Qiuyuan Yang

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About

Qiuyuan Yang’s research focuses on advancing robotic navigation and environmental perception, with a particular emphasis on obstacle avoidance in complex, continuous state spaces. Her most cited work, “Research on Obstacle Avoidance Method of Robot Based on Region Location” (2022), introduces a novel three-step framework that leverages Region Proposal Networks (RPN) to localize obstacle areas, construct environmental maps, and enable adaptive path planning. This approach addresses a critical challenge in robotics—generalizing obstacle avoidance across diverse, unstructured environments—by shifting from traditional point-based detection to region-based localization. Though early in her career, Yang’s work has already garnered attention for its practical implications in autonomous systems, contributing to safer and more efficient robot movement in real-world settings. Her research bridges computer vision and robotics, offering a scalable solution for applications ranging from warehouse automation to assistive robotics. With a growing citation count, Yang is establishing herself as a promising voice in intelligent navigation, and her methodology has potential to influence future developments in dynamic obstacle handling and spatial reasoning.