Chihiro Yukawa
Papers
5
Total Citations
32
H-Index
3
About
Chihiro Yukawa is a researcher at the forefront of intelligent robotics and manufacturing automation, with a core focus on developing advanced robotic vision systems for industrial quality control. Their work uniquely integrates fuzzy inference mechanisms with Convolutional Neural Networks (CNNs) to enhance image acquisition and recognition, particularly for detecting micro-roughness and micro-convexities on arbitrary surfaces—a critical challenge in precision manufacturing. Yukawa’s most cited paper (2021, 15 citations) pioneers a fuzzy-based robot vision framework that optimizes CNN training data, directly supporting Industry 4.0’s push for automated inspection. Subsequent studies (2022, 9 and 3 citations) systematically evaluate and compare methods for vibration reduction and surface recognition, demonstrating practical improvements in robot arm movement and detection accuracy. With a growing citation footprint and a series of experimental validations, Yukawa’s contributions bridge the gap between theoretical AI and real-world manufacturing, offering scalable solutions for non-destructive testing. Their work is notable for its systematic approach to arbitrary surface analysis, positioning them as an emerging authority in intelligent robotic perception for smart factories.
Research Focus
Key Achievements
Top Papers
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- 3Design of a Robot Vision System for Microconvex Recognition3 citations · 2022
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