Aydin Can
Papers
2
Total Citations
30
H-Index
2
About
Aydin Can is a robotics researcher specializing in autonomous navigation, simultaneous localization and mapping (SLAM), and nonlinear control for unmanned aerial vehicles (UAVs). His work focuses on overcoming critical challenges in quadcopter autonomy, particularly in degraded or hostile environments where conventional sensors fail. Can's most influential contribution is his 2021 paper on "Dynamics-Based Modified Fast SLAM," which has accumulated 23 citations. In this work, he developed a modified Rao-Blackwellized Particle Filter to jointly estimate inertial sensor bias and drift, significantly improving localization accuracy for quadcopters operating under sensor uncertainty. His 2022 study on discrete-time sliding mode control (DTSMC) for autonomous navigation using Hector SLAM, with 7 citations, addresses the practical implementation of robust controllers for remote sensing in nuclear environments. By integrating SLAM with advanced control theory, Can's research bridges the gap between theoretical autonomy and real-world deployment in hazardous settings. His work is particularly notable for targeting the nuclear industry's need for reliable UAV inspection systems, demonstrating a rare combination of theoretical rigor and applied engineering for safety-critical missions.
Research Focus
Key Achievements
Top Papers
- 1
- 2