Alexandr Kobrin
Papers
1
Total Citations
4
H-Index
1
About
Alexandr Kobrin is a robotics researcher whose work focuses on the intersection of neural networks and real-time control systems for industrial automation. His primary research areas include inverse kinematics, hybrid neural network approaches, and the development of intelligent control architectures for multilink robotic manipulators. Kobrin’s most significant contribution is the creation of a novel hybrid method that combines an Adaptive Neuro-Fuzzy Inference System (ANFIS) with the Newton-Raphson numerical method to solve the inverse kinematics problem in real time. This approach addresses a critical challenge in robotics—achieving both speed and accuracy in motion planning for complex, multilink systems. His 2019 paper on this topic, which has garnered 4 citations, demonstrates the practical viability of integrating neural network approximations with classical iterative refinement, paving the way for more responsive and efficient industrial robots. Kobrin’s work is particularly notable for its direct applicability to manufacturing and automation, where real-time control is essential. By bridging the gap between soft computing and traditional numerical methods, he offers a scalable solution that enhances the precision and reliability of robotic systems, making his research a valuable reference for engineers and researchers in robotics and control theory.
Research Focus
Key Achievements
Top Papers
- 1