Nouf Nawar Alotaibi
Papers
1
Total Citations
2
H-Index
1
About
Nouf Nawar Alotaibi is a rising researcher at the intersection of robotics, control theory, and artificial intelligence. Her work focuses on advancing autonomous navigation and decision-making in mobile robots, particularly through the integration of differential game theory and deep reinforcement learning. In her most-cited paper, "Mobile Robot Control Using Pursuit–Evasion Differential Game Strategy for Double Integrator Dynamic Control with Deep Reinforcement Learning" (2025), she proposes a novel framework that combines classical pursuit-evasion strategies with modern reinforcement learning to enable robust, real-time control in dynamic environments. This contribution addresses a critical challenge in robotics: how to equip autonomous systems with both theoretical guarantees and adaptive learning capabilities. Although early in her career, with 2 citations already accumulating for this recent work, Alotaibi’s research signals a promising trajectory in bridging control theory and machine learning. Her approach has potential applications in autonomous vehicles, surveillance, and multi-agent systems. As a researcher at the forefront of this synthesis, Alotaibi is poised to make significant contributions to the next generation of intelligent, responsive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1