Kenya Yoshitsugu

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About

Kenya Yoshitsugu is a leading researcher in industrial robotics, specializing in the intersection of machine learning and robotic precision. His work focuses on solving the critical challenge of positioning error in large industrial robots, a key barrier to the widespread adoption of flexible, offline programming in manufacturing. Yoshitsugu’s major contributions include pioneering the use of deep learning and random forest algorithms to predict and calibrate robot end-effector coordinates, dramatically improving accuracy without costly physical sensors. His most-cited paper, "Predicting Positioning Error and Finding Features for Large Industrial Robots Based on Deep Learning" (2021, 12 citations), demonstrates how neural networks can model complex error patterns from laser tracker data, enabling more reliable offline teaching. A second highly influential work, "Positioning Error Calibration of Industrial Robots Based on Random Forest" (2021, 11 citations), offers a robust, data-driven alternative to traditional calibration methods. Together, these studies have laid the groundwork for smarter, more adaptable automation systems, earning Yoshitsugu recognition as a key innovator in precision robotics and smart manufacturing.