Oguz Kedilioglu
Papers
2
Total Citations
4
H-Index
2
About
Oguz Kedilioglu’s research focuses on advancing robotic perception and motion planning, particularly for articulated and mobile robots operating in industrial environments. His work bridges computer vision and robotics, addressing critical challenges in autonomous navigation and manipulation. In his 2021 study on RGB-D-based human detection and segmentation, Kedilioglu developed methods to enable mobile robots to safely navigate dynamic industrial spaces by accurately identifying and segmenting human figures—a vital contribution to human-robot collaboration. His 2023 paper investigates factors influencing the motion planning accuracy of articulated robots, proposing the integration of objective functions to optimize parameters affecting absolute positioning precision. While still early in his career, with each paper garnering 2 citations, Kedilioglu’s research lays foundational groundwork for more reliable and context-aware robotic systems. His work is particularly relevant for industries seeking to deploy robots in unstructured environments where safety and precision are paramount. By combining perception and planning, Kedilioglu contributes to the next generation of intelligent, human-aware industrial automation.
Research Focus
Key Achievements
Top Papers
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- 2