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About

Mahdi Ahmadi is a robotics researcher whose work centers on the kinematics, dynamics, and calibration of parallel robotic systems, with particular expertise in the HEXA parallel robot. His most significant contributions tackle one of robotics' enduring challenges: solving the forward kinematics problem for parallel manipulators, which resists closed-form analytical solutions due to highly nonlinear relationships between joint variables and end-effector position. Ahmadi's pioneering application of neural networks — including wavelet-based architectures — to this problem offered practical, convergence-reliable alternatives to traditional numerical methods, with his primary 2008 paper accumulating 53 citations and establishing him as a key voice in intelligent robotics solutions. Beyond kinematics, he extended his research into full dynamic modeling of the HEXA robot using Lagrangian formulation, addressing the complexity of nonlinear parallel robot dynamics that few researchers had tackled systematically. His 2014 vision-based calibration work further demonstrated methodological creativity, employing a single USB camera and chess pattern to achieve kinematic calibration without specialized instrumentation. Collectively, Ahmadi's body of work has meaningfully advanced the practical deployment of parallel robots in research and industrial settings, earning nearly 100 citations across his most recognized publications.