DOI: https://doi.org/10.32515/2664-262X.2025.12(43).1.31-43
Optimal Route Planning of Unmanned Aerial Vehicles for Efficient Coverage of a Given Area
About the Authors
Maryna Semeniuta, Associate Professor, PhD in Physics and Mathematics, Associate Professor of the Department of Higher Mathematics and Physics, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-4639-0545, e-mail: semeniutamf@kntu.kr.ua
Serhii Yakymenko, Associate Professor, PhD in Physics and Mathematics, Head of the Department of Higher Mathematics and Physics, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-5759-9603, e-mail: yasm@i.ua
Serhii Osadchy, Professor, Doctor of Technical Sciences, Head of the Department of Flight Operations and Flight Safety, Ukrainian State Flight Academy, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-1811-3594, e-mail: srg2005@ukr.net
Abstract
This article addresses the problem of optimizing routes for unmanned aerial vehicles (UAVs) for the inspection of solar panels, which is a specific case of the Traveling Salesman Problem on a complete graph. The aim of the work is to develop a mathematical model for planning UAV routes with the minimization of route length and energy consumption, while ensuring complete coverage of all inspection points.
A program has been developed that implements a UAV routing model, taking into account energy consumption constraints. Four algorithms were applied to solve the problem: greedy algorithm, 2-opt, Ant Colony Optimization, and Genetic Algorithm. Each method was evaluated based on three criteria: execution speed, route length, and energy efficiency.
The experimental results showed that the greedy algorithm is the fastest, but it yields less optimal routes compared to other methods. The 2-opt algorithm did not provide satisfactory results due to a significant increase in energy consumption and route length. The Ant Colony Optimization and Genetic Algorithms showed the best results, providing optimal routes in terms of energy efficiency and minimization of path length.
As a result of the analysis, it can be argued that the choice of algorithm depends on the specific requirements of the problem. For quickly obtaining an initial solution, it is advisable to use the greedy algorithm, for local optimization – the 2-opt algorithm, and for achieving the best results in minimizing energy consumption and route length – the Ant Colony Optimization or Genetic Algorithms.
Keywords
UAV, travelling salesman problem, greedy method, 2-opt algorithm, ant algorithm, genetic algorithm, graph
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References
1. Berezhnyi, A. O. (2020). Methods and information technology for automated flight route planning of unmanned aerial vehicles to improve object search efficiency. Candidate’s thesis. Kharkiv: Kharkiv National Air Force University named after Ivan Kozhedub [in Ukrainian].
2. Vadis, D., & Avrutov, V. (2024). Methods for improving the functional efficiency of UAVs. Mekhanika gyroskopichnykh system, 48, 55–68. https://doi.org/10.20535/0203-3771482024317891 [in Ukrainian].
3. Sonmez A., Kocyigit E., Kugu E. (2015). Optimal path planning for UAVs using genetic algoritm. International Conference on Unmanned Aircraft Systems (ICUAS). June 9-12, 2015. Denver Marriott Tech CenterDenver, Colorado, USA, 2015. P.50-55. DOI: 10.1109/ICUAS.2015.7152274
4. Wang H., Pan W. (2021) Research on UAV Path Planning Algorithms. 8-th Annual International Conference on Geo-Spatial Knowledge and Intelligence IOP Conf. Series: Earth and Environmental Science 693. 2021. DOI:10.1088/1755-1315/693/1/012120
5. Jiang Y., Xu X.-X., Zheng M.-Y., Zhan Z.-H. (2024). Evolutionary computation for unmanned aerial vehicle path planning: a survey. Artificial Intelligence Review. 2024 Vol. 57, № 10. DOI: 10.1007/s10462-024-10913-0
6. Vorotnikov V., Gumenyuk I., Pozdniakov P. (2017). Planning the flight routes of the unmanned aerial vehicle by solving the travelling salesman problem. Technology Audit and Production Reserves. №4/2(36) (2017)., 44–49. [in Ukrainian] DOI:10.15587/2312-8372.2017.108537.
