Ganna Ponomaryova

Kharkiv National University of Radio Electronics

Papers

Total Citations

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About

Ganna Ponomaryova is a researcher at the forefront of robotics and sensor technology, specializing in the integration of MEMS-based inertial sensors with machine learning for advanced robotic control. Her work focuses on solving the critical challenge of real-time robot state classification, enabling machines to interpret their own motion and orientation with greater precision. In her most cited work, "MEMS-Based Inertial Sensor Signals and Machine Learning Methods for Classifying Robot Motion" (2018, 13 citations), she pioneered a method using three-axis MEMS gyroscope data to classify robot states, demonstrating how algorithms like decision trees and neural networks can transform raw sensor signals into actionable intelligence. Ponomaryova further advanced this field in "MEMS accelerometer in hexapod intellectual control" (2018, 4 citations), where she integrated MEMS accelerometers into a hexapod robot's control system, achieving real-time state classification that enhances stability and adaptability in complex terrains. Her contributions bridge the gap between low-cost sensor hardware and intelligent software, offering scalable solutions for autonomous robotics. By pushing the boundaries of how robots perceive their environment, Ponomaryova is shaping the future of responsive, self-aware machines in industries ranging from manufacturing to exploration.