Yinghao Shan
Papers
1
Total Citations
17
H-Index
1
About
Yinghao Shan is a rising researcher in robotics and artificial intelligence, with a primary focus on intelligent motion planning and autonomous navigation. His most notable contribution is the development of an obstacle-avoidable robotic motion planning framework based on deep reinforcement learning (DRL), published in 2024 and already garnering 17 citations. This work addresses persistent challenges in robotic trajectory generation within cluttered environments, offering a universal DRL-based solution that enables robots to autonomously navigate around obstacles without pre-programmed paths. By integrating reinforcement learning with motion planning, Shan’s framework represents a significant step toward more adaptive and intelligent robotic systems, with potential applications in manufacturing, service robotics, and autonomous vehicles. His research bridges the gap between theoretical control methods and practical deployment, demonstrating how DRL can overcome limitations of traditional planning algorithms. As an early-career researcher, Shan’s work is gaining traction for its practical relevance and innovative approach, positioning him as a promising contributor to the fields of robotics and AI-driven automation.
Research Focus
Key Achievements
Top Papers
- 1