Andriy Sarabakha
Nanyang Technological University, Technical University of Munich, Aarhus University
Papers
11
Total Citations
221
H-Index
7
About
Andriy Sarabakha is a leading researcher at the intersection of fuzzy logic, deep learning, and autonomous aerial robotics. His work focuses on developing intelligent control systems that enable unmanned aerial vehicles (UAVs) to navigate reliably under real-world uncertainties, such as sensor noise and dynamic environments. Sarabakha’s major contributions include pioneering input-uncertainty-sensitive nonsingleton fuzzy logic controllers for long-term quadrotor navigation (65 citations) and creating online deep fuzzy learning methods that combine expert knowledge with data-driven adaptation for nonlinear system control (48 citations). He has also advanced sim-to-real transfer learning for robust gate perception in autonomous drone racing with his PencilNet approach (18 citations), and developed the Digital Robot Judge platform (16 citations) to standardize performance benchmarking in real-world manipulation. His work on knowledge transfer between robots with similar dynamics enables high-accuracy impromptu trajectory tracking across different quadrotor platforms (16 citations). With over 200 total citations, Sarabakha’s research bridges theoretical advances in fuzzy control and deep learning with practical, deployable solutions for autonomous systems, making significant strides toward robust, uncertainty-tolerant aerial robotics.
Research Focus
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