Papers
9
Total Citations
86
H-Index
5
About
Reda Boukezzoula’s research lies at the intersection of fuzzy systems, evidence theory, and multi-sensor information fusion, with a strong focus on real-world robotics applications. His most influential work, “Multi-sensor information fusion: Combination of fuzzy systems and evidence theory approaches in color recognition for the NAO humanoid robot” (2017, 27 citations), demonstrates his pioneering approach to integrating fuzzy logic with Dempster-Shafer theory to enhance robotic perception. This work, alongside related studies on NAO robot color recognition (2015, 6 citations; 2015, 5 citations) and 3D object instance recognition via IoT cameras (2019, 12 citations), establishes him as a key contributor to human-robot interaction and autonomous object recognition. Boukezzoula has also made significant theoretical contributions through his development of thick fuzzy sets (TFSs) and thick gradual sets (2020, 5 citations; 2021, 6 citations), offering novel frameworks for modeling uncertainty in complex environments—including underwater robotics. Earlier in his career, he advanced observer-based fuzzy adaptive control for nonlinear systems, with real-time implementation on a robot wrist (2004, 19 citations). Collectively, his work bridges theoretical innovation and practical deployment, earning over 80 citations and shaping how robots perceive and interact with uncertain, dynamic worlds.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6Color Recognition for NAO Robot Using Sugeno Fuzzy System and Evidence5 citations · 2015
- 7
- 8
- 9