Donglin Tang
Papers
2
Total Citations
6
H-Index
2
About
Donglin Tang is a researcher specializing in robotic inspection systems and intelligent defect detection for industrial infrastructure. Their work focuses on the integration of machine learning with robotic platforms—particularly wall-climbing and underwater robots—to automate hazardous inspection tasks. A key contribution is the development of machine learning algorithms for quantifying metal defects in storage tanks, as demonstrated in their 2023 paper on intelligent defect quantification using ultrasonic dry-coupling sensors mounted on wall-climbing robots. This work addresses critical safety challenges by replacing dangerous manual inspections with automated, remote sensing. Tang has also explored underwater robotics, evaluating robot structures and control systems through simulation tools like ADAMS, highlighting their versatility across terrestrial and marine environments. With over 6 citations across their most-cited papers, Tang’s research is gaining traction in the growing field of robotic non-destructive evaluation. Their work is particularly notable for bridging the gap between robotic mobility and real-time defect analysis, offering practical solutions for industries reliant on storage tanks and underwater infrastructure.
Research Focus
Key Achievements
Top Papers
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