Yajie Wang

Papers

Total Citations

H-Index

About

Yajie Wang is a robotics researcher whose work centers on computer vision and autonomous navigation for intelligent systems. Her major contributions lie in two key areas: visual perception for humanoid robots and efficient path planning for mobile robots. In her highly cited work on Go-playing humanoid robots, Wang developed a Python-based chessboard recognition method using OpenCV, employing projection transformation for image correction and binarization for enhanced feature extraction—a practical solution for real-time game interaction. Her second influential paper tackles the inefficiencies of Rapidly-exploring Random Tree (RRT) algorithms, proposing a region-divided node generation strategy that significantly reduces randomness and path redundancy. With over 15 combined citations for these foundational works, Wang’s research demonstrates tangible impact in bridging computer vision and motion planning. Her achievements showcase a talent for solving concrete engineering challenges—from enabling robots to "see" game boards to navigating complex environments—making her work particularly relevant for students and researchers in robotics, autonomous systems, and human-robot interaction.