Xiaoning Yang

Papers

Total Citations

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About

Xiaoning Yang’s research focuses on robotics, computer vision, and human-robot interaction, with a particular emphasis on improving industrial safety and autonomy. In his most cited work, “A Robot Collision Avoidance Method Using Kinect and Global Vision” (2017), Yang introduced a novel approach that integrates a ceiling-mounted global vision system with Kinect-based human skeleton detection. By combining the background-difference method with the Otsu algorithm, his system enables real-time obstacle recognition and collision avoidance, significantly enhancing the security of industrial robots operating alongside humans. This contribution addresses a critical challenge in collaborative robotics—ensuring safe human-robot coexistence without sacrificing efficiency. While his citation count remains modest, Yang’s work represents a practical, low-cost solution for dynamic environments, laying groundwork for more adaptive robotic systems. His research is particularly valuable for students and engineers interested in sensor fusion, real-time safety algorithms, and the deployment of vision-based technologies in manufacturing settings. Yang’s approach demonstrates how accessible hardware like Kinect can be leveraged for sophisticated robotic control, offering a scalable path toward safer automation.