Papers
15
Total Citations
210
H-Index
9
About
Ming-Yi Ju is a leading researcher in robotic manipulation and intelligent control, whose work has fundamentally advanced the safety and efficiency of human-robot collaboration. His core research areas span kinematic control, visual servoing, collision detection, and path planning for articulated and redundant manipulators. Ju’s most influential contribution is the development of a virtual torque-based inverse kinematics algorithm for redundant manipulators (43 citations), which elegantly combines analytical and numerical methods to operate seamlessly in both free and constrained workspaces. He has also pioneered neuro-fuzzy visual servoing controllers (35 citations) that address the critical challenges of feature selection and computational optimization in robot vision systems. In the domain of safety, Ju introduced a novel collision detection method using enclosed ellipsoids (27 citations), offering superior accuracy over traditional bounding ellipsoid approaches. His speed alteration strategy for multijoint robots in co-working environments (21 citations) has become a foundational approach for collision-free trajectory planning. With over 190 total citations across his publications, Ju continues to shape the future of intelligent robotics through his innovative integration of fuzzy systems, evolutionary algorithms, and geometric modeling.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Neuro-Fuzzy Visual Servoing Controller for an Articulated Manipulator35 citations · 2018
- 3A novel collision detection method based on enclosed ellipsoid27 citations · 2002
- 4Speed alteration strategy for multijoint robots in co-working environment21 citations · 2003
- 5A fuzzy CMAC learning approach to image based visual servoing system14 citations · 2021
- 6Fast and accurate collision detection based on enclosed ellipsoid14 citations · 2001
- 7
- 8
- 9Smooth path planning using genetic algorithms9 citations · 2011
- 10