Papers
14
Total Citations
594
H-Index
7
About
Hejia Gao is a prominent robotics and control systems researcher whose work centers on flexible robotic manipulators, intelligent control strategies, and human-robot interaction. He is best known for pioneering the application of the assumed mode method (AMM) to develop dynamic models of flexible robotic systems, enabling sophisticated neural network and fuzzy logic controllers capable of precise trajectory tracking and vibration suppression. His 2018 papers on neural network control and fuzzy neural network control of flexible manipulators have garnered over 246 and 159 citations respectively, establishing him as a leading voice in adaptive control for compliant robotic systems. Gao's research extends to biologically inspired robotics, including fault-tolerant control of flexible flapping-wing aircraft, and to mobile robot path planning and grasping detection. His comprehensive two-part review series on flexible robotic manipulator systems, published in 2024, has rapidly accumulated over 75 combined citations, reflecting the field's hunger for authoritative synthesis. More recently, Gao has expanded into reinforcement learning-based admittance control for physical human-robot interaction, underscoring his commitment to safe, adaptive, and intelligent robotic systems suited for real-world deployment.
Research Focus
Key Achievements
Top Papers
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- 7A Survey on Path Planning for Mobile Robot Systems8 citations · 2023
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