Zhedong Han

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About

Zhedong Han is a leading researcher in collaborative robotics, specializing in the modeling and control of industrial manipulators. His work focuses on enhancing human-robot interaction through precise force estimation and intuitive teaching methods. Han’s most influential contribution is a comprehensive friction model for collaborative robot joints that accounts for velocity, temperature, and load torque effects—a critical advancement for improving robot accuracy and safety in dynamic environments. This foundational paper has garnered 37 citations, underscoring its impact on the field. He further advanced robotic sensing with an external force estimation method using double encoders, enabling more sensitive detection of physical interactions without external sensors. Han also pioneered a current-based direct teaching approach, allowing operators to guide heavy industrial manipulators by hand using internal joint sensors, simplifying programming and reducing costs. His work bridges the gap between theoretical modeling and practical deployment, with applications in manufacturing and assistive robotics. With a growing citation record and innovations that directly address real-world challenges in robot control and human-robot collaboration, Han is a rising figure in modern robotics engineering.