Raed Alharthi
Papers
1
Total Citations
7
H-Index
1
About
Raed Alharthi is a leading researcher in robotics and artificial intelligence, with a primary focus on deep reinforcement learning for autonomous navigation. His most impactful work introduces a novel collision avoidance approach for path planning in unknown environments, leveraging reinforcement learning’s action-reward principles to overcome the limitations of traditional motion planning methods. This paper has garnered 7 citations since 2025, reflecting its timely contribution to safe, adaptive robot navigation. Alharthi’s research addresses a critical gap: existing motion planning approaches often fail in dynamic or unstructured settings, whereas his reinforcement learning-based framework enables robots to learn optimal paths in real time without prior environmental knowledge. His work has significant implications for autonomous systems, from warehouse logistics to search-and-rescue operations. By integrating deep reinforcement learning with path planning, Alharthi advances the field toward more intelligent, self-learning robots capable of navigating complex, unpredictable spaces. His contributions are shaping the future of autonomous robotics, making him a notable figure in the intersection of AI and robotic control systems.
Research Focus
Key Achievements
Top Papers
- 1