Kais Bouallegue
Papers
7
Total Citations
166
H-Index
4
About
Kais Bouallegue is a leading researcher at the intersection of nonlinear dynamics, intelligent control, and mobile robotics. His work masterfully integrates chaos theory, fractal processes, and neural networks to solve critical challenges in autonomous navigation and path planning. His most influential contribution is a multi-scroll chaotic system designed for higher-coverage path planning of mobile robots, controlled via a flatness controller—a paper that has garnered 86 citations and established a new paradigm for efficient exploration. Bouallegue has also pioneered a novel class of neural networks (41 citations) and developed robust face recognition systems using bag-of-features and multi-class SVM for robotic applications (20 citations). More recently, he has advanced the field with transfer deep learning for medical image classification and integrated YOLOv4 Tiny with ROS for autonomous logistics object detection. His work on combining fractal, chaos, and neural network approaches for mobile robot path generation demonstrates his unique ability to synthesize disparate mathematical frameworks into practical robotic solutions. Through his innovative fusion of chaos engineering and control theory, Bouallegue continues to shape the future of intelligent, autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2A new class of neural networks and its applications41 citations · 2017
- 3
- 4
- 5Fractal, chaos and neural networks in path generation of mobile robot4 citations · 2020
- 6
- 7Chaos Engineering and Control in Mobile Robotics Applications3 citations · 2018