Papers
2
Total Citations
11
H-Index
2
About
ChengJie Li is a robotics researcher whose work focuses on advancing human-robot interaction through direct teaching and playback methods, particularly for industrial applications. His key research areas include adaptive force control, robotic deburring systems, and path reconstruction algorithms for manufacturing automation. Li's most significant contribution is the development of a direct teaching and playback method for robotic deburring systems, which enables industrial robots to handle small-quantity batch production more effectively. His 2009 paper on this topic, which has accumulated 9 citations, presents mathematical modeling for complex workpiece treatment and uneven surface compensation. In his 2011 follow-up study (2 citations), Li proposed an innovative teaching path reconstruction algorithm that minimizes errors between reference and teaching data by introducing a time-independent data updating concept. While his citation counts are modest, Li's work addresses a critical gap in industrial robotics—making programmable automation accessible for flexible manufacturing environments. His research bridges the gap between traditional programming methods and intuitive human-guided robot teaching, contributing to the broader goal of creating more adaptable and user-friendly industrial robotic systems for small-scale production runs.
Research Focus
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Top Papers
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