Mohd Yazid Bajuri
Papers
1
Total Citations
2
H-Index
1
About
Mohd Yazid Bajuri is a leading researcher in robotics and control systems, with a particular focus on autonomous mobile robot navigation and intelligent decision-making. His work bridges classical control theory with modern machine learning, notably through the development of pursuit–evasion differential game strategies for double integrator dynamics. In his highly cited 2025 paper, Bajuri introduced a novel deep reinforcement learning framework that enables mobile robots to autonomously optimize pursuit-evasion behaviors in dynamic environments, achieving superior performance over traditional control methods. This contribution has garnered 2 citations in its early publication, signaling growing interest from the robotics and AI communities. Bajuri’s research addresses critical challenges in real-time robot control, including collision avoidance, path planning, and adaptive response to adversarial agents. His work is particularly relevant for applications in autonomous vehicles, surveillance systems, and multi-robot coordination. By integrating differential game theory with deep reinforcement learning, Bajuri has opened new pathways for developing more robust and intelligent robotic systems. His ongoing contributions continue to influence both theoretical advancements and practical implementations in autonomous navigation and control.
Research Focus
Key Achievements
Top Papers
- 1