Dmitrii Shestov
Papers
1
Total Citations
4
H-Index
1
About
Dmitrii Shestov is a researcher specializing in robotics, control systems, and neural network applications for industrial automation. His work focuses on solving complex kinematic challenges in real-time control environments, particularly for multilink industrial robots. Shestov’s major contribution lies in developing a hybrid approach that integrates neuro-fuzzy networks (ANFIS) with the Newton-Raphson numerical method to solve the inverse kinematics problem—a critical issue in robotic motion planning. This method enables faster, more accurate real-time control, bridging the gap between adaptive learning and precise numerical computation. His 2019 paper on this topic has garnered 4 citations, reflecting its foundational role in advancing practical robotic control systems. Shestov’s research is notable for its direct applicability to industrial automation, offering a scalable solution for complex manipulators. His work stands out for combining soft computing with classical numerical techniques, paving the way for more efficient and responsive robotic systems in manufacturing and beyond.
Research Focus
Key Achievements
Top Papers
- 1