Sezgin Secil
Papers
9
Total Citations
102
H-Index
5
About
Sezgin Secil is a robotics researcher whose work spans industrial automation, human-robot interaction, and autonomous 3D reconstruction systems. His research has made meaningful contributions to two interconnected domains: ensuring safety in collaborative robotics environments and enabling robots to intelligently perceive and reconstruct their surroundings. Secil's most influential work focuses on safe human-robot interaction, with his 2021 paper on minimum distance calculation using skeletal tracking accumulating 51 citations — a strong indicator of its relevance to the growing field of collaborative robotics. His subsequent 2023 study on collision-free path planning for industrial manipulators further advances practical safety frameworks for shared workspaces. Equally significant is his body of work on autonomous 3D surface reconstruction, where he has developed methods allowing industrial robots equipped with laser profile sensors to independently capture and reconstruct unknown objects — work with direct applications in automated inspection, reverse engineering, and object recognition. His 2014 visualization framework laid early groundwork that he continued refining through multiple studies across nearly a decade. More recently, Secil has extended his expertise to unmanned aerial vehicles, proposing novel coverage path planning methods for inspecting complex structures. With over 90 cumulative citations, his research demonstrates consistent and growing impact across robotics, sensing, and automation.
Research Focus
Key Achievements
Top Papers
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- 5A robotic system for autonomous 3-D surface reconstruction of objects5 citations · 2017
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