Kirill Yefremov
Papers
3
Total Citations
78
H-Index
2
About
Kirill Yefremov is a robotics researcher whose work centers on the intelligent control and optimal path planning of mobile wheeled robots. His primary contributions lie at the intersection of neural network control and classical mechanics, where he develops algorithms that enable robots to navigate efficiently and autonomously. Yefremov’s most influential paper, "Neural network control of a wheeled mobile robot based on optimal trajectories" (2020, 55 citations), introduces a novel method for training artificial neural networks to plan paths that ensure optimal motion from a robot’s current position to a target. This work has become a key reference for researchers seeking to combine machine learning with real-time robotic control. In a complementary study, "Euler Elasticas for Optimal Control of the Motion of Mobile Wheeled Robots" (2019, 21 citations), Yefremov explores the use of Euler elasticas—curves that minimize control effort—as optimal trajectories, providing a rigorous framework for experimental realization. His more recent work, "Experimental Investigations of the Controlled Motion of the Roller Racer Robot" (2021), extends his research into non-holonomic systems, demonstrating his commitment to bridging theory and practice. With a growing citation record, Yefremov is establishing himself as a thoughtful contributor to autonomous robotics, particularly in the design of energy-efficient and computationally tractable control systems.
Research Focus
Key Achievements
Top Papers
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