DETECTION OF OBJECTS ON THE GROUND BY INTELLIGENT ROBOTS IN A RAPIDLY CHANGING ENVIRONMENT

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A.A. Nikitina
Federal State Budgetary Scientific Institution «Institute of Artificial Intelligence Problems», Donetsk
Research interests: computer information technology

S.I. Ulanov
Federal State Budgetary Scientific Institution «Institute of Artificial Intelligence Problems», Donetsk
Research interests: intelligent robotic systems

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UDC 528.013
DOI 10.34757/2413-7383.2023.30.3.003
Language:Russian

Annotation: The article presents several of the most effective approaches for solving the problem of detecting objects on the ground by intelligent robots in a rapidly changing environment. The application of computer vision, machine learning based on self-learning convolutional neural network is justified. The study of this problem has shown that the most effective result will be the use of these approaches in a complex.

Keywords:self-learning convolutional neural network, FPGA, GPS data, data reduction.

References:
1. Bychkov, I.V., Kenzin, M.Yu, Maksimkin, N.N. A two-level evolutionary approach to routing a group of underwater robots in conditions of periodic rotation of the composition. Proceedings of SPIIRAN St. Petersburg, volume 18 No. 22019, pp. 267-301.
2. Blidberg D.R. The Development of Autonomous Underwater Vehicles (AUV); A Brief Summary. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). 2001. vol. 4. pp. 1.
3. Kenzin M.Yu., Bychkov I.V., Maksimkin N.N. A hybrid approach to solve the dynamic patrol routing problem for group of underwater robots. 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). 2016. pp. 1114–1119.
4. Nikitina, A. A. Analiz dostizheniy nauchnykh issledovaniy i innovatsiy v oblasti iskusstvennykh neyronnykh setey (INS) [Analysis of scientific research and innovation achievements in the field of artificial neural networks (INS)]. Materialy Donetskogo mezhdunarodnogo nauchnogo kruglogo stola «Iskusstvennyy intellekt: teoreticheskiye aspekty i prakticheskoye primeneniye» II-2020 [Materials of the Donetsk International Scientific round table "Artificial intelligence: theoretical aspects and practical application" AI-2020]. Donetsk: GU IPII, 2020. pp. 142-148.
5. Nikitina, A.A., Blizno M.V. Obzor mashinnykh metodov stereozreniya [Review of machine methods of stereovision] Materialy Donetskogo mezhdunarodnogo kruglogo stola «Iskusstvennyy intellekt: teoreticheskiye aspekty i prakticheskoye primeneniye. II – 2021». [Materials of the Donetsk International round table "Artificial intelligence: theoretical aspects and practical application. AI – 2021"]. Donetsk : GU "IPII", 27.05.2021. pp. 104-109.
6. Zuev M.V., Butov O.A., Ivanova S.B., Nikitina A.A., Ulanov S. I. Sposob obucheniya neyronnoy seti upravleniya robotom [Method of training a neural network of robot control] Problemy iskusstvennogo intellekta (International Peer-Reviewed Scientific Journal «Problems of Artificial Intelligence»), ISSN 2413-7383 [International Peer-Reviewed Scientific Journal "Problems of Artificial Intelligence", ISSN 2413-7383]. 2021 No. 2 (21). pp. 22-33.
7. Zuev V.M., Blizno M.V. Algoritm kalibrovki stereokamery [Stereo camera calibration algorithm] Problemy iskusstvennogo intellekta (International Peer-Reviewed Scientific Journal «Problems of Artificial Intelligence»), ISSN 2413-7383 [Problems of artificial intelligence (International PeerReviewed Scientific Journal "Problems of Artificial Intelligence")], ISSN 2413-7383. 2020. № 4 (19).
