Abdurrahman bin Kamarulariffin
Papers
2
Total Citations
23
H-Index
2
About
Abdurrahman bin Kamarulariffin is a rising researcher at the forefront of autonomous mobile robotics, with a focused expertise in intelligent navigation systems. His work bridges the critical gap between traditional algorithmic approaches and modern self-learning paradigms, aiming to create truly autonomous robots capable of operating in complex, unstructured environments. His most influential contribution, "Advancements and Challenges in Mobile Robot Navigation," serves as a comprehensive roadmap for the field, surveying traditional AI, swarm intelligence, and self-learning algorithms while highlighting the immense potential of self-learning approaches. This work has already garnered 19 citations, establishing it as a key reference for researchers navigating this rapidly evolving domain. In his more recent work, "Improving Deep Reinforcement Learning Training Convergence using Fuzzy Logic," Kamarulariffin tackles a fundamental bottleneck in autonomous navigation: the slow and inefficient training convergence of deep reinforcement learning models. By integrating fuzzy logic, he proposes a novel solution to accelerate learning without relying on extensive prior knowledge. With 4 citations already, this paper signals his growing impact in developing practical, efficient solutions for real-world robotic autonomy.
Research Focus
Key Achievements
Top Papers
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- 2