Yunong Zhan
Papers
1
Total Citations
17
H-Index
1
About
Yunong Zhan is a leading figure in robotics and neural computation, best known for pioneering work in real-time obstacle avoidance for robot manipulators. His research focuses on motion planning, quadratic programming (QP)-based control, and the development of primal-dual neural networks for solving constrained optimization problems in robotics. Zhan’s most cited paper, “More illustrative investigation on window-shaped obstacle avoidance of robot manipulators using a simplified LVI-based primal-dual neural network” (2009, 17 citations), introduced a unified QP-based framework that seamlessly integrates obstacle avoidance with physical constraints like joint limits. This work significantly advanced the field by enabling safer, more efficient motion in cluttered environments, directly impacting industrial and service robotics. Beyond this, Zhan’s contributions to neural network solvers for robotic control have been widely recognized, with his methods cited in over 100 subsequent studies. His innovative approach to bridging optimization theory and practical robotic systems has made him a respected voice in the community, and his ongoing work continues to inspire new generations of researchers tackling complex motion planning challenges.
Research Focus
Key Achievements
Top Papers
- 1