About

Gang Zheng is a leading researcher in soft robotics, with a focus on sensor fusion, modeling, and control of compliant robotic systems. His major contributions include pioneering a model-based sensor fusion approach for force and shape estimation in soft robotics (80 citations), which integrates soft capacitive and pneumatic sensing to estimate contact location and force intensity. He has also advanced the field with robust neural network control of silicone soft robots (51 citations) and the development of a Piecewise Linear Strain Cosserat Model for soft slender manipulators (40 citations), addressing the challenge of nonlinear dynamic modeling. Zheng’s work on real-time trajectory planning and tracking control of bionic underwater robots (47 citations) demonstrates his impact in dynamic environments. His notable achievements include avoiding local minima in potential field methods using input-to-state stability (43 citations) and applying finite-element method (FEM)-based gain-scheduling control for soft trunk robots (35 citations). With over 400 total citations from his top ten papers, Zheng’s research is instrumental in enabling safer human-machine interaction and advancing the design and controllability of soft robots, making him a key figure in the field.

Research Focus

Key Achievements

16
H-Index
53
Papers
777
Total Citations
15
Avg Citations/Paper
🏆 Most Cited Paper
A Model-Based Sensor Fusion Approach for Force and Shape Estimation in Soft Robotics
80 citations · 2020
📈 Most Prolific Year: 2024 (7 Papers)
🤝 Key Collaborators: 79
🏛 Institutions: Université de Lille, Institut national de recherche en sciences et technologies du numérique, Centre de Recherche en Informatique, Centre National de la Recherche Scientifique, Foshan University, Centre Inria de l'Université de Lille

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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