Atsushi Kohama
Papers
3
Total Citations
100
H-Index
3
About
Atsushi Kohama is a leading researcher in robotics, with a primary focus on robot calibration, kinematic modeling, and sensor-based automation. His most influential contribution is a highly cited 2010 work (87 citations) that dramatically improves robot accuracy by combining a laser tracking system with neural networks to compensate for non-geometric errors, while also using genetic algorithms to select optimal measurement points. This work addresses a critical challenge in industrial robotics: the gap between theoretical kinematic models and real-world performance. Kohama further refined this approach in his 2009 calibration study, demonstrating how neural networks can correct residual errors after geometric parameter optimization. Beyond calibration, he has contributed to mobile robotics and SLAM (Simultaneous Localization and Mapping), developing a novel object detection and recognition method that uses template matching with SIFT features and invisible floor marks to narrow search spaces in indoor environments. His research bridges precision engineering and intelligent perception, making robots more accurate and autonomous. With a career spanning both theoretical modeling and practical implementation, Kohama’s work has significant implications for manufacturing automation, where sub-millimeter accuracy is essential.
Research Focus
Key Achievements
Top Papers
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