Accurately tracking the position of machinery like tunnel borers or robots in underground environments is a major challenge. Standard GPS fails in these settings, and sensor systems like inertial navigation units accumulate large positioning errors over time. This paper presents a novel, data-driven solution to this problem. The core of our system is a powerful data processing framework that fuses these vast, real-time sensor datasets. Using a machine learning model, we correct the drift in the motion sensors, enabling highly accurate, real-time geological positioning.
| Mualliflar | Khonturaev, Sardorbek, Qayumov, Arslonbek |
|---|---|
| Jurnal | Techscience.uz - техника фанлари долзарб масалалри |
| Nashr sanasi | 2025-11-02 |
| Jild | 3 |
| Son | 10 |
| Betlar | 18-21 |
| Til | Ingliz |
| DOI | 10.47390/ts-v3i10y2025no3 |
DOI: 10.47390/ts-v3i10y2025no3 · Maqolaning asl sahifasi
Real-Time Tracking, Geological Monitoring, Data-Driven System, Machine Learning, Big Data., Real vaqt rejimida kuzatish, geologik monitoring, ma'lumotlarga asoslangan tizim, mashinali oqitish, Big Data.
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