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About

Niculae Mihai is a robotics researcher whose work centers on robot kinematics, intelligent control systems, and virtual instrumentation for robotic applications. His most significant contributions address one of robotics' enduring challenges: solving the inverse kinematics problem with precision and efficiency. His 2014 paper on assisted research methods for inverse kinematics, which has garnered 22 citations, established foundational approaches for minimizing trajectory errors amid complex redundant solutions. Building on this, Mihai pioneered the application of iterative pseudo-inverse Jacobian Neural Network Matrix techniques for controlling DC motors in robotic manipulators, earning 12 citations and demonstrating his commitment to achieving extreme joint displacement precision. A recurring theme throughout his research is the integration of LabVIEW instrumentation as a platform for both theoretical modeling and experimental validation. His 2018 virtual instrumentation library, comprising over 350 dedicated tools for kinematics, dynamics, and structural design, represents a particularly practical contribution to the robotics research community. Mihai has also explored multi-robot collaborative systems, developing general mathematical models applicable to modular robot configurations. Across his body of work, totaling approximately 50 citations, he consistently bridges computational intelligence, neural network methodologies, and hands-on engineering practice to advance robotic control systems.