Shuying Wang

Beijing Union University, Southwest Jiaotong University

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About

Shuying Wang is a leading researcher in autonomous navigation and mobile robotics, with a focus on intelligent path planning and obstacle avoidance. Her work bridges the gap between classical algorithms and modern deep learning, addressing critical challenges in real-world robot motion. Wang’s highly cited 2021 review on deep reinforcement learning for path planning (21 citations) established a foundational framework for integrating reinforcement learning with sensor-based navigation, enabling robots to adapt to dynamic environments. She further advanced the field with her 2022 study on fusing improved A* and DWA algorithms (11 citations), solving the long-standing problem of low efficiency and static-only obstacle avoidance in traditional A* methods. By proposing a hybrid approach that combines global path optimization with local dynamic replanning, Wang’s contributions have directly enhanced the autonomy and safety of mobile robots in complex, unpredictable settings. Her work is widely recognized for its practical impact on autonomous vehicles, warehouse logistics, and service robotics, making her a key figure in the evolution of intelligent navigation systems.