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About

Alexander Lozhkin is a robotics researcher whose work bridges the gap between classical geometry and modern autonomous systems. His primary research areas include robot trajectory planning, precision motion control, and the application of neural networks in autonomous robotics. Lozhkin’s most significant contributions lie in developing novel mathematical methods for calculating complex planar trajectories—particularly Jordan curves and elliptic paths—that enable high-precision robot movement without relying on traditional approximation techniques. His 2016 paper on Jordan curve trajectory calculation, with 36 citations, introduces a method grounded in the intrinsic properties of the plane, addressing a long-standing challenge in high-accuracy motion. This work is complemented by his 2014 study on elliptic trajectories (12 citations) and his 2019 precision calculation method for plane trajectories (7 citations), both of which advance the mathematical apparatus for designing robots with complex motion paths. In 2020, Lozhkin expanded into deep learning, exploring convolutional neural network training for autonomous robotics, demonstrating his versatility in combining geometric theory with AI-driven approaches. His research offers practical solutions for industries requiring high-fidelity geometric modeling and precise robotic control.