Papers
6
Total Citations
29
H-Index
3
About
Mingda Ge is a robotics researcher whose work spans humanoid motion planning, multi-arm coordination, and mobile robot control. His primary research areas include real-time kinematic planning, neural network-based control, and trajectory optimization for robotic systems operating in dynamic environments. Ge’s most notable contribution is the first application of high-order differential estimation to humanoid robot motion planning, published in 2024, which has already garnered 11 citations. This work introduces a multiobjective optimization model that solves time-varying linear equations, avoiding computational bottlenecks common in traditional approaches. He also developed a self-organizing competitive neural network for kinematically synchronous planning in multi-arm robots, achieving real-time cooperative manipulation with physical coupling (6 citations). Earlier work includes a neural network-enhanced PID controller for non-holonomic wheeled mobile robots (5 citations) and vision-based joint angular-acceleration planning for redundant manipulators in dynamic environments (3 citations). Ge’s recent 2025 paper on hole attitude estimation for robot assembly addresses practical challenges in visual and force-based assembly planning. His research consistently bridges theoretical control methods with practical robotic applications, making significant strides in real-time, adaptive robot motion planning.
Research Focus
Key Achievements
Top Papers
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- 5Attitude Alignment Based on Hole Attitude Estimation in Robot Assembly2 citations · 2025
- 6A New Pressure-adsorption Climbing Robot Realized through Ducted Fan2 citations · 2020