Tetsuaki Katou
Papers
2
Total Citations
54
H-Index
2
About
Tetsuaki Katou is a pioneering researcher in robotic manipulation and sensor-based state estimation, whose work has significantly advanced the precision of industrial manipulators. His primary research areas include kinematic control, sensor fusion, and real-time end-effector sensing for robotic systems. Katou’s major contribution is the development of the **Kinematic Kalman Filter (KKF)**, a novel sensor-based state estimator that overcomes the limitations of traditional motor encoder measurements. By accounting for kinematic errors, joint flexibility, and gear mechanism uncertainties, the KKF enables accurate estimation of end-effector motion even under external disturbances. His foundational 2009 paper, "Kinematic Kalman Filter (KKF) for Robot End-Effector Sensing," has garnered 51 citations, underscoring its impact on the field. Katou further extended this concept in his 2007 work, proposing a generalized KKF to enhance real-time vision sensors for robotic control. His research bridges the gap between theoretical estimation algorithms and practical industrial applications, offering robust solutions for high-precision tasks. Katou’s work remains a cornerstone for researchers and engineers seeking to improve robot autonomy and reliability in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1Kinematic Kalman Filter (KKF) for Robot End-Effector Sensing51 citations · 2009
- 2