Papers
6
Total Citations
77
H-Index
3
About
Ji Chang is a leading researcher in robotic environmental perception, with a primary focus on vibration-based terrain classification (VTC) and robotic ground classification (RGC) for wheeled and skid-steering mobile robots. His work addresses critical challenges in autonomous navigation, particularly the detection of non-geometric hazards such as uneven, soft, or slippery terrains that threaten traversal efficiency and safety. Chang’s most cited paper, “Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers” (2019, 43 citations), provides a foundational analysis of VTC techniques. He further advanced the field with “Laplacian Support Vector Machine for Vibration-Based Robotic Terrain Classification” (2020, 22 citations), introducing semi-supervised learning to improve classification accuracy. A key contribution is his pioneering work on unsupervised domain adaptation and broad feature alignment, enabling robust ground classification in dynamic, real-world environments where training and operational conditions differ. His research on frequency-temporal disagreement adaptation and online estimation of instantaneous centers of rotation for skid-steering robots has also enhanced navigation and control performance. With over 75 total citations, Chang’s innovations are essential for developing field-deployable autonomous robots capable of safe, efficient operation in complex outdoor settings.
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