies

Electronic-nose devices have received considerable attention in the field of sensor technology during the past twenty years, largely due to the discovery of numerous applications derived from research in diverse fields of applied sciences. Recent applications of electronic nose technologies have come through advances in sensor design, material improvements, software innovations and progress in microcircuitry design and systems integration. The invention of many new e-nose sensor types and arrays, based on different detection principles and mechanisms, is closely correlated with the expansion of new applications. Electronic noses have provided a plethora of benefits to a variety of commercial industries, including the agricultural, biomedical, cosmetics, environmental, food, manufacturing, military, pharmaceutical, regulatory, and various scientific research fields. Advances have improved product attributes, uniformity, and consistency as a result of increases in quality control capabilities afforded by electronic-nose monitoring of all phases of industrial manufacturing processes. This paper is a review of the major electronic-nose technologies, developed since this specialized field was born and became prominent in the mid 1980s, and a summarization of some of the more important and useful applications that have been of greatest benefit to man

Classifiers With a Reject Option for Early Time-Series Classification

Early classification of time-series data in a dynamic environment is a challenging problem of great importance in signal processing. This paper proposes a classifier architecture with a reject option capable of online decision making without the need to wait for the entire time series signal to be present. The main idea is to classify an odor/gas signal with an acceptable accuracy as early as possible. Instead of using posterior probability of a classifier, the proposed method uses the "agreement" of an ensemble to decide whether to accept or reject the candidate label. The introduced algorithm is applied to the bio-chemistry problem of odor classification to build a novel Electronic-Nose called Forefront-Nose. Experimental results on wind tunnel test-bed facility confirms the robustness of the forefront-nose compared to the standard classifiers from both earliness and recognition perspectives

Toxic gases detection and tolerance level classification using machine learning algorithms

Abstract— with rapid population increases, people are facing the challenge to maintain healthy conditions. One of the challenges is air pollution. Due to industrial development and vehicle usage air pollution is becoming a high threat to human life. This air pollution forms through various toxic contaminants. This toxic contamination levels increase and cause severe damage to the living things in the environment. To identify the toxic level present in the polluted air various methods were proposed by the authors, But failed to detect the tolerance level of toxic gases. This article discusses the methods to detect toxic gasses and classify the tolerance level of gasses present in polluted air. Various sensors and different algorithms are used for classifying the tolerance level. For this purpose “Artificial Sensing Methodology” (ASM), commonly known as e-nose, is a technique for detecting harmful gases. SO2-D4, NO2-D4, MQ-135, MQ136, MQ-7, and other sensors are used in artificial sensing methods (e-nose). “Carbon monoxide, Sulfur dioxide, nitrogen dioxide, and carbon dioxide” are all detected by these sensors. The data collected by sensors is sent to the data register from there it is sent to the Machine learning Training module (ML) and the comparison is done with real-time data and trained data. If the values increase beyond the tolerance level the system will give the alarm and release the oxygen

Electronic noses for environmental monitoring applications

Electronic nose applications in environmental monitoring are nowadays of great interest, because of the instruments’ proven capability of recognizing and discriminating between a variety of different gases and odors using just a small number of sensors. Such applications in the environmental field include analysis of parameters relating to environmental quality, process control, and verification of efficiency of odor control systems. This article reviews the findings of recent scientific studies in this field, with particular focus on the abovementioned applications. In general, these studies prove that electronic noses are mostly suitable for the different applications reported, especially if the instruments are specifically developed and fine-tuned. As a general rule, literature studies also discuss the critical aspects connected with the different possible uses, as well as research regarding the development of effective solutions. However, currently the main limit to the diffusion of electronic noses as environmental monitoring tools is their complexity and the lack of specific regulation for their standardization, as their use entails a large number of degrees of freedom, regarding for instance the training and the data processing procedures