Abdul Rehman Khan
Papers
1
Total Citations
20
H-Index
1
About
Abdul Rehman Khan is a leading researcher in autonomous robotics and intelligent control systems, with a particular focus on deep learning architectures for real-time robotic navigation. His most impactful work introduces an optimally configured Hierarchical Parallel Gated Recurrent Unit (HP-GRU) model, enhanced by the Hyperband algorithm, to achieve precise control of wall-following robots. By leveraging GRUs—which excel at processing time-series data and overcoming the vanishing gradient problem inherent in traditional RNNs—Khan’s framework enables robots to navigate complex environments with remarkable accuracy and efficiency. This contribution, published in 2021 and garnering 20 citations, demonstrates his ability to bridge theoretical advances in neural network optimization with practical robotic applications. Khan’s research holds significant promise for autonomous systems in manufacturing, logistics, and service robotics, where reliable wall-following is essential. His work exemplifies a commitment to developing computationally efficient, high-performance models that push the boundaries of machine learning in robotics, making him a notable figure in the intersection of AI and control engineering.
Research Focus
Key Achievements
Top Papers
- 1