AVTONOM ROBOTLASHGAN TIZIMLARNI RIVOJLANTIRISH UCHUN HARAKATNI QAYD ETISH MA’LUMOTLARIGA ASOSLANGAN RAQAMLI EGIZAK PLATFORMASI
DOI:
https://doi.org/10.5281/zenodo.19633833Keywords:
raqamli egizak, OptiTrack, harakatni qayd etish, kalibrlash, pozitsion aniqlik, inson-robot hamkorligi, dron, Sanoat 4.0, avtonom robotik tizimlar, xavfsizlikni baholashAbstract
Ushbu maqolada Sanoat 4.0 sharoitida robotlashgan tizimlar va uchuvchisiz qurilmalarning pozitsion
aniqligini oshirish, xavfsizligini baholash hamda avtonom ishlash imkoniyatlarini takomillashtirishga qaratilgan harakatni
qayd etish ma’lumotlari asosidagi raqamli egizak platformasi taklif etiladi. Tadqiqotning dolzarbligi shundaki, zamonaviy
ishlab chiqarish muhitida robot, inson va avtonom tizimlar o‘rtasidagi o‘zaro ta’sir yuqori aniqlik va ishonchli monitoringni
talab qiladi. Maqolada harakatni qayd etish ma’lumotlaridan foydalangan holda real va virtual muhitni muvofiqlashtirish,
kalibrlash va xatoliklarni kamaytirish usullari yoritiladi. Shuningdek, xavfsizlik masofalarini baholash, trayektoriya
og‘ishlarini aniqlash va avtonom boshqaruv sifatini yaxshilash bo‘yicha ilmiy yondashuvlar ishlab chiqiladi. Tadqiqot
natijalari shuni ko‘rsatadiki, OptiTrack kamera tizimlari yordamida shakllantirilgan raqamli egizak platforma robot
manipulyatorlar va dronlar uchun yuqori aniqlikdagi tashqi etalon manbai sifatida xizmat qilishi mumkin. Ilmiy yangilik
sifatida harakatni qayd etish tizimi, raqamli egizak va xatolikni onlayn tuzatish yondashuvlarini yagona arxitekturaga
birlashtirish taklif etiladi.
References
González, L., et al. (2021). Metrological evaluation of human-robot collaborative environments based on optical motion
capture systems. Sensors, 21(11), 3748.
Maculotti, G., et al. (2025). Traceable digital twin for accurate positioning of industrial robot arms in human-robot
collaborative systems. Flexible Services and Manufacturing Journal.
Kirkpatrick, M., et al. (2023). Motion capture-based calibration for industrial robots. Manufacturing Letters, 35, 926–
Maruyama, T., et al. (2021). Digital twin-driven human-robot collaboration using a digital human. Sensors, 21(24),
Wang, B., Zhang, Y., & Zhang, W. (2022). Integrated path planning and trajectory tracking control for quadrotor UAVs
with obstacle avoidance in the presence of environmental and systematic uncertainties: Theory and experiment.
Aerospace Science and Technology, 120, 107277.
Rosner, J., et al. (2025). Multimodal dataset for indoor 3D drone tracking. Scientific Data, 12, 257.
DPJAIT Dataset. (2025). Multimodal dataset for indoor 3D drone tracking. Zenodo.
Shalaby, M. A., et al. (2026). MILUV: A multi-UAV indoor localisation dataset with UWB and vision. The International
Journal of Robotics Research.
Qassab, A., et al. (2024). Autonomous landing of a quadrotor on a moving platform using a motion capture system.
Discover Applied Sciences.
Popescu, M., et al. (2022). Experimental investigations into using motion capture state feedback for real-time control
of a humanoid robot. Sensors, 22(24), 9853.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 MUHANDISLIK VA IQTISODIYOT

This work is licensed under a Creative Commons Attribution 4.0 International License.