Xianyao Ng
Papers
2
Total Citations
52
H-Index
2
About
Xianyao Ng is a robotics researcher whose work focuses on the dynamic identification, modeling, and control of robotic manipulators, with a particular emphasis on safety and obstacle avoidance. His major contributions lie in two key areas: first, developing robust methods for extracting the physical dynamic parameters of robots—such as the link mass matrix—which are essential for accurate control and simulation. His 2020 paper on the KUKA LBR iiwa robot introduced a global optimization approach to retrieve these parameters, a foundational step for high-fidelity robot modeling (42 citations). Second, Ng has advanced obstacle avoidance in dynamic environments by integrating dynamic motion primitives (DMPs) with model predictive control and Kalman filtering, as detailed in his 2022 work (10 citations). This approach enhances a robot’s ability to react to moving obstacles in real time, a critical capability for safe human-robot collaboration. His research bridges theoretical modeling and practical implementation, offering tools that improve both the precision and safety of modern robotic systems. Ng’s work is particularly relevant for researchers in robot control, human-robot interaction, and autonomous systems.
Research Focus
Key Achievements
Top Papers
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