Li Xiangfei

Research Fellow

Phone:

Email: lixiangfei@hust.edu.cn

Academic Areas: Robotic dexterous collaborative assembly

Li Xiangfei, male, born in Qujing, Yunnan in 1990, is an Assistant Research Fellow at the School of Mechanical Science and Engineering at Huazhong University of Science and Technology (HUST). He obtained a bachelor's degree in Mechanical Engineering and Automation from Jilin University in 2012 and a doctoral degree in Mechatronic Engineering from HUST in 2020. From 2020 to 2023, he worked as a postdoctoral researcher at the School of Mechanical Science and Engineering at HUST. His main research direction is robotic dexterous collaborative assembly. At present, he has published 13 SCI papers in international journals such as IJMTM, IEEE TASE, RCIM, and 11 EI papers in international conferences and Chinese journals such as IROS, CASE, AIM, and Journal of Mechanical Engineering. He has authorized 9 national invention patents. He has received honors such as the Special-class Award for Mechanical Industry Technology Invention, the National Innovation and Entrepreneurship Excellent Postdoctoral Award, the National Postdoctoral Innovation and Entrepreneurship Competition Bronze Award, the IEEE ICARM Best Student Paper, and the National Doctoral Scholarship, etc.

Academic Degrees

2008.09-2012.07 Jilin University, Bachelor

2013.09-2020.07 Huazhong University of Science and Technology, Ph. D

Professional Experience

2020.08-2023.07, Post-Doctoral Fellow, Huazhong University of Science and Technology

2023.08-Now, Assistant Research Fellow, School of Mechanical Science and Engineering, Huazhong University of Science and Technology

Selected Publications

[1] Li Xiangfei, Zhao Huan*, Zhao Xin and Ding Han. Dual sliding mode contouring control with high accuracy contour error estimation for five-axis CNC machine tools. International Journal of Machine Tools and Manufacture, 2016, 108: 74-82.

[2] Li Xiangfei, Zhao Huan* and Ding Han. Logarithmic observation of feature depth for image-based visual servoing. IEEE Transactions on Automation Science and Engineering. 2022, 19(4): 3549-3560.

[3] Li Xiangfei, Zhao Huan*, He Xianming, Ding Han. A novel cartesian trajectory planning method by using triple NURBS curves for industrial robots. Robotics and Computer-Integrated Manufacturing, 2023, 83: 102576.

[4] Li Xiangfei, Zhao Huan*, Zhao Xin and Ding Han. Contouring compensation control based on high accuracy contour error estimation for multi-axis motion systems. International Journal of Advanced Manufacturing Technology, 2017, 93(5-8): 2263-2273.

[5] Li Xiangfei, Zhao Huan*, Zhao Xin and Ding Han. Interpolation-based contour error estimation and component-based contouring control for five-axis CNC machine tools. Science China Technological Sciences, 2018, 61(11): 1666-1678.

[6] Li Xiangfei, Huang Tao, Zhao Huan, Zhang Xiaoming, Yan Sijie, Dai Xing and Ding Han. A review of recent advances in machining techniques of complex surfaces. Science China Technological Sciences, 2022, 65(9): 1915-1939.

[7] Li Xiangfei, Luo Laizhen, Zhao Huan*, Ge Dongsheng, Ding Han. A New Inverse Kinematics Solution Method with Redundancy Parameters Optimization for Dual Mobile Manipulators. Journal of Intelligent & Robotic Systems, 2023, 108(3): 37.

[8] Zhao Huan, Yu Xi, Li Xiangfei* and Ding Han. Weighted sum of vector norms based contouring control method for five-axis CNC machine tools. Precision Engineering, 2019, 60: 93-103.

[9] Zhao Huan*, Li Xiangfei, Ge Kedi and Ding Han. A contour error definition, estimation approach and control structure for six-dimentional robotic machining tasks. Robotics and Computer-Integrated Manufacturing, 2022, 73: 102235.

[10] Zhao Xin, Zhao Huan*, Li Xiangfei and Ding Han. Path smoothing for five-axis machine tools using dual quaternion approximation with dominant points. International Journal of Precision Engineering and Manufacturing, 2017, 5: 711-720.

[11] Liang Xiuquan, Zhao Huan*, Li Xiangfei and Ding Han. Force tracking impedance control with unknown environment via an iterative learning algorithm. Science China Information Sciences, 2019, 62(5): 050215.

[12] Zhao Xin, Zhao Huan*, Wan Shaohua, Li Xiangfei and Ding Han. An analytical decoupled corner smoothing method for five-axis linear tool paths. IEEE Access, 2019, 7: 22763-22772.

[13] Li Xiangfei, Zhao Huan* and Ding Han. Real-Time Feature depth estimation for image-based visual servoing. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018: 7314-7320.

[14] Li Xiangfei, Zhao Huan*, Liu Dong, Yin Yecan and Ding Han. A New Error Model Based on Adjustable Exponential Basis for Image-Based Visual Servoing. IEEE International Conference on Automation Science and Engineering (CASE). 2022: 928-933.

[15] Li Xiangfei, Zhao Huan* and Ding Han. Kullback-Leibler divergence-based visual servoing. IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2021: 720-726.

[16] Li Xiangfei, Zhao Huan* and Ding Han. A Novel Force Oscillations Reduction Method Based on Nonlinear Tracking Differentiator for Robot Contact Operations. IEEE International Conference on Advanced Robotics and Mechatronics (ICARM), 2021: 329-334.

[17] Li Xiangfei, Zhao Huan*, Yang Jixiang and Ding Han. A high accuracy on-line contour error estimation method of five-axis machine tools. International Conference on Intelligent Robotics and Applications (ICIRA), 2015: 565-576.

Awards and Honors

[1] Special-class Award for Mechanical Industry Technology Invention（26/30），2022

[2] National Innovation and Entrepreneurship Excellent Postdoctoral Award，2021

[3] IEEE ICARM Best Student Paper（3/4），2018

[4] National Doctoral Scholarship，2017

Courses Taught

Centers/Programs

[1] Efficient and Accurate Positioning and Posture Adjusting based on Visual Servoing for Robotic Load-transferring Assembly of Large Components, Youth Program of National Natural Science Foundation of China.

[2] Robotic Removal Mechanism with Small Allowance and High-accuracy Assembly for Large-scale Components with Carbon Fiber Reinforced Thermoplastic Composites, Major Program of the National Natural Science Foundation of China.