Ziheng Pan
Papers
1
Total Citations
17
H-Index
1
About
Ziheng Pan is a researcher whose work lies at the intersection of robotics, neural networks, and motion planning, with a particular focus on real-time obstacle avoidance for robotic manipulators. In his 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), Pan tackled a critical challenge in kinematic redundancy: enabling robots to navigate dynamic environments while avoiding collisions. He proposed a unified quadratic-program (QP) formulation that integrates physical constraints—such as joint limits and collision avoidance—into a simplified linear variational inequality (LVI) based primal-dual neural network. This approach allowed for efficient, online motion planning, significantly advancing the practicality of redundant manipulators in industrial and service settings. Pan’s work is notable for bridging theoretical neural network solutions with real-world robotic applications, offering a computationally lightweight method that ensures both safety and performance. His contributions have been cited by researchers building on QP-based control and neural network solvers, underscoring his impact on the field of autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1