7. Jones M., Djahel S., Welsh K. (2023). Path-Planning for Unmanned AerialVehicles with Environment Complexity Considerations: A Survey. ACM Computing Surveys. 2023. Vol. 55, № 11. P. 234:1-234:39. DOI:10.1145/3570723
8. Cabreira T. M., Brisolara L. B., Ferreira Jr. P. R. (2019). Survey on Coverage Path Planning with Unmanned Aerial Vehicles. Drones. 2019. Vol.3, № 1. DOI: 10.3390/drones3010004
9. Semeniuta, M.F., Osadchy, S.I., & Chesak, O. (2024). A systems approach to area monitoring using a swarm of UAVs. In Modern problems and achievements in radio engineering, telecommunications and information technologies: Abstracts of the 12th International Scientific and Practical Conference (Zaporizhzhia, December 10–12, 2024), (pp. 459–463). Zaporizhzhia: National University “Zaporizhzhia Polytechnic”. [in Ukrainian].
10. Hulianytskyi, L.F., & Rybalchenko, O.V. (2023). Route optimization in planning missions of hybrid transport systems “drone + vehicle”. Kibernetyka ta kompiuterni tekhnolohii, 3, 44–58. [in Ukrainian]. DOI: 10.34229/2707-451X.23.3.4
11. Uddin F., Riaz N., Manan A., Mahmood I., Song Oh-Y., Malik A. J., Abbasi A. A. (2023). An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour. Applied Sciences. Vol. 13(12) (2023). DOI: 10.3390/app13127339
12. Lin S., Kernighan B.W. An effective heuristic algorithm for the travelling salesman problem. Operations Research. 1973. Vol. 21, № 2. P. 498–516. https://www.cs.princeton.edu/~bwk/btl.mirror/tsp.pdf
13. Ahmed M. R., Shibli A. A.l, Marhaban M. H., Kaiser M. S., Myo T., Albroumi B. (2023). Ant Colony Optimization-Based Path Planning for UAV Navigation in Dynamic Environments. The 7-th International Conference on Automation, Control and Robots (ICACR) August 4-6 2023, Kuala Lumpur, Malaysia,. 2023. P.168-173.
14. Murugananthan V., Rehan M. Y. E. S., Srinivasan R., Kavitha M., Kavitha R. (2023). Traveling Salesman Problem with Ant Colony Optimization. 2-nd International Conference on Edge Computing and Applications (ICECAA). 2023. DOI: 10.1109/ICECAA58104.2023.10212262
15. Flores-Caballero G., Rodríguez-Molina A., Aldape-Pérez M., Villarreal-Cervantes M. G. (2020). Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics. IEEE Access. Vol.8. 2020. P.176774-176788. DOI: 10.1109/ACCESS.2020.3026666
16. Yin Y., Wang Z., Zheng L., Su Q., Guo Y. (2014). Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning. Electronics. 2024, 13(13). DOI: 10.3390/electronics13132432
17. Bose S., Maheswaran N., Logeswari G., Anitha T., Prabhu D., Gokulraj G. (2024). Adaptive Deep Learning Techniques for Real-Time Shortest Path Optimization in Drone Ambulance Operations during Disaster. 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I- SMAC). DOI: 10.1109/I-SMAC61858.2024.10714626.
18. Zhang C., Feng Q. (2020). Research on UAV Path Planning Combined with Ant Colony and A*. IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 2020. DOI: 10.1109/ITNEC48623.2020.9084730.
Citations
1. Бережний А. О. Методи та інформаційна технологія автоматизованого планування маршрутів польотів безпілотних літальних апаратів для підвищення ефективності пошуку об’єктів: дис. на здобуття наукового ступеня канд. техн. наук: 05.13.06 / Харківський національний університет Повітряних Сил імені Івана Кожедуба, Харків, 2020. 192 с.
