Yunxiao Shan
Papers
3
Total Citations
206
H-Index
3
About
Yunxiao Shan is a leading researcher in mobile robotics, specializing in motion planning, localization, and multi-robot systems. Their most influential work, “A Fast and Efficient Double-Tree RRT*-Like Sampling-Based Planner,” with 168 citations, introduces a novel variant of the RRT* algorithm that dramatically accelerates convergence rates for high-quality path planning, addressing a critical bottleneck in sampling-based planners for real-time robotic applications. Shan also advanced autonomous navigation in “Large-Scale Navigation Method for Autonomous Mobile Robot Based on Fusion of GPS and Lidar SLAM” (30 citations), proposing a robust fusion of RTK-GPS and LiDAR SLAM to ensure seamless indoor-outdoor localization—a key challenge for field robots. More recently, Shan tackled the pressing issue of resilient multi-robot coordination in “Robust Cooperative Localization With Failed Communication and Biased Measurements” (8 citations), developing a decentralized method that maintains precision despite communication failures and sensor biases. This work is vital for deploying robot teams in GPS-denied or adversarial environments. Across these contributions, Shan’s research consistently pushes the boundaries of autonomous navigation, enabling faster, more reliable, and more adaptable robotic systems.
Research Focus
Key Achievements
Top Papers
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