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Application of a Genetic Algorithm in Planning the Optimal Route of Unmanned Aerial Vehicles Used for Large Area Monitoring. / Rodionov, Aleksey S.; Matkurbanov, Tulkin A.; Yagibayeva, Madina R.

Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023. Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 1560-1564.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Rodionov, AS, Matkurbanov, TA & Yagibayeva, MR 2023, Application of a Genetic Algorithm in Planning the Optimal Route of Unmanned Aerial Vehicles Used for Large Area Monitoring. in Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023. Institute of Electrical and Electronics Engineers (IEEE), pp. 1560-1564, 16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, Новосибирск, Russian Federation, 10.11.2023. https://doi.org/10.1109/apeie59731.2023.10347781

APA

Rodionov, A. S., Matkurbanov, T. A., & Yagibayeva, M. R. (2023). Application of a Genetic Algorithm in Planning the Optimal Route of Unmanned Aerial Vehicles Used for Large Area Monitoring. In Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023 (pp. 1560-1564). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/apeie59731.2023.10347781

Vancouver

Rodionov AS, Matkurbanov TA, Yagibayeva MR. Application of a Genetic Algorithm in Planning the Optimal Route of Unmanned Aerial Vehicles Used for Large Area Monitoring. In Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023. Institute of Electrical and Electronics Engineers (IEEE). 2023. p. 1560-1564 doi: 10.1109/apeie59731.2023.10347781

Author

Rodionov, Aleksey S. ; Matkurbanov, Tulkin A. ; Yagibayeva, Madina R. / Application of a Genetic Algorithm in Planning the Optimal Route of Unmanned Aerial Vehicles Used for Large Area Monitoring. Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023. Institute of Electrical and Electronics Engineers (IEEE), 2023. pp. 1560-1564

BibTeX

@inproceedings{947745412c6b492988fe1e17ef391cb1,
title = "Application of a Genetic Algorithm in Planning the Optimal Route of Unmanned Aerial Vehicles Used for Large Area Monitoring",
abstract = "Military, construction, picture and video mapping, medical, search and rescue, parcel delivery, covert area survey, oil rig and power line monitoring are just a few of the applications for unmanned aerial vehicles (UAVs). UAVs are gaining popularity as a model for agricultural, wireless communications and aerial surveillance, as well as service and delivery. Unmanned aerial vehicle (UAV) research has attracted a lot of interest in recent decades due to the rapid growth of computer technology, autonomous control technology, and communication technology. In various industries, including aviation, unmanned systems are replacing manned systems. Due to their high success rates in military and civilian missions, unmanned aerial vehicles (UAVs), one of the most popular and successful unmanned systems, are gradually becoming an important part of the industry. air. The biggest problem with drones is finding the best trajectory in difficult conditions. It seeks to tour all control locations as efficiently as possible in order to receive information from sensors set in wide areas. In this paper, we employed a Genetic Algorithm (GA) to discover the best flight path for unmanned aerial vehicles (UAVs) in a three-dimensional environment, i.e. space. One of the important elements in the suggested strategy is solving the travelling salesman problem (TSP) to find the optimal path. In the future, we can enhance the complexity of our problem through the addition of new constraints imposed by the dynamic environment in which we operate. The experimental results suggest that GA can be used to optimize UAV path planning.",
author = "Rodionov, {Aleksey S.} and Matkurbanov, {Tulkin A.} and Yagibayeva, {Madina R.}",
note = "{\textcopyright} 2023 IEEE.; 16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023 ; Conference date: 10-11-2023 Through 12-11-2023",
year = "2023",
doi = "10.1109/apeie59731.2023.10347781",
language = "English",
isbn = "9798350330885",
pages = "1560--1564",
booktitle = "Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

RIS

TY - GEN

T1 - Application of a Genetic Algorithm in Planning the Optimal Route of Unmanned Aerial Vehicles Used for Large Area Monitoring

AU - Rodionov, Aleksey S.

AU - Matkurbanov, Tulkin A.

AU - Yagibayeva, Madina R.

N1 - Conference code: 16

PY - 2023

Y1 - 2023

N2 - Military, construction, picture and video mapping, medical, search and rescue, parcel delivery, covert area survey, oil rig and power line monitoring are just a few of the applications for unmanned aerial vehicles (UAVs). UAVs are gaining popularity as a model for agricultural, wireless communications and aerial surveillance, as well as service and delivery. Unmanned aerial vehicle (UAV) research has attracted a lot of interest in recent decades due to the rapid growth of computer technology, autonomous control technology, and communication technology. In various industries, including aviation, unmanned systems are replacing manned systems. Due to their high success rates in military and civilian missions, unmanned aerial vehicles (UAVs), one of the most popular and successful unmanned systems, are gradually becoming an important part of the industry. air. The biggest problem with drones is finding the best trajectory in difficult conditions. It seeks to tour all control locations as efficiently as possible in order to receive information from sensors set in wide areas. In this paper, we employed a Genetic Algorithm (GA) to discover the best flight path for unmanned aerial vehicles (UAVs) in a three-dimensional environment, i.e. space. One of the important elements in the suggested strategy is solving the travelling salesman problem (TSP) to find the optimal path. In the future, we can enhance the complexity of our problem through the addition of new constraints imposed by the dynamic environment in which we operate. The experimental results suggest that GA can be used to optimize UAV path planning.

AB - Military, construction, picture and video mapping, medical, search and rescue, parcel delivery, covert area survey, oil rig and power line monitoring are just a few of the applications for unmanned aerial vehicles (UAVs). UAVs are gaining popularity as a model for agricultural, wireless communications and aerial surveillance, as well as service and delivery. Unmanned aerial vehicle (UAV) research has attracted a lot of interest in recent decades due to the rapid growth of computer technology, autonomous control technology, and communication technology. In various industries, including aviation, unmanned systems are replacing manned systems. Due to their high success rates in military and civilian missions, unmanned aerial vehicles (UAVs), one of the most popular and successful unmanned systems, are gradually becoming an important part of the industry. air. The biggest problem with drones is finding the best trajectory in difficult conditions. It seeks to tour all control locations as efficiently as possible in order to receive information from sensors set in wide areas. In this paper, we employed a Genetic Algorithm (GA) to discover the best flight path for unmanned aerial vehicles (UAVs) in a three-dimensional environment, i.e. space. One of the important elements in the suggested strategy is solving the travelling salesman problem (TSP) to find the optimal path. In the future, we can enhance the complexity of our problem through the addition of new constraints imposed by the dynamic environment in which we operate. The experimental results suggest that GA can be used to optimize UAV path planning.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85182264716&origin=inward&txGid=31f7d90865716092417f51aed6d5bb89

UR - https://www.mendeley.com/catalogue/d1e83f9a-cab4-325d-9631-d60c3d060c01/

U2 - 10.1109/apeie59731.2023.10347781

DO - 10.1109/apeie59731.2023.10347781

M3 - Conference contribution

SN - 9798350330885

SP - 1560

EP - 1564

BT - Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering

Y2 - 10 November 2023 through 12 November 2023

ER -

ID: 59613955