Muhamad Yazid Che Abdullah

About

Muhamad Yazid Che Abdullah is a researcher whose work sits at the intersection of robotics, artificial intelligence, and optimization algorithms. His primary focus lies in enhancing simultaneous localization and mapping (SLAM) systems—a critical technology for autonomous navigation in unknown environments. His most cited paper, "GA-PSO-FASTSLAM: A Hybrid Optimization Approach in Improving FastSLAM Performance" (2017, 7 citations), exemplifies his key contribution: integrating genetic algorithms (GA) and particle swarm optimization (PSO) to address the inherent weaknesses of FastSLAM, such as particle depletion and estimation drift. By fusing these metaheuristic techniques, Che Abdullah demonstrated a tangible improvement in both the accuracy and robustness of robot pose estimation and map building. While his citation count reflects a focused, emerging impact, his work is notable for bridging evolutionary computation with practical robotics challenges. This hybrid approach offers a fresh perspective for researchers seeking to optimize real-time autonomous systems, positioning Che Abdullah as a thoughtful contributor to the ongoing refinement of SLAM methodologies.

Research Focus

Key Achievements

Top Papers

1. 1
   GA-PSO-FASTSLAM: A Hybrid Optimization Approach in Improving FastSLAM Performance

   7 citations · 2017

Key Collaborators

Contact & Links