Gas Discrimination for Mobile Robots

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issn 1650-8580 isbn 978-91-7668-762-8

Marco Trincavelli received both his BSc degree (2003) and his MSc degree (2006) in Computer Science Engineering from the Politecnico di Milano, Milan, Italy. He additio-nally received a MSc degree in Electrical Engineering and Computer Science from the Lund Tekniska Högskola, Lund, Sweden, in 2006. In 2007–2010 he has been a graduate student at the Center for Applied Autonomous Sensor Sys-tems, Örebro University, Örebro, Sweden. During autumn 2009 he visited the Tokyo University of Agriculture and Technology as a guest researcher. In autumn 2010 he spent another period as a guest researcher at the BioCircuits Institute at the University of California, San Diego. His research interests include machine learning and artificial olfaction with particular focus on mobile robotics applications. The ability to monitor and identify gases is required in a variety of applications ranging from air pollution monitoring, food and beverage quality assessment, medical diagnosis, exploration of hazardous areas and search and rescue opera-tions. Though, most of the currently available gas sensing technologies suffer from many shortcomings like lack of selectivity (the sensor responds to more than one chemical compound), drift in the response and crossensitivity to physical variables like temperature and humidity. One of the possible solutions to overcome these limitations is to use an array of partially selective gas sensors and interpret their response using signal processing techniques and pattern recognition algorithms. The contributions presented in this thesis focus around the design of algorithms for gas identification with particular attention to mobile robotics applications.