DATE: WED, SEPTEMBER 9, 2020
TIME: 11:00 AM EDT (UTC -4)
Join Dr. Burgués as he demonstrates how he has built faster and more reliable MOX sensors for tracking turbulent chemical plumes.
Millions of years of evolution has aided animals and insects to develop the highly sensitive ability to track and navigate odor plumes over great distances. This behavior is integral to their survival and propagation of the species; decades of research has gone into finding a way to replicate this inate behaviour in autonomous mobile robots. And while it is accepted that animals are able to find hidden information from complex signals for odor navigation purposes, the sub-Hz bandwidth of chemical sensors largely limits the efficacy of information retrieval. Naturally, this hinders the application of mobile robots for chemical source localization tasks.
During this webinar sponsored by Aurora Scientific, Dr. Burgués will discuss how he and his team are leveraging various signal processing and machine learning techniques in order to decode the fine-scale structure of turbulent chemical plumes using low-cost chemical sensors. Specifically, he will discuss three signal processing methods they developed to improve MOX sensor dynamics, and share the experimental setups they used to test their theories. Finally, he will share data from recent experiments and elaborate on the conclusions of their studies and how robotic plume tracking technology might apply to industrial and air quality monitoring, research and more.
About the presenter:
Javier Burgués, PhD
Post Doctoral Researcher
Institute for BioEngineering of Catalonia (IBEC)
Javier Burgués received his BSc degree in Telecommunication Engineering from the University Autónoma of Madrid (2010), an MSc degree in Computer Science from the University of Southern California (2013), and the PhD degree in Engineering and Applied Sciences from the University of Barcelona (2019). He is currently a post-doc researcher at the Institute for Bioengineering of Catalonia (IBEC) in Barcelona. His main research interests include the application of signal processing and pattern recognition techniques to chemical sensor data, integration of chemical sensors into robotic platforms, and the development of bioinspired flight algorithms for localization and mapping of chemical sources.