Papers
3
Total Citations
20
H-Index
3
About
Pei Gao’s research sits at the intersection of robotics, digital twin technology, and deep learning, with a sharp focus on advancing the precision and intelligence of industrial parallel robots. These machines, widely used in food packaging and parts assembly, are the central subject of Gao’s work. A key contribution is the development of a status monitoring and positioning compensation system for digital twins of parallel robots—a 2025 paper already garnering 10 citations for addressing the gap between idealized monitoring data and real-world conditions. Gao also introduced RP-YOLOX-DL, a deep learning hybrid method that significantly improves target positioning accuracy and response time, solving longstanding inefficiencies in machine-vision-based parallel robot systems (5 citations). Further extending this line of inquiry, Gao proposed a 3D pickup estimation method using point cloud simplification and registration, enabling more reliable spatial perception (5 citations). Collectively, these contributions push the boundaries of how parallel robots perceive, adapt, and perform in dynamic industrial environments. Gao’s work is particularly notable for integrating digital twin frameworks with real-time compensation, a forward-looking approach that promises to make manufacturing systems more resilient and autonomous.
Research Focus
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Top Papers
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