Papers
10
Total Citations
175
H-Index
7
About
Guangxi Li is a robotics and automation researcher whose work sits at the intersection of motion planning, machining robotics, and intelligent control systems. His most significant contributions center on the development of advanced trajectory and feedrate optimization methods for hybrid robots—systems combining parallel and serial mechanisms that offer unique advantages in precision manufacturing. Li's foundational work on toolpath corner smoothing, progressing from C2 to C3 continuous methods, has earned substantial recognition, with his jerk-limited feedrate scheduling approach alone accumulating 48 citations since 2022, establishing him as a leading voice in smooth, efficient robot motion for 5-DOF hybrid machining platforms. His research on singular trajectory avoidance and drive-constraint-aware feedrate scheduling further demonstrates a comprehensive approach to real-world manufacturing challenges. Beyond machining robotics, Li has shown a remarkable breadth of curiosity, contributing to diverse fields including bio-inspired microrobotics—exploring algal cells as functional microrobots for biomedical applications—and the theoretical intersection of tensor networks and neural networks. His more recent work on stiffness identification and friction stir welding robots signals a deepening focus on heavy-load industrial applications, reflecting both scientific rigor and strong engineering relevance.
Research Focus
Key Achievements
Top Papers
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- 6Robotized algal cells and their multiple functions14 citations · 2021
- 7Tensor Networks Meet Neural Networks: A Survey and Future Perspectives14 citations · 2023
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