Linjie Dong
Papers
9
Total Citations
53
H-Index
2
About
Linjie Dong is a leading researcher in intelligent robotics, specializing in autonomous inspection systems for critical infrastructure. His work spans weld seam tracking, cable trench navigation, and bridge cable defect detection, with a focus on deep learning and sensor-less force estimation. Dong’s most-cited paper, “Weld Seam Identification and Tracking of Inspection Robot Based on Deep Learning Network” (2022, 38 citations), introduces a novel deep learning approach for autonomous weld seam recognition on large spherical tanks, significantly enhancing inspection efficiency and safety. He has also advanced path planning with an improved A* algorithm for cable trench robots (2024) and developed an adjustable magnetic adsorption wall-climbing robot for tank inspection (2023). Notably, his recent work on a sensor-less guider contact force estimation method for endovascular robots (2025) demonstrates his versatility, applying robotic principles to medical guidance systems. With over 50 cumulative citations across his publications, Dong’s contributions are shaping the future of automated inspection in hazardous environments, from underground cable tunnels to bridge cables, making him a key figure in applied robotics and infrastructure maintenance.
Research Focus
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