Yuri Rumyantsev
Papers
2
Total Citations
10
H-Index
2
About
Yuri Rumyantsev is a researcher focused on the intersection of control theory, robotics, and machine learning, with particular expertise in neural network modeling and symbolic regression. His work addresses fundamental challenges in autonomous robot navigation and control system synthesis. His most cited paper, "Identification of Neural Network Model of Robot to Solve the Optimal Control Problem" (2021, 8 citations), develops mathematical models crucial for calculating optimal control strategies and improving robot positioning accuracy through sensor data correction. In his subsequent work, "Synthesis of a Feedback Controller by the Network Operator Method for a Mobile Robot Rosbot in Gazebo Environment" (2023, 2 citations), Rumyantsev introduces an innovative approach combining machine learning with symbolic regression to create stabilization systems for mobile robots. This method is notable for its universality, enabling numerical solutions to control synthesis problems without requiring pre-labeled training data. Rumyantsev's contributions are particularly relevant for researchers working on autonomous systems, as his methods offer practical solutions for real-world robot control challenges, bridging the gap between theoretical control theory and practical implementation in simulated environments like Gazebo.
Research Focus
Key Achievements
Top Papers
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