Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds

Abstract

In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources.

A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments

“…The observations are colored according to their class posteriors, The z-axis corresponds to the concentration level of the observations, and the red and green circle markers indicate the positions of two gas sources respectively. of the class labels obtained, which is also useful information for other subsequent tasks like gas distribution mapping and gas source declaration for the applications in mobile robotic olfaction [32]. In summary, we demonstrate that the KmP approach is feasible and effective in gas discrimination in open environments, which is in particular helpful for those applications that supervised or semi-supervised methods can be hardly applied due to the lack of training data.…”

Using Chemical Sensors as “Noses” for Mobile Robots

“…[24], the recovery time of a metal-oxide gas sensor on the robot was reduced to 1 s by selecting a sensor with fast response and using it with a suction pump to quickly replace air samples around the sensor. Mobile robots equipped with an electronic nose system can detect a specific target gas even under the presence of other interfering gases [8,11,17,19,27,28,[38][39][40][41]. An electronic nose (or e-nose in short) consists of an array of gas sensors and a pattern classifier.…”

“…Only recently, robots equipped with a laser scanner or GPS devices have started to be used [8,25,26,28,[38][39][40][41]. Self-localization, path-planning, and obstacle avoidance capabilities of these robots enable fully autonomous operation even in cluttered environments.…”

Measuring Gas Concentration and Wind Intensity in a Turbulent Wind Tunnel with a Mobile Robot

“…The same procedure was also applied in [14] in order to identify multiple odour sources, using gas sensors which are not capable of gathering gas concentrations levels directly. Similarly, in [15] different distribution maps have been used to discriminate the location of different gas sources and in [16] a multirobot system was proposed in order to obtain gas distribution maps and develop experiments with different gas source-finding algorithms. In a similar direction, [17] proposes a methodology to generate large gas distribution maps, but in this case, by using a flying drone which also includes onboard sensors in order to measure gas concentration and wind intensity and direction.…”