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About

Hee-Chan Song is a leading researcher in robotic manufacturing automation, with a focus on intelligent assembly, force control, and human-safe industrial robotics. His work addresses critical challenges in automating complex, traditionally manual tasks. Song’s most influential paper, “Tool path generation based on matching between teaching points and CAD model for robotic deburring” (2012, 18 citations), pioneered a method to generate precise tool paths for manipulators, enabling robots to safely perform harmful deburring operations on arbitrary-shaped parts—a key contribution to worker safety and automation flexibility. He further advanced robotic dexterity through “USB assembly strategy based on visual servoing and impedance control” (2015, 12 citations), which integrates vision and force feedback to automate high-precision connector insertion tasks, directly supporting inspection process automation. His work on “Force control based jigless assembly strategy of a unit box using dual-arm and friction” (2013) demonstrates innovative use of dual-arm robots and friction to eliminate costly jigs in final assembly steps, improving manufacturing efficiency. Collectively, Song’s research has garnered over 33 citations, establishing him as a pivotal figure in developing practical, sensor-guided robotic solutions for manufacturing automation.