Hamed Safari

Papers

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About

Hamed Safari is a researcher specializing in robotics, artificial intelligence, and parallel manipulator kinematics. His work focuses on solving complex mechanical problems through intelligent computational methods, particularly in the design and control of spherical parallel manipulators (SPMs). In his most-cited study, Safari conducted a comparative analysis of neural and neuro-fuzzy networks—including back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and adaptive neuro-fuzzy inference systems—to address the direct path generation of a novel 3(RPSP)-S fully spherical parallel manipulator. This work demonstrated how artificial intelligence can effectively replace traditional analytical methods for solving direct kinematics, offering faster and more adaptable solutions for robotic motion control. With citations reaching into the broader robotics and AI communities, Safari’s contributions highlight the growing intersection of machine learning and mechanical design. His research is particularly valuable for students and engineers working on advanced robotic systems, where precision and real-time performance are critical.