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Поле DC | Значение | Язык |
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dc.contributor.author | Gyrichidi, N. | en |
dc.contributor.author | Romanov, A. M. | en |
dc.contributor.author | Trofimov, O. V. | en |
dc.contributor.author | Eroshenko, S. A. | en |
dc.contributor.author | Matrenin, P. V. | en |
dc.contributor.author | Khalyasmaa, A. I. | en |
dc.date.accessioned | 2025-02-25T10:52:07Z | - |
dc.date.available | 2025-02-25T10:52:07Z | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | Gyrichidi, N., Romanov, A. M., Trofimov, O. V., Eroshenko, S. A., Matrenin, P. V., & Khalyasmaa, A. I. (2024). GNSS-Based Narrow-Angle UV Camera Targeting: Case Study of a Low-Cost MAD Robot. Sensors, 24(11), [3494]. https://doi.org/10.3390/s24113494 | apa_pure |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.other | Final | 2 |
dc.identifier.other | All Open Access; Gold Open Access; Green Open Access | 3 |
dc.identifier.other | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195882566&doi=10.3390%2fs24113494&partnerID=40&md5=0477003f17f0f71e7f0f7e5b68029439 | 1 |
dc.identifier.other | https://www.mdpi.com/1424-8220/24/11/3494/pdf?version=1716913085 | |
dc.identifier.uri | http://elar.urfu.ru/handle/10995/141660 | - |
dc.description.abstract | One of the key challenges in Multi-Spectral Automatic Diagnostic (MAD) robot design is the precise targeting of narrow-angle cameras on a specific part of the equipment. The paper shows that a low-cost MAD robot, whose navigation system is based on open-source ArduRover firmware and a pair of low-cost Ublox F9P GNSS receivers, can inspect the 8 × 4 degree ultraviolet camera bounding the targeting error within 0.5 degrees. To achieve this result, we propose a new targeting procedure that can be implemented without any modifications in ArduRover firmware and outperforms more expensive solutions based on LiDAR SLAM and UWB. This paper will be interesting to the developers of robotic systems for power equipment inspection because it proposes a simple and effective solution for MAD robots’ camera targeting and provides the first quantitative analysis of the GNSS reception conditions during power equipment inspection. This analysis is based on the experimental results collected during the inspection of the overhead power transmission lines and equipment inspections on the open switchgear of different power plants. Moreover, it includes not only satellite, dilution of precision, and positioning/heading estimation accuracy but also the direct measurements of angular errors that could be achieved on operating power plants using GNSS-only camera targeting. © 2024 by the authors. | en |
dc.description.sponsorship | Russian Science Foundation, RSF, (22-79-10315); Russian Science Foundation, RSF | en |
dc.description.sponsorship | This work was supported by the Russian Science Foundation, research project No. 22-79-10315. | en |
dc.format.mimetype | application/pdf | en |
dc.language.iso | en | en |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.rights | cc-by | other |
dc.source | Sensors | 2 |
dc.source | Sensors | en |
dc.subject | CAMERA TARGETING | en |
dc.subject | CORONA DISCHARGE | en |
dc.subject | ENERGY | en |
dc.subject | GLOBAL NAVIGATION SATELLITE SYSTEM | en |
dc.subject | INSPECTION | en |
dc.subject | POWER TRANSMISSION LINE | en |
dc.subject | REAL-TIME KINEMATIC | en |
dc.subject | SUBSTATION | en |
dc.subject | UV SENSORS | en |
dc.subject | COSTS | en |
dc.subject | ELECTRIC POWER TRANSMISSION | en |
dc.subject | ELECTRIC SUBSTATIONS | en |
dc.subject | FIRMWARE | en |
dc.subject | INSPECTION | en |
dc.subject | MACHINE DESIGN | en |
dc.subject | OVERHEAD LINES | en |
dc.subject | ROBOTS | en |
dc.subject | AUTOMATIC DIAGNOSTICS | en |
dc.subject | CAMERA TARGETING | en |
dc.subject | CORONA DISCHARGES | en |
dc.subject | ENERGY | en |
dc.subject | GLOBAL NAVIGATION SATELLITE SYSTEMS | en |
dc.subject | MULTI-SPECTRAL | en |
dc.subject | POWER TRANSMISSION LINES | en |
dc.subject | REAL TIME KINEMATIC | en |
dc.subject | SUBSTATION | en |
dc.subject | UV SENSOR | en |
dc.subject | ARTICLE | en |
dc.subject | CAMERA | en |
dc.subject | CASE STUDY | en |
dc.subject | DILUTION | en |
dc.subject | ELECTRIC POWER PLANT | en |
dc.subject | ENERGY | en |
dc.subject | GLOBAL NAVIGATION SATELLITE SYSTEM | en |
dc.subject | NAVIGATION SYSTEM | en |
dc.subject | QUANTITATIVE ANALYSIS | en |
dc.subject | SENSOR | en |
dc.subject | ULTRAVIOLET RADIATION | en |
dc.subject | CAMERAS | en |
dc.title | GNSS-Based Narrow-Angle UV Camera Targeting: Case Study of a Low-Cost MAD Robot | en |
dc.type | Article | en |
dc.type | info:eu-repo/semantics/article | en |
dc.type | info:eu-repo/semantics/publishedVersion | en |
dc.identifier.doi | 10.3390/s24113494 | - |
dc.identifier.scopus | 85195882566 | - |
local.contributor.employee | Gyrichidi N., Institute of Artificial Intelligence, MIREA—Russian Technological University (RTU MIREA), Moscow, 119454, Russian Federation | en |
local.contributor.employee | Romanov A.M., Institute of Artificial Intelligence, MIREA—Russian Technological University (RTU MIREA), Moscow, 119454, Russian Federation | en |
local.contributor.employee | Trofimov O.V., Institute of Artificial Intelligence, MIREA—Russian Technological University (RTU MIREA), Moscow, 119454, Russian Federation | en |
local.contributor.employee | Eroshenko S.A., Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, Ekaterinburg, 620002, Russian Federation | en |
local.contributor.employee | Matrenin P.V., Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, Ekaterinburg, 620002, Russian Federation, Power Supply Systems Department, Novosibirsk State Technical University, Novosibirsk, 630073, Russian Federation | en |
local.contributor.employee | Khalyasmaa A.I., Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, Ekaterinburg, 620002, Russian Federation | en |
local.issue | 11 | - |
local.volume | 24 | - |
dc.identifier.wos | 001246373700001 | - |
local.contributor.department | Institute of Artificial Intelligence, MIREA—Russian Technological University (RTU MIREA), Moscow, 119454, Russian Federation | en |
local.contributor.department | Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, Ekaterinburg, 620002, Russian Federation | en |
local.contributor.department | Power Supply Systems Department, Novosibirsk State Technical University, Novosibirsk, 630073, Russian Federation | en |
local.identifier.pure | 58838941 | - |
local.description.order | 3494 | |
local.identifier.eid | 2-s2.0-85195882566 | - |
local.fund.rsf | 22-79-10315 | |
local.identifier.wos | WOS:001246373700001 | - |
local.identifier.pmid | 38894285 | - |
Располагается в коллекциях: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
Файлы этого ресурса:
Файл | Описание | Размер | Формат | |
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2-s2.0-85195882566.pdf | 5,93 MB | Adobe PDF | Просмотреть/Открыть |
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