Papers
2
Total Citations
7
H-Index
2
About
Yanling Chen’s research lies at the intersection of robotics, human-computer interaction (HCI), and intelligent inspection systems. Her work focuses on enhancing the efficiency and usability of robotic systems through innovative gesture recognition and autonomous navigation. In her early influential study, “The hand shape recognition of Human Computer Interaction with Artificial Neural Network” (2009, 4 citations), Chen introduced a novel approach to HCI by developing a hand shape recognition system using artificial neural networks. This work addressed the limitations of traditional gesture-based commands, which often require large, complex movements, by proposing simpler hand shapes that significantly improve interaction efficiency with robots. More recently, Chen advanced the field of autonomous robotics with her 2021 paper, “Automatic inspection method of cable tunnel in complex environment based on quadruped robot” (3 citations). Here, she developed a method for quadruped robots to autonomously inspect cable tunnels in challenging, unstructured environments, ensuring reliable performance over extended distances. This work has practical implications for infrastructure maintenance and safety. Though her citation counts are modest, Chen’s contributions are notable for their applied focus on real-world robotic challenges, bridging theoretical research with tangible engineering solutions.
Research Focus
Key Achievements
Top Papers
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