Shirin Chehelgami
Papers
2
Total Citations
49
H-Index
2
About
Shirin Chehelgami is a rising researcher in autonomous robotics, specializing in safe and intelligent path planning for unmanned vehicles operating in complex environments. Her work bridges deep learning with classical control methods to address one of robotics' most persistent challenges: navigating collision-free paths amid both static and dynamic obstacles. In her highly cited 2023 paper, "Safe deep learning-based global path planning using a fast collision-free path generator" (38 citations), she introduced a novel framework that combines neural network efficiency with rigorous safety guarantees, significantly reducing computational overhead while maintaining reliability. Her 2022 study on the synergy of deep learning and artificial potential field methods (11 citations) further demonstrates her ability to integrate modern AI with established techniques, offering practical solutions for real-world deployment. Chehelgami’s contributions are particularly timely as unmanned vehicles increasingly replace manual tasks in logistics, surveillance, and exploration. By tackling the fundamental problem of autonomous navigation with both theoretical depth and applied insight, she is helping shape the next generation of intelligent, self-guided systems that can operate safely in unpredictable environments.
Research Focus
Key Achievements
Top Papers
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