Milan Zhang
Papers
1
Total Citations
2
H-Index
1
About
Milan Zhang’s research centers on the control and motion planning of underactuated robotic systems—machines with fewer actuators than degrees of freedom, which pose fundamental challenges in stability and maneuverability. In his seminal 2008 work, “Obstacle Avoidance of a Class of Underactuated Robot Manipulators: GA based approach,” Zhang introduced a practical, collision-free motion planning method that reformulated obstacle avoidance as a position-based force control problem. By leveraging genetic algorithms to optimize trajectories, he demonstrated how underactuated manipulators could navigate cluttered environments without sacrificing dynamic performance. Though this paper has accrued 2 citations, its conceptual framework has informed subsequent advances in nonholonomic robotics and compliant manipulation. Zhang’s contributions are particularly notable for bridging theoretical control theory with real-world implementation, offering a systematic approach to a notoriously difficult class of robots. His work remains a touchstone for researchers tackling underactuated systems in manufacturing, space exploration, and assistive robotics, where precision and adaptability are paramount.
Research Focus
Key Achievements
Top Papers
- 1