Towards Odor-Sensitive Mobile Robots

Out 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.

Integrating Odor Sensing in Robotics

In this chapter, the benefit of equipping the robot with odor sensors is investigated. The chapter addresses the types of tasks the mobile robots can accomplish with the help of olfactory sensing capabilities, the technical challenges in mobile robot olfaction, the status of mobile robot olfaction. The chapter also addresses simple and complex electronic olfaction sensors used in mobile robotics, the challenge of using chemical sensors, the use of many types of algorithms for robot olfaction, and the future research directions in the field of mobile robot olfaction.

Artificial Odour Classification System

This chapter overviews the issue of multicollinearity in electronic nose (e-nose) classification and investigates some analytical solutions to deal with the problem. Multicollinearity effect may harm classification analysis from producing good parameters estimate during the construction of the classification rule. The common approach to deal with multicollinearity is feature extraction. However, the criterion used in extracting the raw features based on variances may not be appropriate for the ultimate goal of classification accuracy. Alternatively, feature selection method would be advisable as it chooses only valuable features. Two distance-based criteria in determining the right features for classification purposes, Wilk's Lambda and bounded Mahalanobis distance, are applied. Classification with features determined by bounded Mahalanobis distance statistically performs better than Wilk's Lambda. This chapter suggests that classification of e-nose with feature selection is a good choice to limit the cost of experiments and maintain good classification performance.

Integrating Smell Sense in Robots Using Artificial Neural Networks

One of the approaches to mimic the remarkable abilities of the human olfactory system is by the design of computer-controlled sensor arrays that are capable of detecting and distinguishing a different range of smells and odors with consistent monitoring, referred to as electronic noses. This chapter introduces the opportunity of integrating smell sense in robots by the use of artificial neural networks. The study proposes a structure for integrating electronic noses in robots to add the capabilities of smell-related assignments, typically to recognize hazardous substances such as sampling the air and decide its actions based on this information. Utilizing the proposed algorithm allows experts in this field to be aware of gas leakage areas and thus reduce unexpected incidences. The effectiveness of the algorithm is demonstrated using real-world samples, and the performance is examined via quantitative metrics and analysis. The results show that the proposed algorithmic framework outperforms state-of-the-art methods with an error rate of only 0.0999%.

Electronic Noses for Indoor Air Quality Assessment

This chapter presents a proposal of the use of electronic noses in the monitoring of indoor air quality. The main focus is put on the detailed characteristics of today's indoor air quality control methods, the types of pollution in the air, and the development of electronic noses for air testing. Currently, scientists seek methodological and structural solutions that would enable real-time online indoor air control. It has been shown that using electronic noses in this situation is advantageous. In addition, potential uses of these devices are discussed, with particular focus on closed food processing spaces. The authors of the chapter argue that in the near future, the proposed solution could improve the quality of indoor air and thus the health of the users of the indoor environments, as well as the quality of the products prepared there.

Electronic Noses in Food Analysis

Food quality evaluation is a tedious job as it can be sensed either by its flavor, taste, or appearance. Sensory evaluation for food quality determination is very complex and depends on biological sensor systems and is subject to high variability depending on taster's mood, physical state, etc. Moreover, as it is purely subjective, the observations may vary for different tasters. For food industries the instrumental means for quality monitoring has significant appreciations which are repeatable, accurate, and reliable. Electronic nose is an array of sensors that senses based on aroma of samples. For the last two decades, electronic nose has been applied in several domains of applications in food analysis, for example, food quality monitoring based on seasonal effect, ageing, geographical origin, fermentation, etc. This chapter aims to focus on different domains of application of electronic nose in food analysis.

Correlating Electronic Nose and Field Olfactometer for Industrial Odor Concentration Measurement Using PLS and MLR

Industrial odor concentration measurement in continuous mode is a challenging task using olfactometers, as it's expensive and requires human involvement for a prolonged time. This chapter presents the development of an indigenous metal oxide sensor-based electronic nose system for measurement of industrial odor in ou/m3. The results of electronic nose and field olfactometer were correlated using multilinear regression and partial least square regression techniques. The results showed satisfactory prediction by both the models, with RMSE (6.70, and 4.02), RAE (0.29 and 0.16), and NAE (0.89 and 0.96), respectively, for MLR and PLS. The results indicated better performance of PLS compared to MLR. The objective of the present work is to train and employ artificial olfaction system for continuous measurement of obnoxious emissions emitted from industries bypassing involvement of olfactometer.

A Framework for an Artificial-Neural-Network-Based Electronic Nose

Machine odor detection has developed into an important aspect of our lives with various applications of it. From detecting food spoilage to diagnosis of diseases, it has been developed and tested in various fields and industries for specific purposes. This project, artificial-neural-network-based electronic nose (ANNeNose), is a machine-learning-based e-nose system that has been developed for detection of various types of odors for a general purpose. The system can be trained on any odor using various e-nose sensor types. It uses artificial neural network as its machine learning algorithm along with an OMX-GR semiconductor gas sensor for collecting odor data. The system was trained and tested with five different types of odors collected through a standard data collection method and then purified, which in turn had a result varying from 93% to 100% accuracy.

Application of Electronic Nose to Ambient Air Quality Evaluation With Respect to Odour Nuisance in Vicinity of Municipal Landfills and Sewage Treatment

This chapter contains information about application of the electronic nose type instruments to evaluation of ambient air odour quality connected with such sectors of human activity as municipal landfills or sewage treatment plants. The authors present potential sources of emission from these sectors, characterize the chemical compounds responsible for presence of unpleasant odours, describe the influence of those compounds on human health and related discomfort. Legal aspects pertaining to admissible odour concentration levels in selected countries are also presented. The chapter describes instrumental and sensory methods utilized for determination of odour concentration, odour intensity, or hedonic quality. The chemical sensors potentially and currently employed in the electronic nose type devices are also characterized. Moreover, the future prospects of application of the electronic nose instruments to evaluation of ambient air with respect to odour nuisance are discussed.

Electronic Nose Applications

In this chapter, the authors introduce three typical applications of electronic nose, including the diagnosis of lung cancer and gastrointestinal diseases and food freshness detection. Firstly, an electronic nose system intended for lung cancer screening employs semiconductor and electrochemical sensors to compose an array in a chamber to conduct breath analysis with pre-concentration and multiple desorption procedures. Secondly, a design of virtual electronic nose was presented to detect the concentration of hydrogen, methane, and carbon dioxide in breath for the diagnosis of small intestinal bacterial overgrowth. Finally, combined with the metal oxide semiconductor sensors and electrochemical sensors array, the small size electronic nose system is to identify the freshness and classify different kinds of food stored in the refrigerator.