Jing‐Rong Li
Papers
7
Total Citations
165
H-Index
4
About
Jing-Rong Li is a leading researcher in intelligent robotic manufacturing, specializing in adaptive robotic machining, human-robot collaboration, and process optimization for complex industrial tasks. His work addresses critical challenges in automating high-precision, contact-rich operations such as grinding, lapping, and assembly. Li’s most impactful contribution is a region-based toolpath generation method for robotic milling of freeform surfaces, which optimizes stiffness and has garnered 83 citations. He further advanced robotic belt grinding by developing an easy-to-grind region partitioning approach (38 citations) and an adaptive human-robot collaboration framework for complex workpieces (26 citations). His research also includes a novel force feedback model for virtual robot teaching in belt lapping (8 citations) and an adaptive force and posture control strategy for automated wiring terminal assembly (4 citations), addressing a traditionally manual, knowledge-intensive process. More recently, Li has explored graphic-enhanced collision detection for complex manufacturing environments (3 citations) and multi-objective planning for machining postures in narrow tool-accessible spaces (3 citations). Through these innovations, Li has significantly enhanced the precision, efficiency, and adaptability of robotic manufacturing systems, bridging the gap between automation and the nuanced demands of real-world industrial applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2A robotic belt grinding approach based on easy-to-grind region partitioning38 citations · 2020
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- 4A novel force feedback model for virtual robot teaching of belt lapping8 citations · 2017
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