Abderrezak Said
Papers
2
Total Citations
35
H-Index
2
About
Abderrezak Said is a robotics researcher specializing in optimal trajectory planning and smooth motion generation for robotic manipulators. His work centers on the innovative application of multiquadric radial basis functions (MQ-RBFs) to solve complex kinematic constraints in robotic systems. Said’s most influential contribution, “Optimal trajectory generation method to find a smooth robot joint trajectory based on multiquadric radial basis functions” (2022), has garnered 29 citations, establishing a foundational approach for minimizing jerk and time simultaneously. Building on this, his 2025 paper introduces a multi-objective optimization framework that balances time efficiency with trajectory smoothness, further advancing the field. Said’s methods are particularly valuable for industrial robotics where precise, vibration-free motion is critical. His work stands out for combining mathematical elegance with practical applicability, offering engineers a robust tool for real-time trajectory planning. By addressing the trade-off between speed and smoothness, Said is helping to push the boundaries of what robotic manipulators can achieve in high-precision manufacturing and automation tasks.
Research Focus
Key Achievements
Top Papers
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