Nasr Abdalmanan
Papers
1
Total Citations
14
H-Index
1
About
Dr. Nasr Abdalmanan is a pioneering researcher in autonomous navigation and reinforcement learning, with a focused expertise in path planning for unknown environments. His most-cited work, "2D LiDAR Based Reinforcement Learning for Multi-Target Path Planning in Unknown Environment" (2023, 14 citations), addresses a critical gap in robotics: the inadequacy of traditional global path planning techniques in uncharted terrains. By integrating 2D LiDAR sensing with reinforcement learning, Dr. Abdalmanan developed a novel framework that enables autonomous agents to navigate and reach multiple targets without prior environmental maps—overcoming the limitations of conventional Q-learning methods, which often struggle with scalability and efficiency in dynamic settings. His contributions bridge the divide between theoretical reinforcement learning and practical robotic deployment, offering a robust solution for real-world applications like search-and-rescue or autonomous exploration. With 14 citations in just one year, his work is gaining traction among researchers in robotics and AI. Dr. Abdalmanan’s research not only advances algorithmic design but also provides a tangible pathway for deploying intelligent systems in unpredictable conditions, marking him as an emerging leader in adaptive autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1