Dianyu Yang
Papers
1
Total Citations
13
H-Index
1
About
Dr. Dianyu Yang is a rising scholar in autonomous systems and robotics, whose work centers on robust path planning and navigation under uncertainty. Her most-cited paper, "Reinforcement Learning Path Planning Method with Error Estimation" (2021, 13 citations), tackles a critical challenge in autonomous driving: planning reliable trajectories in GPS-denied environments where odometry sensors accumulate drift. By integrating reinforcement learning with explicit error estimation, Dr. Yang’s approach moves beyond traditional kinematic-only methods, enabling robots to adaptively account for sensor inaccuracies during navigation. This contribution is particularly impactful for real-world applications in underground mining, planetary exploration, and indoor delivery, where GPS signals are unavailable. While her citation count reflects an early-career trajectory, the practical significance of her work—bridging machine learning with classical control challenges—has already drawn attention from researchers addressing safety-critical autonomy. Dr. Yang’s research exemplifies how intelligent error-aware planning can enhance the reliability of autonomous systems in degraded sensing conditions, marking her as a promising voice in the next generation of robotics innovation.
Research Focus
Key Achievements
Top Papers
- 1Reinforcement Learning Path Planning Method with Error Estimation13 citations · 2021