What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Electrical engineering
His primary areas of study are Control theory, Control theory, Artificial intelligence, Computer vision and Adaptive control.
His Control theory research incorporates elements of Control engineering and Impedance control.
His Control theory research includes elements of Stability, Tracking, Position, Robot and Trajectory.
His study in the field of Robotics, Mobile robot and Object also crosses realms of Calibration.
His study in the fields of Visual servoing, Image plane and Feature extraction under the domain of Computer vision overlaps with other disciplines such as Multi channel and System of measurement.
His Adaptive control research includes themes of Feature and Adaptive algorithm.
His most cited work include:
- Dynamic sliding PID control for tracking of robot manipulators: theory and experiments (228 citations)
- Qualitative test and force optimization of 3-D frictional form-closure grasps using linear programming (220 citations)
- Uncalibrated visual servoing of robots using a depth-independent interaction matrix (209 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Robot, Control theory and Control theory.
Artificial intelligence connects with themes related to Adaptive algorithm in his study.
His Computer vision study incorporates themes from Jacobian matrix and determinant, Odometry and Visual odometry.
His work on Simulation expands to the thematically related Robot.
In his study, which falls under the umbrella issue of Control theory, Teleoperation is strongly linked to Control engineering.
His Control theory research integrates issues from Control system, Matrix, Convergence, Nonlinear system and Trajectory.
He most often published in these fields:
- Artificial intelligence (42.29%)
- Computer vision (33.58%)
- Robot (30.35%)
What were the highlights of his more recent work (between 2017-2021)?
- Robot (30.35%)
- Artificial intelligence (42.29%)
- Computer vision (33.58%)
In recent papers he was focusing on the following fields of study:
Yun-Hui Liu spends much of his time researching Robot, Artificial intelligence, Computer vision, Control theory and Control theory.
His Robot research is multidisciplinary, relying on both Control engineering, Network topology, Path and Visualization.
As part of one scientific family, Yun-Hui Liu deals mainly with the area of Artificial intelligence, narrowing it down to issues related to the Position, and often Estimation theory.
His studies in Computer vision integrate themes in fields like Point, Visual odometry and Robustness.
Yun-Hui Liu has included themes like Mobile robot and Tractor in his Control theory study.
He has researched Control theory in several fields, including Lyapunov function, Singularity, Jacobian matrix and determinant, Actuator and Robot end effector.
Between 2017 and 2021, his most popular works were:
- Fourier-Based Shape Servoing: A New Feedback Method to Actively Deform Soft Objects into Desired 2-D Image Contours (50 citations)
- Iterative learning impedance control for rehabilitation robots driven by series elastic actuators (47 citations)
- Formation Control of Nonholonomic Mobile Robots Without Position and Velocity Measurements (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Electrical engineering
Robot, Artificial intelligence, Control theory, Control theory and Computer vision are his primary areas of study.
The various areas that Yun-Hui Liu examines in his Robot study include Visualization and Eye tracking.
Many of his studies on Artificial intelligence apply to Machine learning as well.
His study in Control theory is interdisciplinary in nature, drawing from both Deformation control and Mobile robot.
His Control theory study integrates concerns from other disciplines, such as Control system, Linear approximation, Continuum, Lyapunov function and Visual servoing.
His Computer vision research incorporates themes from Representation, Position and Robustness.
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