Dan Paune

Universitatea Națională de Știință și Tehnologie Politehnica București

Papers

Total Citations

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About

Dan Paune is a researcher whose work bridges neural network theory and mobile robotics, with a particular focus on trajectory optimization and guidance systems. His most cited paper, "Assisted Research of the Neural Network" (2012, 13 citations), addresses a fundamental challenge in robotics: minimizing the discrepancy between a system's output and its target. In this work, Paune demonstrates how online convergence to a target can be achieved without pre-programmed paths, offering a dynamic approach to real-time robot navigation. While his citation count is modest, the conceptual contribution is significant—proposing a framework where neural networks enable adaptive, on-the-fly corrections in autonomous systems. Paune's research speaks to the growing need for intelligent, self-correcting robots in unstructured environments, and his work lays groundwork for more responsive guidance algorithms. For students and researchers exploring neural network applications in robotics, Paune's focus on convergence without predefined trajectories offers a thought-provoking entry point into adaptive control.