Tetsuji SHIMOGAWA
Papers
1
Total Citations
8
H-Index
1
About
Tetsuji Shimogawa is a robotics researcher whose work focuses on enhancing the precision and adaptability of industrial automation systems. His primary research areas include trajectory tracking control, learning-based robotics, and the application of B-spline functions to improve robotic motion accuracy. Shimogawa’s most notable contribution is his development of a dual-process learning control algorithm, which combines Global Learning (GL) and Local Learning (LL) to significantly refine the trajectory tracking of industrial robot arms. This approach, detailed in his 2004 paper "Improvement of trajectory tracking for industrial robot arms by learning control with B-spline," has garnered 8 citations and demonstrates both simulation and experimental validation. By integrating B-spline techniques, his work addresses critical challenges in real-time error correction and repetitive motion tasks, offering a practical solution for high-precision manufacturing environments. Shimogawa’s research bridges theoretical control methods with industrial applications, making his findings valuable for engineers and researchers seeking to enhance robot performance in assembly, welding, and material handling. His contributions underscore the potential of learning-based approaches to overcome traditional limitations in robotic trajectory control.
Research Focus
Key Achievements
Top Papers
- 1