Papers
6
Total Citations
37
H-Index
3
About
Juncheng Zou’s research bridges the gap between theoretical multibody dynamics and practical human-robot interaction, with a focus on bicycle modeling, visual control, and motion prediction. His most cited work, “Development of Efficient Nonlinear Benchmark Bicycle Dynamics for Control Applications” (20 citations), introduces a symbolic method for modeling nonlinear bicycle dynamics with holonomic and nonholonomic constraints—a foundational contribution for control systems in robotics. Zou further advances this in “Symbolic Derivation of Bicycle Kinematics with Toroidal Wheels” (3 citations), deriving complex constraint equations using symbolic tools like Maple. Addressing real-world challenges, his “Predictive visual control framework of mobile robot for solving occlusion” (5 citations) and its follow-up (4 citations) propose video prediction-based methods to mitigate occlusion in visual servoing, enhancing robot autonomy. His recent work, “Simplified neural architecture for efficient human motion prediction in human-robot interaction” (2024, 3 citations), offers a streamlined approach to anticipating human movement, critical for safe collaboration. With cumulative citations exceeding 37, Zou’s research demonstrates a clear trajectory from rigorous analytical modeling to applied predictive systems, making him a notable contributor to robotics and control engineering.
Research Focus
Key Achievements
Top Papers
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- 5Symbolic derivation of bicycle kinematics with toroidal wheels3 citations · 2015
- 6Human Motion Prediction Based on Visual Tracking2 citations · 2019