2. Вадіс Д., Аврутов В. Методи підвищення функціональної ефективності БПЛА. Механіка гіроскопічних систем: науково-технічний збірник. 2024. Вип. 48. С.55-68. URL: https://doi.org/10.20535/0203-3771482024317891
3. Sonmez A., Kocyigit E., Kugu E. (2015). Optimal path planning for UAVs using genetic algoritm. International Conference on Unmanned Aircraft Systems (ICUAS). June 9-12, 2015. Denver Marriott Tech CenterDenver, Colorado, USA, 2015. P.50-55. DOI: 10.1109/ICUAS.2015.7152274
4. Wang H., Pan W. (2021) Research on UAV Path Planning Algorithms. 8-th Annual International Conference on Geo-Spatial Knowledge and Intelligence IOP Conf. Series: Earth and Environmental Science 693. 2021. DOI:10.1088/1755-1315/693/1/012120
5. Jiang Y., Xu X.-X., Zheng M.-Y., Zhan Z.-H. (2024). Evolutionary computation for unmanned aerial vehicle path planning: a survey. Artificial Intelligence Review. 2024 Vol. 57, № 10. DOI: 10.1007/s10462-024-10913-0
6. Vorotnikov V., Gumenyuk I., Pozdniakov P. (2017). Planning the flight routes of the unmanned aerial vehicle by solving the travelling salesman problem. Technology Audit and Production Reserves. №4/2(36) (2017)., 44–49. [in Ukrainian] DOI:10.15587/2312-8372.2017.108537.
7. Jones M., Djahel S., Welsh K. (2023). Path-Planning for Unmanned AerialVehicles with Environment Complexity Considerations: A Survey. ACM Computing Surveys. 2023. Vol. 55, № 11. P. 234:1-234:39. DOI:10.1145/3570723
8. Cabreira T. M., Brisolara L. B., Ferreira Jr. P. R. (2019). Survey on Coverage Path Planning with Unmanned Aerial Vehicles. Drones. 2019. Vol.3, № 1. DOI: 10.3390/drones3010004
9. Семенюта М.Ф., Осадчий С.І., Чесак О. Системний підхід до моніторингу територій з використанням рою БПЛА. Сучасні проблеми і досягнення в галузі радіотехніки, телекомунікацій та інформаційних технологій: тези доповідей ХІІ Міжнар. наук.-практ. конф., 10-12 грудня 2024. р., м. Запоріжжя. [Електронний ресурс] /Електрон. дані. – Запоріжжя: НУ «Запорізька політехніка», 2024. С. 459-463.
10. Гуляницький Л.Ф., Рибальченко О.В. Оптимізація маршрутів при плануванні місій гібридних транспортних систем «дрон+транспортний засіб». Кібернетика та комп'ютерні технології. 2023. №3 (2023). С. 44–58. DOI: 10.34229/2707-451X.23.3.4
11. Uddin F., Riaz N., Manan A., Mahmood I., Song Oh-Y., Malik A. J., Abbasi A. A. (2023). An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour. Applied Sciences. Vol. 13(12) (2023). DOI: 10.3390/app13127339
12. Lin S., Kernighan B.W. An effective heuristic algorithm for the travelling salesman problem. Operations Research. 1973. Vol. 21, № 2. P. 498–516. https://www.cs.princeton.edu/~bwk/btl.mirror/tsp.pdf
13. Ahmed M. R., Shibli A. A.l, Marhaban M. H., Kaiser M. S., Myo T., Albroumi B. (2023). Ant Colony Optimization-Based Path Planning for UAV Navigation in Dynamic Environments. The 7-th International Conference on Automation, Control and Robots (ICACR) August 4-6 2023, Kuala Lumpur, Malaysia, 2023. P.168-173.
14. Murugananthan V., Rehan M. Y. E. S., Srinivasan R., Kavitha M., Kavitha R. (2023). Traveling Salesman Problem with Ant Colony Optimization. 2-nd International Conference on Edge Computing and Applications (ICECAA). 2023. DOI: 10.1109/ICECAA58104.2023.10212262
15. Flores-Caballero G., Rodríguez-Molina A., Aldape-Pérez M., Villarreal-Cervantes M. G. (2020). Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics. IEEE Access. Vol.8. 2020. P.176774-176788. DOI: 10.1109/ACCESS.2020.3026666
16. Yin Y., Wang Z., Zheng L., Su Q., Guo Y. (2014). Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning. Electronics. 2024, 13(13). DOI: 10.3390/electronics13132432
17. Bose S., Maheswaran N., Logeswari G., Anitha T., Prabhu D., Gokulraj G. (2024). Adaptive Deep Learning Techniques for Real-Time Shortest Path Optimization in Drone Ambulance Operations during Disaster. 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I- SMAC). DOI: 10.1109/I-SMAC61858.2024.10714626.
