Yunong Tian
Papers
9
Total Citations
138
H-Index
5
About
Yunong Tian is a leading researcher in intelligent robotics for power infrastructure inspection, with a focus on computer vision, autonomous navigation, and hybrid robot design. His work addresses critical challenges in automating the inspection and maintenance of overhead power transmission lines, combining deep learning-based object detection with novel robotic mechanisms. Tian’s most cited paper, “High-Voltage Power Transmission Tower Detection Based on Faster R-CNN and YOLO-V3” (45 citations), pioneered the use of advanced neural networks for real-time tower identification in aerial imagery. He has since advanced the field with key contributions to environment perception technologies (31 citations) and vision-based autonomous landing of hybrid robots on powerlines (24 citations), the latter representing a breakthrough in enabling robots to transition between flight and crawling on live cables. His recent work includes a passive compliance obstacle-crossing robot (18 citations) and a coiled cable-conduit-driven hyper-redundant manipulator for narrow-space operations (7 citations). With over 130 total citations and a growing portfolio of papers from 2019 to 2024, Tian’s research is instrumental in making power grid maintenance safer, more efficient, and increasingly autonomous.
Research Focus
Key Achievements
Top Papers
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- 3Vision-Based Autonomous Landing of a Hybrid Robot on a Powerline24 citations · 2022
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- 9Design and Analysis of Hybrid Robots for Powerline Inspection1 citations · 2024