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Deploying an Educational Mobile Robot

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Publicado no:Machines vol. 13, no. 7 (2025), p. 591-610
Autor principal: Plókai Dorina
Outros Autores: Borsa, Détár, Haidegger Tamás, Nagy Enikő
Publicado em:
MDPI AG
Assuntos:
Teaching
Augmented reality
Software
Students
Scientific visualization
Data processing
Trends
Mathematics education
Robots
STEM education
Data analysis
Statistical analysis
Energy consumption
Visualization
Robotics
Skills
Technology education
Big Data
Machine learning
Programming languages
Smoothing
Artificial intelligence
Robot dynamics
Lasers
Science education
Education
Reliability
Sensors
Engineering
Visual programming languages
Inertial platforms
Motion control
Robot control
Correlation analysis
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Resumo: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.
ISSN:2075-1702
DOI:10.3390/machines13070591
Fonte:Engineering Database