Ayman El-Badawy
Papers
3
Total Citations
24
H-Index
3
About
Ayman El-Badawy is a robotics researcher whose work bridges the gap between classical control theory and modern reinforcement learning for multi-agent systems. His primary research areas include nonlinear model predictive control (MPC), multi-robot coordination, and parallel robot dynamics. El-Badawy’s most impactful contribution is his 2019 paper on quadrotor trajectory tracking using nonlinear MPC with ROS implementation, which has garnered 16 citations and demonstrates how optimal control can achieve precise aerial maneuvering. He further advanced the field by addressing overestimation bias in multi-agent deep reinforcement learning through his 2022 work on Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) for flocking control, a significant improvement over standard MADDPG approaches. Additionally, his research on computed torque control for delta parallel robots tackles the challenging nonlinear dynamics of these industrial manipulators used in pharmaceutical and electronics manufacturing. El-Badawy’s work is notable for its practical implementation focus—integrating tools like ACADO Toolkit and ROS—making his contributions directly applicable to real-world autonomous systems, from drone swarms to factory automation.
Research Focus
Key Achievements
Top Papers
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- 3Computed torque control of a prismatic-input delta parallel robot3 citations · 2022