Shiyuan Yang

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Total Citations

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About

Shiyuan Yang is a robotics researcher focused on advancing autonomous navigation and perception for service and transport robots. Their key research areas include obstacle detection, door-type identification, and machine learning for robotic vision. Yang’s major contributions center on developing practical, low-cost sensing methods to enable robots to operate safely and flexibly in dynamic, human-centric environments. Notably, they proposed a novel obstacle detection technique using a camera and line laser, which allows unmanned transport robots to detect unexpected obstacles—such as packages—without relying on predetermined routes, thereby reducing collision risks. This work, published in 2019, has garnered attention for its potential to enhance robotic autonomy. In 2022, Yang extended their research to home environments, introducing a machine learning method to identify door types (e.g., doorknobs vs. handles) from images, a critical step for nursing robots navigating through multiple rooms. With each of their most-cited papers accumulating 2 citations, Yang’s work is steadily building a foundation for safer, more adaptable robots in logistics and healthcare settings. Their research exemplifies the integration of computer vision and robotics to solve real-world mobility challenges.