Sina Askarinejad
Papers
1
Total Citations
2
H-Index
1
About
Sina Askarinejad’s research centers on the kinematic and dynamic identification of robotic systems, with a particular focus on parallel manipulators. His major contribution lies in advancing data-driven approaches to model complex mechanical behavior, moving beyond traditional analytical methods to improve control and accuracy. His most cited work, “Data-Driven Identification of the Jacobian Matrix of a 2-DoF Spherical Parallel Manipulator” (2019), demonstrates how empirical data can replace purely theoretical models for kinematic analysis, a crucial step for precise system control. This paper has garnered 2 citations, reflecting its niche but foundational role in the field. Askarinejad’s work is notable for bridging the gap between modeling and real-world robotic performance, offering practical solutions for parallel manipulator design and automation. His research is particularly valuable for students and engineers seeking to implement robust control strategies in robotics, emphasizing the importance of accurate system identification for effective manipulation.
Research Focus
Key Achievements
Top Papers
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