Masato Suzuki
Papers
4
Total Citations
37
H-Index
4
About
Masato Suzuki is a robotics researcher whose work spans robot calibration, computer vision, and autonomous mobile systems. His most influential contributions focus on improving the positioning accuracy of robotic arms through sophisticated calibration techniques. His 2012 paper on kinematic parameter calibration — his most cited work with 20 citations — introduced a powerful hybrid methodology combining laser tracking systems, neural networks for compensating non-geometric errors, and genetic algorithms for optimal measurement point selection, addressing a longstanding challenge in offline robot teaching where nominal kinematic models fail to account for real-world manufacturing imperfections. Earlier foundational work from 2009 laid the groundwork for this neural network-based calibration approach, while a parallel research thread explored indoor mobile robot localization using monocular vision enhanced by invisible floor markers — a creative environmental modification strategy for robust SLAM performance. More recently, Suzuki has turned his attention to sustainable robot software platforms, reflecting a broader interest in long-term deployability demonstrated through participation in challenges like the Tsukuba Challenge. Across his career, Suzuki's research consistently bridges theoretical modeling with practical engineering solutions, making his work particularly valuable for roboticists seeking reliable, real-world applicable systems.
Research Focus
Key Achievements
Top Papers
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- 4Proposal of Robot Software Platform with High Sustainability4 citations · 2020