Hossam Hassan Ammar
Papers
22
Total Citations
451
H-Index
13
About
Hossam Hassan Ammar is a prominent robotics and control systems researcher whose work sits at the intersection of intelligent control, autonomous systems, and machine learning. His research primarily focuses on mobile robot navigation, manipulator kinematics, and advanced control strategies — areas where he has made substantial and widely recognized contributions. Ammar has pioneered the application of sophisticated control methodologies, including fractional order PID controllers, fuzzy logic systems, and bio-inspired optimization algorithms such as Gray Wolf Optimization, to solve complex real-world robotics challenges. His 2019 papers on path planning for omni-directional fighting robots and robust mobile robot path tracking have each garnered over 55 citations, underscoring their influence in the field. Notably, his deep learning approaches to kinematic modeling of both parallel and serial manipulators represent a forward-thinking fusion of modern AI with classical robotics theory. Perhaps most remarkably, Ammar extended his expertise toward humanitarian applications, developing centralized multi-agent robotic systems for COVID-19 field hospitals, demonstrating both technical versatility and societal commitment. With a cumulative citation count exceeding 350 across his top works, his research continues to shape how autonomous systems are designed, controlled, and deployed in increasingly complex environments.
Research Focus
Key Achievements
Top Papers
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- 2Robust Path Tracking of Mobile Robot Using Fractional Order PID Controller56 citations · 2019
- 3Deep Learning Based Kinematic Modeling of 3-RRR Parallel Manipulator43 citations · 2020
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