Mabolaya Mokakabye
Papers
1
Total Citations
5
H-Index
1
About
Mabolaya Mokakabye is a researcher focused on advancing the practical application of industrial robotics, with a primary emphasis on dynamic modeling, optimal trajectory planning, and parameter identification. His most-cited work, "Robot dynamic model: freudenstein-based optimal trajectory and parameter identification" (2022), addresses a critical gap in robotics: while manufacturers provide kinematic data, an accurate dynamic model is essential for effective controller design and trajectory optimization. By introducing a Freudenstein-based approach, Mokakabye offers a novel method for identifying dynamic parameters and generating optimal trajectories, directly improving robot performance and precision. Though his citation count is still growing, this work has been recognized as a valuable contribution to the field, highlighting his potential to influence future research in robotic control and automation. Mokakabye’s research is particularly relevant for engineers and researchers seeking to bridge the gap between theoretical dynamics and real-world robotic applications.
Research Focus
Key Achievements
Top Papers
- 1