Papers
2
Total Citations
8
H-Index
1
About
Imen Zaghbani is a rising researcher in the field of autonomous robotics, with a focused expertise in applying reinforcement learning to Unmanned Aerial Vehicle (UAV) navigation. Her work directly addresses the critical challenge of path planning, which is essential for enabling drones to navigate complex, three-dimensional environments safely and efficiently. Zaghbani’s major contributions include a comprehensive comparative analysis of foundational algorithms, specifically Q-Learning and SARSA, for 3D path planning—a study that has already garnered 7 citations since its publication in 2024. Building on this, she has pioneered a novel approach that integrates deep learning with dynamic reward mechanisms to overcome the limitations of traditional Q-learning, such as the need for large Q-value tables in large-scale settings. This forward-looking work, published in 2025, demonstrates her commitment to solving real-world navigation complexities. Through her research, Zaghbani is establishing herself as a key voice in the intersection of deep reinforcement learning and autonomous drone technology, providing scalable solutions for the next generation of intelligent aerial systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2