Papers
8
Total Citations
75
H-Index
5
About
Cengiz Deniz is a leading researcher in industrial robotics, specializing in automation, calibration, and human-robot interaction. His work addresses critical challenges in manufacturing and healthcare, with a focus on precision, cost-efficiency, and safety. Deniz’s most impactful contribution is a high-precision, zero-cost method for fully automatic industrial robot TCP calibration (2019, 23 citations), which enables large-scale installations without additional hardware expenses. He also developed a novel hand-eye calibration technique using absolute orientation (2017, 13 citations), simplifying robotic vision integration. In manufacturing, Deniz designed an in-line stereo-camera system for robotic spot welding quality control (2017, 13 citations), enhancing process reliability. During the COVID-19 pandemic, he created a robotic application for automatic specimen collection (2021, 9 citations), reducing infection risks for healthcare workers. His path planning for industrial robot milling (2019, 8 citations) offers a cost-effective alternative to CNC machines. Deniz also advances education with an interactive training platform for offline programming (2017, 4 citations) and community health through aroma therapy robots (2021, 3 citations). His recent work on collision avoidance for pick-and-place robots (2023, 2 citations) further demonstrates his commitment to robust, safe automation. With over 75 citations, Deniz’s innovations bridge theory and practice, making him a key figure in modern robotics.
Research Focus
Key Achievements
Top Papers
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- 3In-line stereo-camera assisted robotic spot welding quality control system13 citations · 2017
- 4A New Robotic Application for COVID-19 Specimen Collection Process9 citations · 2021
- 5Path Planning for Industrial Robot Milling Applications8 citations · 2019
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