Hiroomi Hikawa

Papers

Total Citations

H-Index

About

Hiroomi Hikawa’s research lies at the intersection of intelligent robotics, neural network hardware, and fuzzy control systems. He is best known for pioneering work on hardware implementations of self-organizing maps (SOMs) and feedback SOMs (FSOMs), which dramatically accelerate pattern recognition and real-time control. His 2007 paper on a hardware FSOM for mobile robot location identification demonstrated how custom parallel architectures can enable fast, on-chip learning for autonomous navigation. Hikawa also advanced visual-motor coordination through fuzzy control, proposing a hand-eye robot system that avoids complex 3D calibration by using fuzzy logic to map visual feedback directly to motion commands. His studies on grouping effects in SOM-based vector recognition further refined how unsupervised learning can be applied to image classification. While his citation counts (ranging from 4 to 6 per paper) reflect a focused, specialized audience, his contributions are significant for researchers in embedded neural systems and real-time robotics. Hikawa’s work bridges theoretical neural computation with practical hardware design, offering efficient solutions for mobile robot localization and sensor-based control.