Kirill Kasmynin

Moscow Institute of Physics and Technology

Papers

Total Citations

H-Index

About

Kirill Kasmynin is a researcher at the intersection of robotics, computer vision, and geometric planning. His work focuses on enabling autonomous systems to navigate complex environments efficiently, particularly through the integration of neural methods with classical path planning. His most cited paper, "Vectorized Visibility Graph Planning with Neural Polygon Extraction" (2024), introduces a novel framework that leverages neural networks to extract polygonal representations of obstacles from visual data, then uses these to construct visibility graphs for rapid, collision-free path planning. This approach bridges the gap between deep learning’s perceptual strengths and the computational efficiency of geometric algorithms, offering a scalable solution for real-time robotic navigation. While his citation count is still growing, Kasmynin’s work is notable for its technical rigor and practical relevance, addressing a key challenge in autonomous systems: translating raw sensor input into actionable spatial reasoning. His contributions are particularly valuable for applications in mobile robotics, autonomous driving, and drone navigation, where fast, reliable planning is critical. As an emerging voice in the field, Kasmynin’s research signals a promising direction for hybrid AI-robotics systems.