Pedram Daee
Papers
1
Total Citations
9
H-Index
1
About
Pedram Daee is a researcher whose work lies at the intersection of robotics and algorithmic motion planning, with a particular focus on enhancing the efficiency of probabilistic roadmap (PRM) methods. His most-cited paper, "A sampling algorithm for reducing the number of collision checking in probabilistic roadmaps" (2014, 9 citations), tackles a core challenge in robot motion planning: navigating narrow passages in configuration space. Daee’s key contribution is a novel sampling algorithm that significantly reduces the computational cost of collision checking, a bottleneck in PRM-based pathfinding for high-degree-of-freedom robots. By intelligently guiding sample generation, his approach improves the speed and reliability of motion planning in complex environments, offering practical benefits for autonomous systems. Though his citation count is modest, the work demonstrates a focused effort to solve a persistent problem in robotics, earning recognition among peers in the field. Daee’s research is particularly valuable for students and engineers seeking to optimize robot navigation in constrained settings, and his algorithmic insights continue to inform ongoing developments in sampling-based planning.
Research Focus
Key Achievements
Top Papers
- 1