M. Kazheunikau

University of Algarve, Belarusian State University of Informatics and Radioelectronics

Papers

Total Citations

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About

M. Kazheunikau is a researcher whose work lies at the intersection of robotics and neural computation, with a primary focus on intelligent motion planning for industrial manipulators. His most significant contribution is a novel neural network approach to collision-free path-planning, detailed in his 2006 paper (14 citations). This work introduces a topologically ordered neural network that models the harmonic potential field of a robot's configuration space, sampled via a non-regular grid—an efficient method for navigating complex environments without collisions. Building on this, his 2005 paper (8 citations) extends the methodology to trajectory synthesis, demonstrating how neural networks can generate smooth, feasible paths for robotic arms. Though his citation counts are modest, Kazheunikau's research represents an early and creative fusion of neural network theory with practical robotics challenges, offering a computationally elegant alternative to traditional path-planning algorithms. His work is particularly valuable for students and engineers seeking biologically inspired solutions to real-world automation problems, highlighting the potential of neural models to handle the high-dimensional, non-linear constraints inherent in robotic manipulation.