18. Zhang C., Feng Q. (2020). Research on UAV Path Planning Combined with Ant Colony and A*. IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 2020. DOI: 10.1109/ITNEC48623.2020.9084730.
Copyright (c) 2025 Maryna Semeniuta, Serhii Yakymenko, Serhii Osadchy
Copyright (©) 2025, Igor Shelehov, Dmytro Prylepa, Yuliia Khibovska, Kiril Shamonin, Oleksandr Dorenskyi
Optimal Route Planning of Unmanned Aerial Vehicles for Efficient Coverage of a Given Area
About the Authors
Maryna Semeniuta, Associate Professor, PhD in Physics and Mathematics, Associate Professor of the Department of Higher Mathematics and Physics, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-4639-0545, e-mail: semeniutamf@kntu.kr.ua
Serhii Yakymenko, Associate Professor, PhD in Physics and Mathematics, Head of the Department of Higher Mathematics and Physics, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-5759-9603, e-mail: yasm@i.ua
Serhii Osadchy, Professor, Doctor of Technical Sciences, Head of the Department of Flight Operations and Flight Safety, Ukrainian State Flight Academy, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-1811-3594, e-mail: srg2005@ukr.net
Abstract
Keywords
Full Text:
PDFReferences
1. Berezhnyi, A. O. (2020). Methods and information technology for automated flight route planning of unmanned aerial vehicles to improve object search efficiency. Candidate’s thesis. Kharkiv: Kharkiv National Air Force University named after Ivan Kozhedub [in Ukrainian].
2. Vadis, D., & Avrutov, V. (2024). Methods for improving the functional efficiency of UAVs. Mekhanika gyroskopichnykh system, 48, 55–68. https://doi.org/10.20535/0203-3771482024317891 [in Ukrainian].
3. Sonmez A., Kocyigit E., Kugu E. (2015). Optimal path planning for UAVs using genetic algoritm. International Conference on Unmanned Aircraft Systems (ICUAS). June 9-12, 2015. Denver Marriott Tech CenterDenver, Colorado, USA, 2015. P.50-55. DOI: 10.1109/ICUAS.2015.7152274
4. Wang H., Pan W. (2021) Research on UAV Path Planning Algorithms. 8-th Annual International Conference on Geo-Spatial Knowledge and Intelligence IOP Conf. Series: Earth and Environmental Science 693. 2021. DOI:10.1088/1755-1315/693/1/012120
5. Jiang Y., Xu X.-X., Zheng M.-Y., Zhan Z.-H. (2024). Evolutionary computation for unmanned aerial vehicle path planning: a survey. Artificial Intelligence Review. 2024 Vol. 57, № 10. DOI: 10.1007/s10462-024-10913-0
6. Vorotnikov V., Gumenyuk I., Pozdniakov P. (2017). Planning the flight routes of the unmanned aerial vehicle by solving the travelling salesman problem. Technology Audit and Production Reserves. №4/2(36) (2017)., 44–49. [in Ukrainian] DOI:10.15587/2312-8372.2017.108537.
7. Jones M., Djahel S., Welsh K. (2023). Path-Planning for Unmanned AerialVehicles with Environment Complexity Considerations: A Survey. ACM Computing Surveys. 2023. Vol. 55, № 11. P. 234:1-234:39. DOI:10.1145/3570723
8. Cabreira T. M., Brisolara L. B., Ferreira Jr. P. R. (2019). Survey on Coverage Path Planning with Unmanned Aerial Vehicles. Drones. 2019. Vol.3, № 1. DOI: 10.3390/drones3010004
9. Semeniuta, M.F., Osadchy, S.I., & Chesak, O. (2024). A systems approach to area monitoring using a swarm of UAVs. In Modern problems and achievements in radio engineering, telecommunications and information technologies: Abstracts of the 12th International Scientific and Practical Conference (Zaporizhzhia, December 10–12, 2024), (pp. 459–463). Zaporizhzhia: National University “Zaporizhzhia Polytechnic”. [in Ukrainian].
