Papers
6
Total Citations
83
H-Index
4
About
Xiangjie Kong is a leading researcher in the field of robotic inspection and condition assessment for critical water infrastructure. His work focuses on developing autonomous systems and deep learning techniques to enhance the reliability and safety of underground water distribution networks. Dr. Kong’s major contributions include the creation of an automated defect-detection system for water pipelines using CCTV inspection videos from autonomous robotic platforms, a method that has garnered 31 citations for its practical impact. He has also pioneered valve detection algorithms using deep neural networks, with his 2021 paper on the subject receiving 29 citations, and his earlier 2020 work laying the foundation for this approach. Beyond pipeline inspection, Dr. Kong has explored the kinematic control of cable-driven continuum robots using stretchable capacitive sensors, a 2024 study with 6 citations that advances soft robotics. His notable achievements include integrating robotic RFEC/TC technology for non-destructive testing of transmission mains, as demonstrated in his 2007 work. With a portfolio of highly cited papers, Dr. Kong’s research is instrumental in modernizing water utility maintenance, reducing human error, and enabling proactive infrastructure management.
Research Focus
Key Achievements
Top Papers
- 1
- 2Valve Detection for Autonomous Water Pipeline Inspection Platform29 citations · 2021
- 3Water pipe valve detection by using deep neural networks12 citations · 2020
- 4
- 5Analysis of Key Cognitive Factors in Space Teleoperation Task3 citations · 2019
- 6