Dr. Dmitriy Shutin
German Aerospace Center, Institute for Communications and Navigation.
Register NOW for the webinar: CLICK ME
The problem of exploring a dispersal of a potentially hazardous or toxic material in air using robots has a number of applications for e.g., environmental monitoring, infrastructure inspection, or civil protection, to name only a few. Especially in situations when explored substances pose a health risk to human operators, autonomous solutions are of a great interest. However, the key challenge that arises on a path towards autonomy in this context is a rather complicated dynamics of the dispersed material, coupled with specifics of spatial aperture and low temporal resolution of olfactory (chemical) sensors used for perception. While the former precludes tele-operation (or makes it rather challenging), the latter requires perception and autonomy schemes that are able to cope with very low information rate acquired through olfactory sensing. To address these challenges the proposed solution incorporates two elements that will be discussed in this talk. First, a mobile swarm of robotic sensor carriers is used to increase spatial sampling, and thus capture spatial dynamics more efficiently. Second, a prior information about the dispersal process in terms of domain-specific knowledge is used to support data processing and autonomy. Specifically, the dispersal process is modeled with an advection-diffusion partial differential equation (PDE). The advection, or plainly speaking, the wind – a dominant transport mechanism in a majority of practically relevant applications – is modeled using Navier-Stockes equations and explored with a robotic swarm. Furthermore, using a probabilistic (Bayesian) formulation of the PDE models, the resulting representation can be relaxed to additionally allow for more control over model mismatches. Using data samples collected by multiple robots, the multi-robot exploration then includes two steps: (i) a cooperative solution to an inverse problem of identifying parameters of the PDEs given measurements, and (ii) exploration – the design of an optimal sampling scheme for multiple robotic platforms. This work will describe the used models, discuss the developed probabilistic inference schemes, their advantages and limitations, as well as demonstrate their performance in simulations and in experiments.
Dr. Dmitriy Shutin (Senior Member, IEEE) received the master’s degree in computer science from Dnipropetrovsk State University, Ukraine, in 2000, and the Ph.D. degree in electrical engineering from the Graz University of Technology, Graz, Austria, in 2006.,From 2001 to 2006 and from 2006 to 2009, he was a Teaching Assistant and an Assistant Professor with the Signal Processing and Speech Communication Laboratory, Graz University of Technology. In 2009, he joined the Department of Electrical Engineering, Princeton University, where he worked as a Research Associate until 2011. In 2011, he joined the Institute of Communications and Navigation, German Aerospace Center, where he is currently a Leader of the Swarm Exploration Group.
His research interests include machine learning for signal processing, distributed information and signal processing, and swarm exploration. Dr. Shutin was a recipient of the Best Student Paper Award at the 2005 IEEE International Conference on Information, Communications and Signal Processing (ICICS). In 2009, he was awarded the Erwin Schroedinger Research Fellowship. From 2012 to 2014, he acted as a selected Advisor of German Air Navigation Service Provider within the Navigational System Panel of ICAO.
Note: The ASI webinar series are typically monthly, however, the date and timings of the upcoming talks are subject to change as per the availability of the speaker.
When and how to participate
The webinar will be broadcasted live on January 23, 2023 at 5 pm (CET) on Zoom (approx duration 1h + 30m Q&A)