Papers
1
Total Citations
4
H-Index
1
About
Ehsan Ehsan is a researcher whose work lies at the intersection of robotics, motion planning, and probabilistic search algorithms. His most notable contribution is in the domain of pursuit-evasion on graphs, where he developed a probabilistic approach to locate and track an adversarial, mobile evader within indoor environments. By leveraging the motion planning of mobile pursuers, his work provides a systematic method to search a target and clear a workspace using graph-based representations. The core of his approach relies on Partially Observable Markov Decision Processes (POMDPs), enabling effective decision-making under uncertainty. Although his most-cited paper, "Probabilistic Search and Pursuit Evasion on a Graph" (2015), has garnered 4 citations, it represents a foundational step in integrating probabilistic reasoning with robotic search strategies. Ehsan’s contributions are particularly relevant for applications in security, surveillance, and autonomous navigation, where robots must intelligently locate and neutralize threats in complex, dynamic environments. His work continues to inspire further research into efficient, real-time search and pursuit algorithms.
Research Focus
Key Achievements
Top Papers
- 1Probabilistic Search and Pursuit Evasion on a Graph4 citations · 2015