Fazeleh Tavassolian
Papers
2
Total Citations
17
H-Index
2
About
Dr. Fazeleh Tavassolian is a researcher specializing in robotics and computational kinematics, with a particular focus on parallel robots. Her work addresses the notoriously challenging forward kinematics problem—a complex, nonlinear issue arising from the closed-loop structures of parallel manipulators. Dr. Tavassolian’s major contributions lie in developing innovative, hybrid computational methods that combine neural networks with evolutionary algorithms to solve these problems efficiently. Her 2022 paper on spatial parallel robots, which has garnered 10 citations, introduces a parallel evolutionary neural network approach that significantly improves solution accuracy and speed. Earlier, her 2018 study on a 3-PRR planar parallel robot (7 citations) demonstrated a combined neural network method to overcome the limitations of traditional analytical techniques within a defined workspace. By replacing cumbersome iterative solvers with intelligent, data-driven models, Dr. Tavassolian’s research offers practical, real-time solutions for robot control and design. Her work is highly relevant for students and engineers seeking to apply machine learning to complex mechanical systems, bridging the gap between classical robotics theory and modern artificial intelligence.
Research Focus
Key Achievements
Top Papers
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