Papers
2
Total Citations
31
H-Index
2
About
QingE Wu is a leading researcher in intelligent robotics and autonomous navigation, with a focus on enabling robots to operate safely and adaptively in complex, dynamic environments. Her work bridges the gap between theoretical path planning and practical robotic control, particularly through the development of the RRT*-Fuzzy Dynamic Window Approach (RRT*-FDWA) for collision-free path planning. This 2023 paper, which has already garnered 17 citations, addresses a critical limitation of many existing algorithms by integrating fuzzy logic with the RRT* framework to handle real-time uncertainties in dynamic settings—a key challenge for mobile robots in real-world tasks. Wu has also made significant contributions to tactile sensing and robotic manipulation. Her 2019 study on hardness recognition using semi-supervised generative adversarial networks (14 citations) pioneered a method that reduces the need for massive manually labeled datasets, a common bottleneck in deep learning-based tactile systems. By leveraging semi-supervised learning, she demonstrated how robots can more efficiently perceive material properties through touch, advancing applications in robotic forearms and prosthetics. Wu’s work is notable for its practical impact, offering scalable solutions that enhance both the autonomy and sensory intelligence of robotic systems.
Research Focus
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Top Papers
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