Yingying Meng

University of Science and Technology Beijing

Papers

Total Citations

H-Index

About

Yingying Meng is a researcher at the forefront of intelligent manufacturing and robotics, with a focus on integrating digital twin technology with reinforcement learning for advanced automation. Her most-cited work, "A modified Q-learning algorithm for robot path planning in a digital twin assembly system" (2022, 23 citations), introduces a novel approach that enhances robotic navigation in complex assembly environments. By combining Q-learning with digital twin simulations, Meng enables real-time, adaptive path planning that improves efficiency and reduces errors in automated production lines. This contribution is particularly significant for the development of smart factories, where digital twins serve as virtual testbeds for optimizing robot behaviors before physical deployment. Beyond this flagship study, Meng’s research spans areas such as multi-agent coordination, sensor fusion, and human-robot collaboration, all aimed at creating more flexible and resilient manufacturing systems. Her work has been recognized for bridging the gap between simulation and reality, offering practical solutions for Industry 4.0. With a growing citation record and a clear trajectory toward impactful, application-driven research, Yingying Meng is establishing herself as a key contributor to the future of intelligent automation and digital twin-enabled robotics.