Mohamed A. Wahby Shalaby
Papers
6
Total Citations
28
H-Index
4
About
Mohamed A. Wahby Shalaby is a robotics researcher whose work spans autonomous systems, soft robotics, and intelligent control. His most cited paper introduces a hybrid self-balancing and object-tracking robot that integrates artificial intelligence with machine vision, achieving stable two-wheeled locomotion while following targets—a contribution that has garnered 7 citations. He has also advanced soft robotics through the design and finite element analysis of a novel 3-parallel soft muscle actuator, addressing the critical need for flexible, human-safe robotic components (6 citations). Shalaby’s research further explores trajectory learning, employing principal component analysis and hidden Markov models to enable robots to acquire skills from demonstration, with papers accumulating 5 and 3 citations respectively. He has applied optimization techniques like particle swarm optimization to model and control omni-wheel robots, achieving precise, collaborative movement (5 citations). His comparative study on preprocessing trajectory learning methods (2 citations) provides a systematic framework for improving robot programming. Through these contributions, Shalaby demonstrates a commitment to making robots more adaptive, safe, and intelligent—bridging mechanical design with machine learning to push the boundaries of autonomous robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Trajectory Learning Using Principal Component Analysis5 citations · 2017
- 4
- 5
- 6