Shaochuan Li
Papers
1
Total Citations
3
H-Index
1
About
Shaochuan Li is a researcher whose work sits at the intersection of reinforcement learning and mobile robotics, with a particular focus on intelligent path planning. In his most-cited paper, "Mobile robot path planning based on Q-learning algorithm" (2019, 3 citations), Li proposed a model-free reinforcement learning approach that translates sonar sensor data into navigational decisions, enabling robots to autonomously adapt to their surroundings. This work reflects a broader interest in leveraging advances in AI—inspired by breakthroughs like AlphaGo—to solve real-world robotic challenges. While his citation count is modest, Li’s contributions are notable for their practical integration of Q-learning with sensor-based perception, offering a foundation for further research in adaptive, learning-driven navigation. His work stands as a stepping stone for students and researchers exploring how reinforcement learning can bridge the gap between theoretical algorithms and physical robot autonomy, particularly in dynamic or unknown environments.
Research Focus
Key Achievements
Top Papers
- 1Mobile robot path planning based on Q-learning algorithm3 citations · 2019