Deploying an Educational Mobile Robot
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 pla...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/7/591 |
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| author | Dorina Plókai Borsa Détár Tamás Haidegger Enikő Nagy |
| author_facet | Dorina Plókai Borsa Détár Tamás Haidegger Enikő Nagy |
| author_sort | Dorina Plókai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bd320e88ad334c5988fc04dc807192b2 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-bd320e88ad334c5988fc04dc807192b22025-08-20T03:32:32ZengMDPI AGMachines2075-17022025-07-0113759110.3390/machines13070591Deploying an Educational Mobile RobotDorina Plókai0Borsa Détár1Tamás Haidegger2Enikő Nagy3John Von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, HungaryJohn Von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, HungaryUniversity Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, HungaryJohn Von Neumann Faculty of Informatics, Óbuda University, 1034 Budapest, HungaryThis 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.https://www.mdpi.com/2075-1702/13/7/591mobile educational robotsensor data processingodometry analysisrobot transparency |
| spellingShingle | Dorina Plókai Borsa Détár Tamás Haidegger Enikő Nagy Deploying an Educational Mobile Robot Machines mobile educational robot sensor data processing odometry analysis robot transparency |
| title | Deploying an Educational Mobile Robot |
| title_full | Deploying an Educational Mobile Robot |
| title_fullStr | Deploying an Educational Mobile Robot |
| title_full_unstemmed | Deploying an Educational Mobile Robot |
| title_short | Deploying an Educational Mobile Robot |
| title_sort | deploying an educational mobile robot |
| topic | mobile educational robot sensor data processing odometry analysis robot transparency |
| url | https://www.mdpi.com/2075-1702/13/7/591 |
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