A.Yu. Krasnov
Papers
5
Total Citations
98
H-Index
4
About
A.Yu. Krasnov is a robotics researcher whose work bridges classical control theory and modern machine learning to advance mobile and soft robot autonomy. His key research areas include visual terrain classification, reinforcement learning for soft robotics, and nonlinear trajectory control. Krasnov’s most impactful contribution is a hybrid deep learning method that combines a convolutional neural network with a support vector machine for real-time visual terrain classification on mobile robots, achieving high accuracy despite limited onboard computing (50 citations). He also pioneered the use of deep reinforcement learning for position control of cable-driven soft robotic arms, addressing the challenge of modeling highly deformable materials (36 citations). In earlier work, Krasnov applied differential geometry to synthesize path-following and trajectory control algorithms for omni-wheeled robots, handling moving obstacles and external disturbances with invariant manifold stabilization. His research demonstrates a rare ability to integrate data-driven approaches with rigorous nonlinear control theory, producing practical solutions for field robotics. Notable achievements include developing control algorithms that operate under real-world constraints like unmeasured disturbances and dynamic environments, making his work valuable for both academic researchers and engineers building autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 4Trajectory control for a robot motion in presense of moving obstacles4 citations · 2017
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