Juanjuan Shi
Papers
2
Total Citations
18
H-Index
2
About
Juanjuan Shi is a leading researcher in intelligent fault diagnosis and robotic perception, with a focus on cross-machine domain adaptation and visual-inertial SLAM. Her most-cited work, "Metric Learning-Based Few-Shot Adversarial Domain Adaptation: A Cross-Machine Diagnosis Method for Ball Screws of Industrial Robots" (2024, 14 citations), addresses the critical challenge of unsupervised fault diagnosis across different SCARA robots. By pioneering a metric learning framework that bridges data distribution gaps, she enables reliable, data-efficient health monitoring for industrial robots—a breakthrough for predictive maintenance in manufacturing. In complementary work, her "Mobile robot localization method based on point-line feature visual-inertial SLAM algorithm" (2024, 4 citations) tackles low-light and weak-texture environments by fusing point and line features with inertial data, significantly improving localization accuracy and robustness. This innovation is vital for autonomous navigation in complex industrial settings. Shi’s contributions are shaping the next generation of resilient robotic systems, offering practical solutions for real-world deployment. Her research is widely cited by peers in mechanical engineering and robotics, underscoring its impact on both foundational theory and applied technology.
Research Focus
Key Achievements
Top Papers
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