Gas Discrimination for Mobile Robots

Abstract

The problem addressed in this thesis is discrimination of gases with an array of partially selective gas sensors. Metal oxide gas sensors are the most common gas sensing technology since they have, compared to other gas sensing technologies, a high sensitivity to the target compounds, a fast response time, they show a good stability of the response over time and they are commercially available. One of the most severe limitation of metal oxide gas sensors is the scarce selectivity, that means that they do not respond only to the compound for which they are optimized but also to other compounds. One way to enhance the selectivity of metal oxide gas sensors is to build an array of sensors with different, and partially overlapping, selectivities and then analyze the response of the array with a pattern recognition algorithm. The concept of an array of partially selective gas sensors used together with a pattern recognition algorithm is known as an electronic nose (e-nose).In this thesis the attention is focused on e-nose applications related mobile robotics. A mobile robot equipped with an e-nose can address tasks like environmental monitoring, search and rescue operations or exploration of hazardous areas. In e-noses mounted on mobile robots the sensing array is most often directly exposed to the environment without the use of a sensing chamber. This choice is often made because of constraints in weight, costs and because the dynamic response obtained by the direct interaction of the sensors with the gas plume contains valuable information. However, this setup introduces additional challenges due to the gas dispersion that characterize natural environments. Turbulent and chaotic gas dispersal causes the array of sensors to be exposed to rapid changes in concentration that cause the sensor response to be highly dynamic and to seldom reach a steady state. Therefore the discrimination of gases has to be performed on features extracted from the dynamics of the signal. The problem is further complicated by variations in temperature and humidity, physical variables to which metal oxide gas sensors are crossensitive. For these reasons the problem of discrimination of gases when an array of sensors is directly exposed to the environment is different from when the array of sensors is in a controlled chamber.i ii This thesis is a compilation of papers whose contributions are two folded. On one side new algorithms for discrimination of gases with an array of sensors directly exposed to the environment are presented. On the other side, innovative experimental setups are proposed. These experimental setups enable the collection of high quality data that allow a better insight in the problem of discrimination of gases with mobile robots equipped with an e-nose. The algorithmic contributions start with the design and validation of a gas discrimination algorithm for gas sensors array directly exposed to the environment. The algorithm is then further developed in order to be able to run online on a robot, thereby enabling t...

Calibration of MOX gas sensors in open sampling systems based on Gaussian Processes

“…The outlet of the olfactory blender is placed on the floor 0.5 m upwind with respect to an array of 11 MOX gas sensors (5 Figaro TGS and 6 e2V MiCS) and a PID 1 . The compound selected for this experiment is ethanol which is heavier than air and, consequently, forms plumes at ground level.…”

“…INTRODUCTION Gas sensing applications often require continuous and direct exposition of gas sensors to the environment to be analyzed, since it contains useful information about the nature of the gas plume that can be used e.g. to localize the source of the gas [1]. This configuration, to which we refer as open sampling system (OSS), is the preferred solution when limitations in dimension, payload or energy consumption do not allow the adoption of a sampling system where the sensors are hosted in a chamber with controlled airflow, temperature and humidity.…”

Probabilistic gas quantification with MOX sensors in Open Sampling Systems—A Gaussian Process approach

“…Thus, they are not immediately applicable to OSS because steady state values are almost never reached [12], due to turbulent advection, which dominates gas transport in the target environments.…”

“…The most relevant source of uncertainty is the exposition of the sensors to the 3 turbulent airflow that brings the chemical compound in contact with the sensors. As a consequence, given the slow dynamics of MOX gas sensors and the rapid fluctuations in concentration due to turbulent airflow, the sensors never reach a steady state but continuously fluctuate [12]. In other words, the true concentration causes a range of different response levels due to the effects introduced by the slow transient in the response and the quick fluctuations due to turbulence.…”

Detecting Changes of a Distant Gas Source with an Array of MOX Gas Sensors

“…In an attempt to move towards realistic scenarios, we consider a single gas source that changes compound, intensity or mixture of compounds in the course of a single experiment. MOX sensors are the most common gas sensors used in OSS [10], mainly because of their commercial availability and the high sensitivity to non-hazardous compounds like alcohols (which facilitate experiments). However, MOX gas sensors suffer from long response and recovery times and, consequently, the response seldom reaches a steady state when used in an OSS.…”