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About

Mohsen Asgari is a robotics and control systems researcher whose work centers on the dynamics, modeling, and intelligent control of robotic manipulators — particularly parallel robot architectures. His research has made meaningful contributions to both theoretical and applied robotics, spanning optimal control, trajectory tracking, and medical robotics applications. Among his most notable achievements, Asgari developed a novel medical parallel manipulator inspired by the biomechanics of cardiopulmonary resuscitation, recognizing that a rescuer's two-arm compression technique inherently forms a parallel mechanism — a creative insight that bridges clinical practice and robotics engineering. He has also pioneered work on modified Delta parallel robots, contributing original dynamics modeling and verification frameworks that have informed subsequent research in spatial parallel mechanisms. His 2020 study applying Pontryagin's Minimum Principle to optimal robotic manipulation, alongside a 2021 variational approach to determining "throw-able workspaces" beyond a robot's reachable envelope, demonstrates his range across both classical optimization theory and novel workspace analysis. Asgari has further explored adaptive neuro-fuzzy inference systems (ANFIS) as intelligent controllers for precise trajectory tracking. With citations accumulating across multiple publications, his work continues to serve as a reference point for researchers advancing parallel manipulator design and control.