Mounir Bouhedda
Papers
1
Total Citations
2
H-Index
1
About
Mounir Bouhedda is a researcher in robotics and intelligent control systems, with a primary focus on visual servoing and neural network-based control architectures. His work addresses the challenge of enhancing manipulator robot capabilities through vision-guided control, a domain where precision and adaptive learning are critical. Bouhedda’s most cited paper, “The CMAC Neurocontroller for efficient learning in visual servoing” (2018), introduces a neural controller based on the Cerebellar Model Articulation Controller (CMAC) to improve target tracking in Cartesian space. This contribution is notable for its potential to simplify complex control problems by enabling efficient, real-time learning without requiring explicit mathematical models. While his citation count is modest, his research targets a niche yet impactful area—bridging computer vision and robotic manipulation. Bouhedda’s work is particularly relevant for students and researchers exploring bio-inspired control methods, as CMAC mimics cerebellar functions to achieve fast, adaptive responses. His efforts contribute to the broader goal of making robotic systems more autonomous and responsive in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1The CMAC Neurocontroller for efficient learning in visual servoing2 citations · 2018