Papers
8
Total Citations
34
H-Index
4
About
Chadi Albitar is a robotics researcher whose work spans vision-based calibration, mobile robot control, and bio-inspired locomotion. His early contributions include developing a calibration method for vision systems using pseudo-random patterns to solve correspondence and missing-data challenges—a technique applicable to both structured lighting and camera calibration. In mobile robotics, Albitar has advanced trajectory tracking for non-holonomic and skid-steering platforms, notably applying sliding-mode control to stabilize tracking errors, with related work comparing Lyapunov and sliding-mode approaches. His more recent research focuses on legged locomotion, including central pattern generator (CPG)-based control for bipedal walking that independently modulates joint torque and stiffness, and smooth gait transitions for hexapod robots. Albitar has also explored machine learning for robotics, using Long Short-Term Memory networks to estimate leg states and Bayesian neural networks for forward kinematic prediction of parallel manipulators. His work on path planning for indoor mobile robots in partially-known environments addresses navigation through dynamic crowded spaces. With publications from 2009 to 2024, Albitar’s research demonstrates a sustained focus on improving robot autonomy, control, and perception, contributing to both theoretical foundations and practical implementations in field and service robotics.
Research Focus
Key Achievements
Top Papers
- 1Calibration of vision systems based on pseudo-random patterns7 citations · 2009
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