About

Dr. Halis Altun is a leading researcher at the intersection of intelligent robotic welding, computer vision, and engineering education. His most impactful work centers on active vision sensing for industrial automation, where he developed pioneering seam profiling and robust butt welding finding techniques that enable intelligent robotic welding systems to achieve precise, real-time weld path detection. These contributions, published in 2016, have garnered 112 and 75 citations respectively, underscoring their significance in advancing autonomous manufacturing. Beyond industrial applications, Dr. Altun has made notable strides in hardware acceleration, implementing scale- and rotation-invariant object detection algorithms on FPGA for real-time shape recognition, as well as integrating deep neural networks with knowledge-based edge detectors for robotic pick-and-place operations. His research also extends to educational robotics, where he has explored the pedagogical effects of robotic activities on students’ perceptions of science and developed transnational lifelong learning courses in robotic systems. More recently, he has focused on classifying educational robots for preschool teaching, reflecting a commitment to broadening robotics’ impact from advanced manufacturing to early childhood education. Dr. Altun’s work bridges cutting-edge vision algorithms, hardware implementation, and transformative educational practices, making him a versatile contributor to both the technical and pedagogical dimensions of robotics.

Research Focus

Key Achievements

3
H-Index
9
Papers
210
Total Citations
23
Avg Citations/Paper
🏆 Most Cited Paper
Welding seam profiling techniques based on active vision sensing for intelligent robotic welding
112 citations · 2016
📈 Most Prolific Year: 2016 (3 Papers)
🤝 Key Collaborators: 22
🏛 Institutions: KTO Karatay University, Mevlana University, University of Turkish Aeronautical Association, Istanbul Health and Technology University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago