Future of Smaller, Faster, More Agile, Yet Safer Aerial Robots: Challenges & Opportunities
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 on both model-based and model-free learning methods to handle various real-time aerial robot control problems. Furthermore, some state-of-the-art drone applications, e.g. autonomous drone racing will be elaborated.
Biography
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 University of Leuven (KU Leuven) at the division of mechatronics, biostatistics and sensors 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 Electrical and Computer Engineering as an associate professor and he is the Director of Artificial Intelligence in Robotics laboratory (AiR Lab). He has since published more than 140 peer-refereed book chapters, journal and conference papers in model-based and model-free control, parameter and state estimation, artificial intelligence, computer vision, motion and path planning for robots. 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. He is currently involved in a number EU projects; some of which are “Reliable AI for Marine Robotics” by Horizon 2020 -H2020-MSCA-ITN-2020, European Union and “Open Deep Learning toolkit for Robotics” by by Robotics Core Technology ICT-10-2019-2020, European Union. 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) and members of Computational Intelligence Society and Robotics and Automation Society. Since 1st Jan 2017, he is an Associate Editor of IEEE Transactions on Fuzzy Systems (TFS) and IEEE Transactions on Mechatronics (TMECH).