Golnoush Asaeikheybari
Papers
1
Total Citations
11
H-Index
1
About
Golnoush Asaeikheybari is a researcher whose work sits at the intersection of robotics, mechanism design, and computational optimization. Her primary research focus is on the dimensional synthesis of mechanical linkages—a foundational challenge in robotics that involves determining optimal geometric parameters for desired motion paths. Her most cited work, "Dimensional synthesis of a four-bar linkage mechanism via a PSO-based Cooperative Neural Network approach" (2017, 11 citations), introduces a novel hybrid algorithm that combines Particle Swarm Optimization (PSO) with a Cooperative Neural Network (CNN). This approach addresses the mathematical complexity of synthesis problems by leveraging swarm intelligence and neural cooperation to efficiently explore design spaces. The paper has become a reference point for researchers seeking nature-inspired solutions to kinematic optimization. Asaeikheybari’s contributions demonstrate how integrating machine learning with classical mechanical design can yield powerful tools for robotics. Her work is particularly valuable for students and engineers interested in computational methods for mechanism synthesis, offering a bridge between traditional kinematics and modern AI-driven optimization.
Research Focus
Key Achievements
Top Papers
- 1