Larbi El Bakkali
Papers
11
Total Citations
157
H-Index
7
About
Larbi El Bakkali is a robotics and automation researcher whose work centers on robot manipulator control, trajectory planning, and the kinematic design and optimization of both serial and parallel robotic systems. Over the course of his career, he has made significant contributions to applying nature-inspired and intelligent computational methods — including Ant Colony Optimization, evolutionary algorithms, and fuzzy logic — to solve complex robotics challenges. His most cited work, "Optimization of Arm Manipulator Trajectory Planning in the Presence of Obstacles by Ant Colony Algorithm" (2017, 37 citations), demonstrated the effectiveness of ACO in navigating robots through obstacle-laden environments efficiently. Complementing this, his research on parallel robot kinematics and workspace analysis — including CAD-based approaches implemented in CATIA — has provided practical tools for designing high-accuracy, high-speed robotic systems, collectively garnering over 60 citations across multiple publications. El Bakkali has also advanced intelligent control strategies, developing adaptive fuzzy PID and ANFIS controllers for manipulator positioning, reflecting a holistic approach that bridges mechanical design with sophisticated control theory. His body of work, spanning more than a decade, offers valuable insights for researchers and engineers working at the intersection of computational intelligence and robotic systems engineering.
Research Focus
Key Achievements
Top Papers
- 1
- 2Analysis and Optimum Kinematic Design of a Parallel Robot25 citations · 2017
- 3On The Workspace Optimization of Parallel Robots Based on CAD Approach20 citations · 2019
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- 93-UPU robotic mechanism performance evaluation through kinematic indexes6 citations · 2018
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