Mengying Wang

University of Minnesota, University of Science and Technology of China

Papers

Total Citations

H-Index

About

Mengying Wang is a researcher whose work bridges bioinspired robotics, humanoid locomotion, and rehabilitation engineering. Her primary research areas include hydrodynamic wake sensing for robotic swimmers, energy-efficient gait planning for humanoid robots, and the development of safe, controllable lower-limb rehabilitation exoskeletons. Wang’s most impactful contribution is her work on detecting exotic wakes with hydrodynamic sensors (2019, 20 citations), which advances the understanding of how bioinspired robots can sense and respond to fluid disturbances—a critical capability for schooling and target-following in autonomous underwater vehicles. In humanoid robotics, she proposed an optimized, energy-efficient gait planning algorithm (2018) that uses a 3D Linear Inverted Pendulum Model (LIPM) and allowable Zero Moment Point (ZMP) regions to mimic natural human walking, improving bipedal efficiency. Her rehabilitation research (2019) introduces an exoskeleton with magnetorheological dampers and adjustable degrees of freedom, enhancing patient safety and controllability. With a growing citation record and contributions that span fundamental sensing to applied rehabilitation, Wang’s work is shaping the future of adaptive, human-centered robotic systems.