Xiaoming He
Papers
2
Total Citations
104
H-Index
2
About
Xiaoming He is a leading researcher in industrial robotics and intelligent manufacturing, with a core focus on sensor reliability and autonomous motion planning. His most influential work, a 2021 study on "GAN-Based Data Augmentation Strategy for Sensor Anomaly Detection in Industrial Robots" (92 citations), addresses a critical bottleneck in automated production: the detection of sensor failures that can halt entire manufacturing lines. By pioneering the use of generative adversarial networks to synthesize realistic sensor fault data, He provided a robust solution for training anomaly detection systems where real-world failure data is scarce, directly enhancing the resilience of smart factories. More recently, his 2025 paper on "Improved RRT*-Connect Manipulator Path Planning in a Multi-Obstacle Narrow Environment" tackles the challenge of robotic arm navigation in constrained spaces. By fusing ellipsoidal subset sampling with goal-biased strategies, He’s IRRT*-Connect algorithm dramatically improves path efficiency and success rates in complex, cluttered environments. His work bridges the gap between theoretical robotics and practical industrial deployment, making him a key figure in advancing autonomous, failure-resistant manufacturing systems.
Research Focus
Key Achievements
Top Papers
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