Ayad Abdulrahem Alabdalbari
Papers
1
Total Citations
11
H-Index
1
About
Ayad Abdulrahem Alabdalbari is a researcher whose work lies at the intersection of robotics, artificial intelligence, and optimization algorithms. His primary focus is on mobile robot path planning, a critical challenge in autonomous navigation. Alabdalbari’s most notable contribution is the development of a hybrid Grey Wolf Optimizer and Particle Swarm Optimization (GWO-PSO) algorithm, which significantly improves path planning in static environments by generating collision-free, efficient trajectories. This work, published in 2022, has already garnered 11 citations, reflecting its relevance and impact in the field. By combining the exploration capabilities of GWO with the exploitation strengths of PSO, Alabdalbari addresses a core problem in robotics: balancing computational efficiency with path optimality. His research not only advances theoretical understanding but also offers practical solutions for autonomous systems, from warehouse robots to exploratory drones. Alabdalbari’s contributions are particularly valuable for students and researchers seeking robust, nature-inspired approaches to real-world navigation challenges, marking him as an emerging voice in optimization-driven robotics.
Research Focus
Key Achievements
Top Papers
- 1New robot path planning optimization using hybrid GWO-PSO algorithm11 citations · 2022