Xiaowei Guo

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About

Xiaowei Guo is a leading researcher in intelligent manufacturing and robotics, with a focus on integrating digital twin technology with adaptive control systems. His most-cited work, "A modified Q-learning algorithm for robot path planning in a digital twin assembly system" (2022, 23 citations), introduces a novel reinforcement learning approach that enables robots to autonomously navigate complex assembly environments by leveraging real-time digital twin simulations. This contribution bridges the gap between virtual modeling and physical execution, significantly improving efficiency and flexibility in automated production lines. Guo’s research addresses critical challenges in Industry 4.0, particularly in optimizing robot motion planning under dynamic conditions. His work has been recognized for its practical applications in smart manufacturing, where his algorithms reduce path planning time while enhancing collision avoidance. By combining Q-learning with digital twin frameworks, Guo has advanced the field of cyber-physical production systems, offering scalable solutions for next-generation factories. His ongoing research continues to explore the synergy between artificial intelligence and digital twins, positioning him as a key contributor to the evolution of autonomous assembly systems.