Yexing Yang
Papers
1
Total Citations
1
H-Index
1
About
Yexing Yang is a robotics researcher whose work focuses on integrating deep reinforcement learning (DRL) with advanced control strategies to achieve safe, collision-free navigation for autonomous mobile robots (AMRs) in dynamic environments. Their most-cited paper, "Collision-Free Robot Path Planning by Integrating DRL with Noise Layers and MPC" (2025), introduces a novel framework that combines DRL with noise layers and model predictive control (MPC), enabling robots to perceive complex scenarios and adapt their motion in real time. This work addresses a critical challenge in industrial automation and intelligent logistics, where AMRs must navigate unpredictable surroundings without compromising safety or efficiency. With 1 citation already, Yang’s research is gaining traction for its practical impact on real-world robotic systems. Their contributions lie at the intersection of machine learning and control theory, offering scalable solutions for autonomous navigation. Yang’s innovative approach to integrating noise layers enhances the robustness of DRL policies, marking a significant step toward more reliable and adaptive robotic systems in manufacturing, warehousing, and beyond.
Research Focus
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Top Papers
- 1