Luo Yang
Papers
2
Total Citations
46
H-Index
2
About
Luo Yang is a pioneering researcher in robotics and intelligent control, with a focus on dynamic motion planning and real-time decision-making for autonomous systems. His work bridges the gap between theoretical control theory and practical robotic applications, particularly in high-speed, unpredictable environments. Yang’s most cited paper, “Ball Motion Control in the Table Tennis Robot System Using Time-Series Deep Reinforcement Learning” (2021, 37 citations), addresses a fundamental challenge in robotics: accurately predicting and intercepting a fast-moving ball by accounting for its position, linear velocity, and spin. This work demonstrates how deep reinforcement learning can enable robots to achieve near-professional-level performance in real-time. In another notable contribution, “An Efficient Approach for Stability Analysis and Parameter Tuning in Delayed Feedback Control of a Flying Robot Carrying a Suspended Load” (2019, 9 citations), Yang developed a computationally efficient method for stabilizing drones with suspended payloads—a critical problem in aerial logistics and search-and-rescue operations. By introducing a first-order time-delay model for stability region analysis, his research provides a practical framework for tuning controllers in time-critical systems. Yang’s work is highly influential in robotics, control engineering, and AI-driven automation, inspiring new approaches to adaptive, real-time control in complex, dynamic environments.
Research Focus
Key Achievements
Top Papers
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