Title: Agile Robot Navigation Using (un)Conventional Sensors
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 will also be elaborated.
Bio: Erdal Kayacan received a PhD in electrical and electronic engineering at Bogazici University, Istanbul, Turkey. After finishing his post-doctoral research in KU Leuven at the Division of Mechatronics, Biostatistics and Sensors (MeBioS) in 2014, he worked at Nanyang Technological University at the School of Mechanical and Aerospace Engineering as an assistant professor for four years. He was an associate professor at Aarhus University at the Department of Engineering from 2018 to 2023. He is pursuing his career as a full professor at the Department of Electrical and Information Technology at Paderborn University, Germany. His research areas are computational intelligence methods, sliding mode control, model predictive control, mechatronics and unmanned aerial vehicles.