Papers
1
Total Citations
42
H-Index
1
About
Linqi Zhang is a leading researcher in human-robot collaboration and intelligent manufacturing, with a focus on integrating augmented reality (AR) and deep reinforcement learning (DRL) to enhance industrial automation. His most-cited work, "A mixed perception-based human-robot collaborative maintenance approach driven by augmented reality and online deep reinforcement learning" (2023, 42 citations), introduces a groundbreaking framework that fuses real-time human perception with adaptive robotic decision-making. This approach enables robots to learn optimal maintenance strategies through online DRL, while AR provides intuitive visual guidance for human operators, significantly improving task efficiency and safety in complex environments. Zhang’s contributions bridge the gap between cognitive ergonomics and autonomous systems, offering scalable solutions for Industry 4.0. His research has been widely recognized for its practical impact, with citations spanning robotics, human factors, and manufacturing engineering. By advancing mixed-perception interfaces, Zhang is shaping the future of collaborative workspaces where humans and robots seamlessly interact, reducing downtime and error rates. His work continues to inspire innovations in adaptive automation and human-robot teaming.
Research Focus
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Top Papers
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