Eduard Petlenkov
Papers
6
Total Citations
37
H-Index
3
About
Eduard Petlenkov’s research lies at the intersection of intelligent control, robotics, and neural network modeling, with a strong emphasis on energy efficiency and human–machine interaction. His pioneering work on applying self-organizing Kohonen maps to segment and recognize surgeon hand motions during endoscopic surgery (16 citations) established a novel framework for analyzing dynamic human movements in medical settings. More recently, Petlenkov has focused on minimizing power consumption in industrial robotics, introducing neural-network-based strategies for managing energy use in linear Delta robots and optimizing pick-and-place operations (9 and 6 citations). His contributions extend to advanced control architectures, including digital twin simulations combined with reinforcement learning for wheel-on-leg mobile robots (2 citations). Beyond technical innovation, Petlenkov is a leader in engineering education, developing active blended learning approaches tailored to diverse student backgrounds within the EuroTeQ Engineering University alliance. His work directly addresses Industry 4.0 and 5.0 requirements, bridging cutting-edge automation with sustainable, human-centered design. With a career spanning foundational neural network methods to practical robotic energy management, Petlenkov’s research continues to shape both intelligent systems and the next generation of engineers.
Research Focus
Key Achievements
Top Papers
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