Wataru Oshiumi
Papers
1
Total Citations
22
H-Index
1
About
Wataru Oshiumi is a researcher at the forefront of robotics and sensor fusion, specializing in enhancing the positioning accuracy of moving robots. His most cited work, "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 inertial measurement unit (IMU) data with ultra-wideband (UWB) time-of-flight measurements and OptiTrack Motion Capture System (OptiT-MCS) data. This approach significantly improves robot localization in complex environments, addressing critical challenges in autonomous navigation. Oshiumi’s contributions lie at the intersection of machine learning and sensor integration, demonstrating how ANN-based data fusion can overcome the limitations of individual sensors, such as IMU drift or UWB signal interference. His work has practical implications for industrial robotics, drone navigation, and human-robot interaction, where precise positioning is essential. With a growing citation record, Oshiumi is recognized for advancing robust, real-time positioning solutions that push the boundaries of autonomous system reliability.
Research Focus
Key Achievements
Top Papers
- 1