Hedieh Jafarpourdavatgar
Papers
2
Total Citations
5
H-Index
2
About
Hedieh Jafarpourdavatgar is a rising researcher in autonomous navigation and robotic path planning, whose work bridges theoretical optimization with real-world kinematic constraints. Her most-cited paper, "New Design of Smooth PSO-IPF Navigator With Kinematic Constraints" (2024, 3 citations), introduces a novel hybrid approach combining Particle Swarm Optimization with an Improved Potential Field method to generate smooth, collision-free trajectories for mobile robots—directly addressing the industry-critical challenge of minimizing jerky movements for stable and efficient operation. Building on this, her 2025 paper "Geometrical Optimal Navigation and Path Planning – Bridging Theory, Algorithms, and Applications" (2 citations) expands the scope to autonomous systems including self-driving cars, surgical robots, and space rovers, integrating geometry, optimization, and machine learning to tackle navigation in dynamic environments. Though early in her career, Jafarpourdavatgar’s work demonstrates a clear trajectory toward practical, safety-critical applications, establishing her as a promising voice in the intersection of geometric optimal control and autonomous systems engineering.
Research Focus
Key Achievements
Top Papers
- 1New Design of Smooth PSO-IPF Navigator With Kinematic Constraints3 citations · 2024
- 2