Papers
4
Total Citations
21
H-Index
3
About
Aoqi Liu is a researcher specializing in adaptive control, visual servoing, and human-robot interaction, with a focus on enhancing the autonomy and reliability of robotic manipulators. Their major contributions lie in developing resilient and uncalibrated visual servoing systems that can operate effectively despite uncertainties in kinematics, dynamics, and actuator failures. Notably, Liu’s work on "Resilient adaptive trajectory tracking control for uncalibrated visual servoing systems with unknown actuator failures" (2023, 8 citations) addresses critical challenges in real-world robotic applications, while their study on "Uncalibrated Adaptive Visual Servoing of Robotic Manipulators with Uncertainties in Kinematics and Dynamics" (2023, 7 citations) introduces a depth-independent control scheme that eliminates the need for camera calibration. Liu has also explored tremor attenuation for teleoperation using a broad learning system (2021, 4 citations), advancing human-robot synchronization. With a growing citation impact and a focus on adaptive event-triggered control (2023, 2 citations), Liu’s research is paving the way for more robust and intelligent robotic systems in uncertain environments.
Research Focus
Key Achievements
Top Papers
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