Liguo Yao
Papers
6
Total Citations
30
H-Index
3
About
Liguo Yao is a rising researcher at the forefront of intelligent manufacturing and multi-robot systems, whose work is redefining how robots collaborate in dynamic industrial environments. His primary research areas span multi-robot coordination, human-robot collaborative task planning, and digital twin technologies for mechatronics. Yao’s major contributions include pioneering a subtask-learning framework for robot self-assembly in flexible manufacturing, which has garnered 12 citations and laid the groundwork for adaptive assembly lines. He has also developed a novel hybrid optimization approach for multi-step path planning in dynamic environments, enabling robots to navigate unpredictably while maintaining formation control through an innovative leader-follower model. In human-robot collaboration, Yao’s multi-objective optimization method balances assembly line efficiency with worker safety. Notably, his work on a Graph Neural Network-based digital twin lightweight method (DTL-GNN) addresses critical real-time interaction challenges in mechatronic systems, while his meta-learning approach for fault diagnosis in industrial robot motors tackles the difficult problem of sparse fault data. With six papers published in 2025 alone, Yao is rapidly building a reputation for solving practical, high-impact problems in smart manufacturing, making him a key voice in the next generation of industrial robotics research.
Research Focus
Key Achievements
Top Papers
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- 5DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network2 citations · 2025
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