Chaoqing Tang
Papers
3
Total Citations
9
H-Index
2
About
Chaoqing Tang is a robotics researcher specializing in visual perception, sensor recovery, and data-efficient automation for industrial applications. Their core research addresses critical challenges in visual robotics for assembly lines and pipeline inspection, particularly under data-scarce and sensor-failure conditions. Tang’s major contributions include developing robust object positioning methods that enable visual robotics to adapt to new tasks with minimal training data, overcoming partial image cropping and missing data in automatic assembly lines. They have also pioneered compressed sensing techniques for recovering sensor failures in magnetic flux leakage (MFL) sensors used by pipeline inspection robots, significantly improving the reliability of health monitoring for fossil energy pipelines. With key papers published between 2022 and 2025, including “Robust Object Positioning for Visual Robotics in Automatic Assembly Line under Data-Scarce Environments” (4 citations) and “Recovery of Partial Sensor Failure for Magnetic Flux Leakage Sensors in Pipeline Inspection Robots by Block Compressed Sensing” (3 citations), Tang’s work directly enhances safety and efficiency in critical infrastructure. Their research is particularly notable for addressing real-world industrial constraints—limited data, sensor degradation, and partial visual occlusion—making their contributions highly relevant for students and researchers working on practical robotics and sensor systems.
Research Focus
Key Achievements
Top Papers
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