Tingting Shu
Papers
8
Total Citations
230
H-Index
4
About
Tingting Shu is a robotics researcher whose work centers on precision control, visual servoing, and accuracy enhancement for industrial robotic systems. Her research addresses one of manufacturing's most persistent challenges: closing the gap between a robot's commanded and actual end-effector pose during dynamic operation. Shu's most influential contribution, "Dynamic Path Tracking of Industrial Robots With High Accuracy Using Photogrammetry Sensor" (2018, 111 citations), introduced a position-based visual servoing framework that leverages photogrammetry to provide real-time three-dimensional corrective feedback — a significant departure from traditional open-loop task execution. Her complementary work on online pose correction using optical coordinate measurement machines (65 citations) further demonstrated how external metrology systems can dramatically improve robot accuracy without costly hardware replacement. Across her body of work, Shu has progressively refined her control architectures, incorporating adaptive neuro-PID controllers, Kalman filtering, and adaptive iterative learning control to handle dynamic uncertainties in robot trajectories. Her more recent research extends these principles to parallel robots and multi-robot coordination in automated fiber placement — a critical aerospace manufacturing process. With over 200 total citations, Shu's contributions represent a cohesive and practically impactful research program bridging precision measurement and advanced robot control.
Research Focus
Key Achievements
Top Papers
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- 3Accuracy enhancement of industrial robots by on-line pose correction27 citations · 2017
- 4Dynamic Visual Servoing of A 6-RSS Parallel Robot Based on Optical CMM17 citations · 2021
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