Kaito Uehara
Papers
1
Total Citations
22
H-Index
1
About
Kaito Uehara is a leading researcher in robotics and sensor fusion, specializing in positioning accuracy for autonomous systems. His work centers on integrating inertial measurement units (IMUs), ultra-wideband (UWB) technology, and motion capture systems to enhance robot navigation in complex environments. His most cited paper, "Artificial Neural Network Approach to Guarantee the Positioning Accuracy of Moving Robots by Using the Integration of IMU/UWB with Motion Capture System Data Fusion" (2022, 22 citations), introduces a novel artificial neural network (ANN) method that fuses IMU and UWB time-of-flight measurements with OptiTrack Motion Capture System data. This approach significantly improves real-time positioning accuracy for moving robots, addressing critical challenges in indoor localization and autonomous navigation. Uehara’s contributions are pivotal for applications in robotics, automation, and smart infrastructure, where precise movement tracking is essential. His work has been recognized for bridging theoretical data fusion techniques with practical robotic systems, offering a robust solution that reduces error margins in dynamic environments. With growing citation impact, Uehara continues to advance sensor integration methodologies, making him a notable figure in the field of robotic positioning and control.
Research Focus
Key Achievements
Top Papers
- 1