Xiaoxiao Guo
Papers
1
Total Citations
3
H-Index
1
About
Xiaoxiao Guo is a researcher advancing the frontiers of intelligent robotics through deep reinforcement learning. Her primary focus lies in developing robust path planning algorithms for mobile robots operating in complex, dynamic environments—a critical challenge for applications like large-scale structure assembly measurement and quality inspection. Guo’s most cited work introduces DPDQN-TER, an improved deep reinforcement learning approach that achieves efficient and stable navigation in obstacle-dense, changing scenarios. This method directly addresses the limitations of traditional reinforcement learning, which often struggles with real-world unpredictability. With 3 citations on this recent 2025 paper, her contributions are gaining traction among peers seeking practical, adaptive solutions for autonomous systems. Guo’s research bridges the gap between theoretical RL advances and real-world robotic deployment, offering tangible improvements in environmental adaptability and task reliability. Her work is particularly valuable for students and engineers aiming to integrate intelligent decision-making into measurement and quality inspection workflows, where precision and safety are paramount. As her citation count grows, Guo is establishing herself as a key voice in making mobile robots more capable and trustworthy partners in industrial automation.
Research Focus
Key Achievements
Top Papers
- 1