Yian Song
Papers
3
Total Citations
7
H-Index
2
About
Yian Song is a researcher advancing the safety and autonomy of mobile robotic systems, with a focus on human–robot interaction, path planning, and dynamic obstacle avoidance. In their 2024 work, Song introduced a “safety posture field framework” for mobile manipulators, which models the coupling between platform motion and arm dynamics to predict and mitigate collision risks during human–robot interaction—a critical contribution for collaborative manufacturing environments. That paper has already garnered 3 citations, signaling early impact. Song also developed a novel “Heuristic Expanding Disconnected Graph” method for mobile robot path planning, which reduces computational redundancy by intelligently expanding disconnected graph components, achieving faster planning than traditional graph-search algorithms (3 citations). Most recently, in 2025, Song proposed “TAP—time-aligned prediction,” a dynamic obstacle avoidance strategy that synchronizes prediction horizons with robot motion to improve real-time navigation in cluttered settings. Together, these works demonstrate Song’s commitment to solving core challenges in mobile robotics—safety, efficiency, and adaptability—and position them as an emerging voice in intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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