Papers
2
Total Citations
7
H-Index
2
About
Xinghong Jiang is a researcher focused on advancing intelligent robotics, with particular expertise in visual recognition, positioning systems, and fault diagnosis for specialized inspection robots. In their foundational 2008 work, Jiang developed a method for intelligent robot visual recognition and positioning using a parallel-institution robot vision platform, proposing effective target recognition and positioning programs that integrated advanced image processing techniques. This work, with 4 citations, laid groundwork for practical robotic vision applications. More recently, Jiang addressed the critical challenge of reliability in tunnel inspection robots through a 2020 study introducing a T-S fuzzy fault tree analysis (FTA) method for intelligent fault diagnosis. By modeling the positioning system with T-S fuzzy FTA, Jiang demonstrated how to systematically identify and diagnose failures, enhancing the operational safety and autonomy of robots in demanding underground environments. This contribution, garnering 3 citations, showcases Jiang’s commitment to bridging theoretical fuzzy logic with real-world robotic reliability. Together, these works highlight Jiang’s sustained focus on making robots more perceptive and resilient, contributing valuable methods to the fields of robotic vision and intelligent fault diagnosis.
Research Focus
Key Achievements
Top Papers
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