10. Hulianytskyi, L.F., & Rybalchenko, O.V. (2023). Route optimization in planning missions of hybrid transport systems “drone + vehicle”. Kibernetyka ta kompiuterni tekhnolohii, 3, 44–58. [in Ukrainian]. DOI: 10.34229/2707-451X.23.3.4
11. Uddin F., Riaz N., Manan A., Mahmood I., Song Oh-Y., Malik A. J., Abbasi A. A. (2023). An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour. Applied Sciences. Vol. 13(12) (2023). DOI: 10.3390/app13127339
12. Lin S., Kernighan B.W. An effective heuristic algorithm for the travelling salesman problem. Operations Research. 1973. Vol. 21, № 2. P. 498–516. https://www.cs.princeton.edu/~bwk/btl.mirror/tsp.pdf
13. Ahmed M. R., Shibli A. A.l, Marhaban M. H., Kaiser M. S., Myo T., Albroumi B. (2023). Ant Colony Optimization-Based Path Planning for UAV Navigation in Dynamic Environments. The 7-th International Conference on Automation, Control and Robots (ICACR) August 4-6 2023, Kuala Lumpur, Malaysia,. 2023. P.168-173.
14. Murugananthan V., Rehan M. Y. E. S., Srinivasan R., Kavitha M., Kavitha R. (2023). Traveling Salesman Problem with Ant Colony Optimization. 2-nd International Conference on Edge Computing and Applications (ICECAA). 2023. DOI: 10.1109/ICECAA58104.2023.10212262
15. Flores-Caballero G., Rodríguez-Molina A., Aldape-Pérez M., Villarreal-Cervantes M. G. (2020). Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics. IEEE Access. Vol.8. 2020. P.176774-176788. DOI: 10.1109/ACCESS.2020.3026666
16. Yin Y., Wang Z., Zheng L., Su Q., Guo Y. (2014). Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning. Electronics. 2024, 13(13). DOI: 10.3390/electronics13132432
17. Bose S., Maheswaran N., Logeswari G., Anitha T., Prabhu D., Gokulraj G. (2024). Adaptive Deep Learning Techniques for Real-Time Shortest Path Optimization in Drone Ambulance Operations during Disaster. 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I- SMAC). DOI: 10.1109/I-SMAC61858.2024.10714626.
18. Zhang C., Feng Q. (2020). Research on UAV Path Planning Combined with Ant Colony and A*. IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 2020. DOI: 10.1109/ITNEC48623.2020.9084730.
Citations
1. Бережний А. О. Методи та інформаційна технологія автоматизованого планування маршрутів польотів безпілотних літальних апаратів для підвищення ефективності пошуку об’єктів: дис. на здобуття наукового ступеня канд. техн. наук: 05.13.06 / Харківський національний університет Повітряних Сил імені Івана Кожедуба, Харків, 2020. 192 с.
2. Вадіс Д., Аврутов В. Методи підвищення функціональної ефективності БПЛА. Механіка гіроскопічних систем: науково-технічний збірник. 2024. Вип. 48. С.55-68. URL: https://doi.org/10.20535/0203-3771482024317891
3. Sonmez A., Kocyigit E., Kugu E. (2015). Optimal path planning for UAVs using genetic algoritm. International Conference on Unmanned Aircraft Systems (ICUAS). June 9-12, 2015. Denver Marriott Tech CenterDenver, Colorado, USA, 2015. P.50-55. DOI: 10.1109/ICUAS.2015.7152274
4. Wang H., Pan W. (2021) Research on UAV Path Planning Algorithms. 8-th Annual International Conference on Geo-Spatial Knowledge and Intelligence IOP Conf. Series: Earth and Environmental Science 693. 2021. DOI:10.1088/1755-1315/693/1/012120
5. Jiang Y., Xu X.-X., Zheng M.-Y., Zhan Z.-H. (2024). Evolutionary computation for unmanned aerial vehicle path planning: a survey. Artificial Intelligence Review. 2024 Vol. 57, № 10. DOI: 10.1007/s10462-024-10913-0
6. Vorotnikov V., Gumenyuk I., Pozdniakov P. (2017). Planning the flight routes of the unmanned aerial vehicle by solving the travelling salesman problem. Technology Audit and Production Reserves. №4/2(36) (2017)., 44–49. [in Ukrainian] DOI:10.15587/2312-8372.2017.108537.
