Papers
6
Total Citations
210
H-Index
4
About
Tingli Su is a leading researcher at the intersection of state estimation, mobile intelligence, and agricultural robotics. Her most influential work, "The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods" (125 citations), pioneers a transformative shift from traditional model-based approaches to hybrid frameworks that integrate data-driven techniques, significantly advancing automated systems in IoT, unmanned vehicles, and robotics. Su’s 2018 paper on mobile intelligence (53 citations) tackles the grand challenge of enabling robots to move with human-like fluidity in complex environments, directly impacting autonomous cars and service robots. She further demonstrates practical innovation with a real-time deep learning system for vegetable recognition (20 citations), boosting efficiency for agricultural robots. Her contributions extend to optimal scheduling for networked control systems, addressing critical integration of sensors, controllers, and actuators. With recent work on inertial pose estimation using cascade networks, Su continues to push boundaries in aerospace and robotics navigation. Her research, spanning theory to application, has earned over 200 citations and established her as a key figure in modern automation and intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods125 citations · 2021
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- 3
- 4Optimal and Robust Scheduling for Networked Control Systems8 citations · 2018
- 5Inertial Pose Estimation Method Based on Multi-Genre Cascade Networks2 citations · 2024
- 6