Papers
3
Total Citations
18
H-Index
3
About
Zhenning Yu is a pioneering researcher at the intersection of intelligent robotics and advanced materials, whose work spans autonomous navigation systems and self-healing soft devices. His most impactful contribution, "The Mobile Robot Anti-disturbance vSLAM Navigation Algorithm based on RBF Neural Network" (2019, 10 citations), addresses critical challenges in industrial automation by developing robust visual SLAM algorithms that enable Auto Guidance Vehicles to navigate complex environments with unknown disturbances—a key enabler for Industry 4.0 and intelligent manufacturing. Yu's innovative use of RBF neural networks for anti-disturbance control has provided practical solutions for flexible path optimization in industrial settings. In a groundbreaking leap into materials science, his 2024 work on "3D-Printable Elastomers for Real-Time Autonomous Self-Healing in Soft Devices" (4 citations) introduces photocurable elastomers that can mend damage without external stimuli or manual intervention, overcoming critical limitations of previous self-healing materials. His earlier research on two-wheeled robot control (2018, 4 citations) further demonstrates his expertise in handling nonlinear dynamics and road disturbances. Yu's unique ability to bridge robotics control theory with functional materials positions him as a versatile innovator advancing both autonomous systems and next-generation soft robotics.
Research Focus
Key Achievements
Top Papers
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