Naoki Kitahara

National Institute of Technology, Maizuru College, Sanyo-Onoda City University, Tokyo University of Science

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About

Naoki Kitahara is a researcher whose work bridges the frontiers of intelligent manufacturing, mechatronics education, and multi-agent robotic systems. His most influential contribution lies in the innovative application of neural networks and fuzzy control to robotic welding. In his highly cited 2002 paper, Kitahara addressed a critical challenge in automated manufacturing: the real-time sensing and control of weld pool depth, a parameter that cannot be measured directly. By developing a neural network method to estimate this depth from observable welding-side data, he provided a practical solution that significantly enhanced the precision and autonomy of robotic welding processes. This work, which has garnered 35 citations, remains a foundational reference for researchers in adaptive welding control. Beyond this core achievement, Kitahara has also contributed to the design of experimental mechatronics education systems and the development of control architectures for multiple mobile robots. His exploration of self-control and server-supervisory control modes for swarm robotics, inspired by biological behaviors like that of *Ligia exotica*, demonstrates a forward-looking approach to intelligent distributed numerical control (DNC) systems. Through these efforts, Kitahara has advanced both the theoretical understanding and practical implementation of intelligent robotic systems.