Naoki Oshiro

University of the Ryukyus University Hospital, University of the Ryukyus

Papers

Total Citations

H-Index

About

Naoki Oshiro’s research lies at the intersection of robotics, machine learning, and neural computation, with a particular focus on autonomous underwater vehicles (AUVs) and biologically inspired visual processing. His most-cited work, “Fuzzy controller for AUV robots based on machine learning and genetic algorithm” (2023, 6 citations), introduces an innovative control system that combines fuzzy logic with genetic algorithms to enhance the adaptive navigation of underwater robots—a contribution that addresses key challenges in autonomous exploration and environmental monitoring. Earlier in his career, Oshiro explored how self-organizing maps (SOMs) can separate visual information into position and direction, first in a 2004 paper (4 citations) and then extending the model with inhibitory connections in 2005 (3 citations). These studies offer foundational insights into how neural networks can decompose complex sensory data, mimicking biological vision systems. Though his citation counts are modest, Oshiro’s work demonstrates a consistent thread of creativity in applying computational intelligence to real-world robotic systems and neural modeling, making him a thoughtful contributor to the fields of soft computing and autonomous robotics.