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

Sensors

Bennetts Victor Hernandez²  Erik Schaffernicht²  Victor Pomareda¹  Achim J. Lilienthal²  Santiago Marco¹ 

[1]Signal and Information Processing for Sensing Systema, Institute for Bioengineering of Catalonia, Baldiri Reixac 4-8, 08028-Barcelona, Spain

[2] E-Mails:

[3]Applied Autonomous Sensor Systems, Örebro University, Fakultetsgatan 1, 70182 Örebro, Sweden

[4] E-Mails:

关键词: environmental monitoring;    gas discrimination;    gas distribution mapping;    service robots;    open sampling systems;    PID;    metal oxide sensors;   

DOI  :  10.3390/s140917331

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.