A Prof. Erdal Kayacan
Department of Engineering, Electrical and Computer Engineering, Aarhus University, Denmark
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.
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 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.