Papers
38
Total Citations
579
H-Index
13
About
Dr. Akif Durdu is a leading researcher in robotics, unmanned aerial vehicles (UAVs), and intelligent control systems, whose work bridges theoretical innovation with real-world application. His most significant contributions lie in path planning and autonomous navigation, where he developed the GDRRT* algorithm and its optimized variants, PSO-GDRRT* and BiLSTM-PSO-GDRRT*, achieving 70 citations for advancing goal-distance-based UAV path planning. In sensor fusion, his deep learning hybrid visual-inertial odometry approach, HVIOnet, has garnered 51 citations for enhancing UAV position estimation. Durdu has also made impactful strides in computer vision, with a comparative study on CNN and HOG for occlusion handling in human tracking (60 citations), and in robust control, where his sliding mode control methods for DC motor speed and position tracking have accumulated over 76 citations collectively. His work on hierarchical wireless drone networks and six-legged spider robot walking algorithms further demonstrates his versatility. With a total of over 400 citations across his top publications, Dr. Durdu’s research is foundational for students and engineers seeking to understand cutting-edge autonomous systems, offering both theoretical depth and practical frameworks for navigation, control, and perception.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Speed Control of a DC Motor with Variable Load Using Sliding Mode Control56 citations · 2016
- 4
- 5Comparison of optimal path planning algorithms46 citations · 2018
- 6Path Planning Algorithms for Unmanned Aerial Vehicles31 citations · 2019
- 7
- 8Design of Six Legged Spider Robot and Evolving Walking Algorithms23 citations · 2015
- 9The YTU dataset and recurrent neural network based visual-inertial odometry20 citations · 2021
- 10