8. Zuev V.M., Nikitina A.A. Razrabotka uskoritelya svertochnoy neyronnoy seti na baze PLIS, ispol'zuyushchego algoritm Vinograda PLIS [Development of a convolutional neural network accelerator on the basis of FPGA using the algorithm of FPGA Grapes] Materialy Donetskogo mezhdunarodnogo kruglogo stola «Iskusstvennyy intellekt: teoreticheskiye aspekty [Materials of the Donetsk International Round table "Artificial Intelligence: theoretical aspects and practical application. AI – 2021"]. Donetsk : GU "IPII", 27.05.2021 pp. 96-103.
9. Eremeev S.V., Abakumov A.V., Andrianov, D.E., Shirabakina T.A. Method of vectorization of satellite images based on their decomposition by topological features. Computer science and automation. 2023. Volume 22, No. 1. Рр. 110-145.
10.Chernikhovsky D.M., Alekseev A.S. Determination of average heights and stocks of stands based on the processing of topographic radar survey information, digital terrain models and GIS technologies. The works of SPIIRAN. Volume 18 No. 2, 2019. рр. 416-444.
11. Pikalev Ya.S., Ermolenko T.E. On neural architectures of feature extraction for the problem of object recognition on devices with limited computing power. Artificial intelligence: theoretical aspects, practical application: materials of the Donetsk International Scientific Round Table. Donetsk : FGBNU "IPII", 2023. 252 p. pp. 142-147.
12. Zuev V.M. Comparison of detection by the classical method. Artificial intelligence: theoretical aspects, practical application : materials of the Donetsk International Scientific Round table. Donetsk : FGBNU "IPII", 2023. 252 p. pp. 70-79.
13. Frolov V.V. Methods of synchronous addition of video signals. Artificial intelligence: theoretical aspects, practical application : materials of the Donetsk International Scientific Round table. Donetsk : FGBNU "IPII", 2023. 252 p. p. 217-220.
14. Frolov V.V., Piguz V.N. Intelligent video surveillance systems with integrated video analytics for solving security alarm problems. Artificial intelligence: theoretical aspects and practical application: materials of the Donetsk International Scientific Round Table. Donetsk : GU "Institute of Artificial Intelligence Problems" (GU "IPII"), 2022. 216 p. pp. 133-140.
15.Blizno M.V., Zuev V.M., Ivanova S.B. Image recognition by coefficients of discrete cosine transformation. Artificial intelligence: theoretical Aspects and practical application : materials of the Donetsk International Scientific Round Table. Donetsk : GU "Institute of Artificial Intelligence Problems" (GU "IPII"), 2022. 216 p. pp. 92-96.
16.Butov O. A. Review of automated design and simulation tools. Artificial intelligence: theoretical aspects and practical application : materials of the Donetsk International Scientific Round Table. Donetsk : GU "Institute of Artificial Intelligence Problems" (GU "IPII"), 2022. 216 c. pp. 102-113.
17. Polous A. G. Possibilities of processing ontologies using the dotNetRDF library. Donetsk Readings 2023: Education, Science, Innovation, Culture and Modern Challenges : Proceedings of the VIII International Scientific Conference (Donetsk, October 25-27, 2023). Volume 2: Physical, Technical and Computer Sciences / edited by Prof. S.V. Bespalova. Donetsk : DonGU Publishing House, 2023. 382 p. pp. 344- 346.
18. Pokintelitsa A. E. Features of data reduction coming to the input of a robotic system. Artificial intelligence: theoretical aspects, practical application : materials of the Donetsk International Scientific Round table. Donetsk : FGBNU "IPII", 2023. 252 p. p. 162-167.

Issues: 3(30)'2023
Section: Informatics, Computer Engineering and Control
Cite: Nikitina, A.A. DETECTION OF OBJECTS ON THE GROUND BY INTELLIGENT ROBOTS IN A RAPIDLY CHANGING ENVIRONMENT // A.A. Nikitina, S.I. Ulanov // Проблемы искусственного интеллекта. - 2023. № 3 (30). - http://search.rads-doi.org/project/13749/object/201185 doi: 10.34757/2413-7383.2023.30.3.003

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