Deploying an Educational Mobile Robot

Abstract

This study presents the development of a software solution for processing, analyzing, and visualizing sensor data collected by an educational mobile robot. The focus is on statistical analysis and identifying correlations between diverse datasets. The research utilized the PlatypOUs mobile robot platform, equipped with odometry and inertial measurement units (IMUs), to gather comprehensive motion data. To enhance the reliability and interpretability of the data, advanced data processing techniques—such as moving averages, correlation analysis, and exponential smoothing—were employed. Python-based tools, including Matplotlib and Visual Studio Code, were used for data visualization and analysis. The analysis provided key insights into the robot’s motion dynamics; specifically, its stability during linear movements and variability during turns. By applying moving average filtering and exponential smoothing, noise in the sensor data was significantly reduced, enabling clearer identification of motion patterns. Correlation analysis revealed meaningful relationships between velocity and acceleration during various motion states. These findings underscore the value of advanced data processing techniques in improving the performance and reliability of educational mobile robots. The insights gained in this pilot project contribute to the optimization of navigation algorithms and motion control systems, enhancing the robot’s future potential in STEM education applications.