Papers
2
Total Citations
4
H-Index
2
About
Yong Chang is a robotics researcher whose work focuses on improving the adaptability and perception of autonomous mobile robots, particularly in challenging, dynamic environments. His key research areas include wall-climbing robot design, compliant mechanisms, and visual simultaneous localization and mapping (SLAM). Chang’s major contribution lies in addressing two critical limitations in robotics: the inability of wall-climbing robots to navigate highly curved surfaces, and the vulnerability of traditional SLAM algorithms to moving objects. In his 2022 paper on wall-climbing robots, he proposed a novel two-stage passive compliant adsorption mechanism that uses elastic deformation to self-adapt to high-curvature walls, significantly enhancing robot mobility on non-planar surfaces. That same year, he advanced visual SLAM by developing a dynamic SLAM algorithm that fuses semantic information with geometric constraints, enabling robots to maintain accurate localization even when dynamic objects are present. While his most-cited papers currently hold 2 citations each, they represent foundational steps toward more robust and versatile robotic systems. Chang’s work is particularly notable for its practical focus on real-world deployment, offering solutions to problems that directly limit robot autonomy in industrial inspection, surveillance, and exploration tasks.
Research Focus
Key Achievements
Top Papers
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- 2