S.H.H. Zargarbashi

Isfahan University of Technology, National Academies of Sciences, Engineering, and Medicine

Papers

Total Citations

183

H-Index

About

S.H.H. Zargarbashi is a leading researcher in robotic machining and human-robot collaboration, with a career spanning foundational work in industrial robotics to cutting-edge cobotic systems. His primary research areas include robot posture optimization, dexterity analysis for machining robots, and multi-modal human-robot interaction. Zargarbashi’s major contributions center on enhancing the precision and flexibility of robotic systems. His highly cited work on posture optimization in robot-assisted machining (91 citations) established critical methods for improving toolpath accuracy and reducing vibration in industrial settings. Complementing this, his study on the Jacobian condition number as a dexterity index (77 citations) provided a rigorous mathematical framework for evaluating and maximizing robot manipulability in 6R machining robots, becoming a standard reference in the field. More recently, Zargarbashi has advanced collaborative robotics with the MuViH dataset and recognition pipeline (2025, 15 citations), a multi-view hand gesture system designed for intuitive human-robot interaction on finishing platforms. This work addresses the growing demand for flexible cobots capable of safe, adaptive collaboration with human operators. His research trajectory—from optimizing rigid industrial robots to enabling fluid human-robot teamwork—demonstrates a sustained impact on both theoretical robotics and practical manufacturing applications.