7. Jones M., Djahel S., Welsh K. (2023). Path-Planning for Unmanned AerialVehicles with Environment Complexity Considerations: A Survey. ACM Computing Surveys. 2023. Vol. 55, № 11. P. 234:1-234:39. DOI:10.1145/3570723
8. Cabreira T. M., Brisolara L. B., Ferreira Jr. P. R. (2019). Survey on Coverage Path Planning with Unmanned Aerial Vehicles. Drones. 2019. Vol.3, № 1. DOI: 10.3390/drones3010004
9. Семенюта М.Ф., Осадчий С.І., Чесак О. Системний підхід до моніторингу територій з використанням рою БПЛА. Сучасні проблеми і досягнення в галузі радіотехніки, телекомунікацій та інформаційних технологій: тези доповідей ХІІ Міжнар. наук.-практ. конф., 10-12 грудня 2024. р., м. Запоріжжя. [Електронний ресурс] /Електрон. дані. – Запоріжжя: НУ «Запорізька політехніка», 2024. С. 459-463.
10. Гуляницький Л.Ф., Рибальченко О.В. Оптимізація маршрутів при плануванні місій гібридних транспортних систем «дрон+транспортний засіб». Кібернетика та комп'ютерні технології. 2023. №3 (2023). С. 44–58. DOI: 10.34229/2707-451X.23.3.4
11. Uddin F., Riaz N., Manan A., Mahmood I., Song Oh-Y., Malik A. J., Abbasi A. A. (2023). An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour. Applied Sciences. Vol. 13(12) (2023). DOI: 10.3390/app13127339
12. Lin S., Kernighan B.W. An effective heuristic algorithm for the travelling salesman problem. Operations Research. 1973. Vol. 21, № 2. P. 498–516. https://www.cs.princeton.edu/~bwk/btl.mirror/tsp.pdf
13. Ahmed M. R., Shibli A. A.l, Marhaban M. H., Kaiser M. S., Myo T., Albroumi B. (2023). Ant Colony Optimization-Based Path Planning for UAV Navigation in Dynamic Environments. The 7-th International Conference on Automation, Control and Robots (ICACR) August 4-6 2023, Kuala Lumpur, Malaysia, 2023. P.168-173.
14. Murugananthan V., Rehan M. Y. E. S., Srinivasan R., Kavitha M., Kavitha R. (2023). Traveling Salesman Problem with Ant Colony Optimization. 2-nd International Conference on Edge Computing and Applications (ICECAA). 2023. DOI: 10.1109/ICECAA58104.2023.10212262
15. Flores-Caballero G., Rodríguez-Molina A., Aldape-Pérez M., Villarreal-Cervantes M. G. (2020). Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics. IEEE Access. Vol.8. 2020. P.176774-176788. DOI: 10.1109/ACCESS.2020.3026666
16. Yin Y., Wang Z., Zheng L., Su Q., Guo Y. (2014). Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning. Electronics. 2024, 13(13). DOI: 10.3390/electronics13132432
17. Bose S., Maheswaran N., Logeswari G., Anitha T., Prabhu D., Gokulraj G. (2024). Adaptive Deep Learning Techniques for Real-Time Shortest Path Optimization in Drone Ambulance Operations during Disaster. 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I- SMAC). DOI: 10.1109/I-SMAC61858.2024.10714626.
18. Zhang C., Feng Q. (2020). Research on UAV Path Planning Combined with Ant Colony and A*. IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 2020. DOI: 10.1109/ITNEC48623.2020.9084730.
Copyright (©) 2025, Igor Shelehov, Dmytro Prylepa, Yuliia Khibovska, Kiril Shamonin, Oleksandr Dorenskyi