Hadj Ahmed Abbassi
Papers
5
Total Citations
22
H-Index
4
About
Hadj Ahmed Abbassi is a robotics researcher whose work spans control systems, motion planning, and human-robot interaction. His key research areas include backstepping control augmented by neural networks for robot manipulators, stochastic optimization for wheeled mobile manipulators with under-actuated platforms, and speech-based high-level control for teleoperated robotic arms. Abbassi’s major contributions include developing a novel control approach that combines backstepping strategies with online neural networks to improve tracking performance in robot manipulators, and proposing a sub-optimal motion planner that addresses the complex problem of trajectory planning under dynamic constraints for nonholonomic platforms. His work on integrating voice commands as a high-level control mode for teleoperated manipulators has advanced accessible human-robot interfaces. With over 20 citations across his most-cited papers, Abbassi has also explored task and path planning architectures for mobile robots, contributing to autonomous navigation in explorer rovers. His research demonstrates a commitment to bridging theoretical control methods with practical robotic applications, making his work valuable for students and researchers interested in intelligent robotics and automation.
Research Focus
Key Achievements
Top Papers
- 1Backstepping Control Augmented by Neural Networks For Robot Manipulators6 citations · 2008
- 2
- 3Speech as a high level control for teleoperated manipulator arm4 citations · 2010
- 4A planning architecture for mobile robotics4 citations · 2008
- 5