Systematic drift characterization in differential wheeled robot using external VR tracking: Effects of route complexity and motion dynamics
Artykuł w czasopiśmie
MNiSW
70
Lista 2024
| Status: | |
| Autorzy: | Skulimowski Stanisław, Rybka Szymon, Tatara Bartosz, Welman Michał Dawid |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 3 |
| Wolumen/Tom: | 21 |
| Strony: | 117 - 136 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | The work was co-financed by the Department of Computer Science Lublin University of Technology and Lublin University of Technology Scientific Fund FD-20/IT-3/015 |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 30 września 2025 |
| Abstrakty: | angielski |
| Industrial mobile robots face critical positioning challenges that impact manufacturing efficiency, warehouse automation productivity, and biomedical service delivery. This paper presents a reproducible framework for quantifying odometric drift in differential-drive robots, validated by consumer-grade, low-cost VR tracking. Applications include industrial automation calibration, warehouse logistics management, and precision biomedical device positioning. Through more than 750 automated experimental trials spanning a comprehensive matrix of motor configurations and path geometries, the results show that both path complexity and turn size significantly influence drift patterns. Specifically, routes with higher geometric complexity (12-15 segments) exhibited 22% greater position error than simpler paths. The analysis used advanced metrics such as the Normalized Drift Contribution Index. The results confirm robust, high-resolution drift analysis and provide a low-cost validation tool for robot calibration in manufacturing and medical instrumentation. The work provides actionable insights for optimizing robot programming, calibration, and curriculum design, and establishes a scalable protocol for benchmarking autonomous navigation systems in real-world scenarios. In addition, the methodology enables data-driven decision making for robot fleet management, reducing operational downtime compared to manual calibration methods, while providing quantitative performance benchmarks essential for industrial quality control standards. |
