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Keynote Lectures

How to Address Uncertainty in Smaller, Faster, More Agile, Yet Safer Drones?
Erdal Kayacan, Aarhus University, Denmark

Collective and Individual Decision-Making in Swarm Robotics
Sanaz Mostaghim, Otto-von-Guericke-Universität Magdeburg, Germany

 

How to Address Uncertainty in Smaller, Faster, More Agile, Yet Safer Drones?

Erdal Kayacan
Aarhus University
Denmark
 

Brief Bio
Erdal Kayacan received a Ph.D. degree in electrical and electronic engineering at Bogazici University, Istanbul, Turkey in 2011. After finishing his post-doctoral research in KU Leuven at the division of mechatronics, biostatistics and sensors (MeBioS) in 2014, he worked in Nanyang Technological University, Singapore at the School of Mechanical and Aerospace Engineering as an assistant professor for four years. Currently, he is pursuing his research at Aarhus University at the Department of Engineering as an associate professor.

He has since published more than 110 peer-refereed book chapters, journal and conference papers in model-based and model-free control, parameter and state estimation, and their robotics applications.  He has completed a number of research projects which have focused on the design and development of ground and aerial robotic systems, vision-based control techniques and artificial intelligence. Dr. Kayacan is co-writer of a course book “Fuzzy Neural Networks for Real Time Control Applications, 1st Edition Concepts, Modeling and Algorithms for Fast Learning”. He is a Senior Member of Institute of Electrical and Electronics Engineers (IEEE). Since 1st Jan 2017, he is an Associate Editor of IEEE Transactions on Fuzzy Systems and IEEE Transactions on Mechatronics.


Abstract
Request for increased, almost perfect, accuracy and efficiency of aerial robots pushes the operation to the boundaries of the performance envelope and, thus, induces a need for reliable operation at the very limits of attainable performance. The use of advanced learning algorithms, which can learn the operational dynamics online and adjust the operational parameters accordingly, might be a candidate solution to all the aforementioned problems. This talk will focus both model-based and model-free learning methods to handle various real-time aerial robot control problems.  Furthermore, due to the cost associated with data collection and training, the topics related to approaches such as transfer learning will also be mentioned to transfer knowledge between aerial robots and thereby increase the efficiency of their control. Not but not the least, some state-of-the-art drone applications, e.g. autonomous drone racing and fully autonomous cinematography system for aerial drones with the aim of letting the onboard artificial intelligence completely take over the film directing, will also be elaborated.



 

 

Collective and Individual Decision-Making in Swarm Robotics

Sanaz Mostaghim
Otto-von-Guericke-Universität Magdeburg
Germany
 

Brief Bio
Sanaz Mostaghim is a professor of computer science and head of SwarmLab at the Otto von Guericke University Magdeburg, Germany. She holds a PhD degree (2004) in electrical engineering from the University of Paderborn, Germany. Sanaz has worked as a postdoctoral fellow at ETH Zurich in Switzerland and as a lecturer at Karlsruhe Institute of Technology (KIT), Germany, where she received her habilitation degree in applied computer science. Her research interests are in the area of evolutionary multi-objective optimization and decision-making, swarm intelligence, and their applications in robotics and science. Sanaz is a member of the executive board of Informatics Germany and the head of the RoboCup team at the University of Magdeburg. She is an active member of IEEE Computational Intelligence Society (CIS) and serves as a member of the CIS Administration Committee. She is associate editor of IEEE Transactions on Evolutionary Computation and member of the editorial board of several international journals. 


Abstract
Available soon



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