Ahmed Regragui
Papers
2
Total Citations
6
H-Index
1
About
Ahmed Regragui is a researcher at the forefront of autonomous mobile robotics, with a focused expertise in intelligent navigation, trajectory planning, and vision-based control systems. His work addresses critical challenges in enabling robots to operate efficiently in large, complex, and unstructured environments. Regragui’s major contributions include the development of "Waypoint-guided trajectory planning for mobile robots using GPT-4.1 mini and ensemble learning-based action prediction," a pioneering approach that integrates large language models with ensemble machine learning to overcome the memory and computational bottlenecks of traditional path planning algorithms. This work has already garnered 5 citations, signaling its early impact in the field. Additionally, his research on "Vis-To-Nav: Visual Autonomous Navigation for Mobile Robots with a Limited Field of View" tackles the practical constraint of restricted sensor perception, advancing robust navigation under realistic conditions. Regragui’s work is notable for bridging cutting-edge AI—such as GPT models—with classical robotics challenges, offering scalable, real-time solutions for autonomous systems. His research is essential reading for students and engineers seeking to understand the next generation of intelligent, adaptive robot navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2