Optimal Multi-UAV Path Planning for Monitoring in Windy Conditions and under Uncertainty

Authors

  • Mohammad Sanjari-Parizi * Department of Industrial Engineering, KN Toosi University of Technology, Tehran, Iran. https://orcid.org/0009-0003-9808-2314
  • Hossein Pouralihasani Gohar Zamin Mining and Industrial Company, Sirjan, Iran.
  • Mohammadali Saadati-Parizi Gohar Zamin Mining and Industrial Company, Sirjan, Iran.

https://doi.org/10.22105/raise.v3i1.84

Abstract

Nowadays, due to the advances in Unmanned Aerial Vehicles (UAV) technology, they are used in various applications, including surveillance and imaging of various areas. In this paper, the problem of routing UAVs to image specific areas with different accuracy requirements is investigated. The mission planning was done considering different weather conditions and wind currents, and the uncertainty in wind speed, which affects the final speed of the drone, was investigated. A mathematical model was presented to determine the optimal flight paths of the drones, while taking into account the imaging accuracy that is adjusted by altitude. