Shuangyan Li
Papers
1
Total Citations
2
H-Index
1
About
Shuangyan Li has made foundational contributions to robotics motion planning, particularly in dynamic and obstacle-rich environments. Her most cited work introduces a novel algorithm—the rolling timeframe biased rapidly-exploring random tree—which enables robots to track moving targets while navigating unpredictable obstacles. By analyzing the stochastic properties of rapidly-exploring random trees, her research addresses a critical challenge in autonomous systems: maintaining real-time adaptability when both the goal and surrounding obstacles are in motion. This work, published in 2006, has garnered 2 citations, reflecting its niche but specialized impact on the field of dynamic path planning. Li’s research sits at the intersection of robotics, control theory, and probabilistic algorithms, offering practical solutions for applications ranging from autonomous vehicles to industrial manipulators. Her approach emphasizes computational efficiency and robustness, key requirements for real-world deployment. While her citation count is modest, the technical depth of her algorithm—particularly its ability to bias tree growth toward a moving goal while avoiding dynamic obstacles—demonstrates a sophisticated understanding of stochastic motion planning. For students and researchers in robotics, Li’s work serves as a valuable reference for tackling the complexities of real-time tracking in uncertain environments.
Research Focus
Key Achievements
Top Papers
- 1