Papers
4
Total Citations
32
H-Index
3
About
Quan Liu is a researcher specializing in human-robot collaboration, robotic systems optimization, and intelligent control, with contributions spanning industrial automation, disassembly line systems, and distributed simulation architectures. His most influential work, "Adaptive Real-Time Similar Repetitive Manual Procedure Prediction and Robotic Procedure Generation for Human-Robot Collaboration" (2023), has garnered 17 citations and addresses the critical challenge of enabling robots to anticipate and mirror human actions in real time — a significant step toward seamless collaborative manufacturing environments. His 2019 study on mixed-model multi-robotic disassembly lines applied multi-objective evolutionary optimization to simultaneously minimize energy consumption and line length, contributing meaningfully to sustainable manufacturing and carbon emission reduction, accumulating 8 citations. Liu has also advanced wheeled mobile robot control through an RBFNN-informed adaptive sliding mode framework, demonstrating his breadth in intelligent control theory. More recently, his proposed three-layer distributed simulation architecture for heterogeneous robot collaboration (2024) reflects a forward-looking approach to scalable, networked robotic systems. Collectively, Liu's work bridges theoretical optimization and practical robotics deployment, making him a notable contributor to the evolving field of smart manufacturing and autonomous systems.
Research Focus
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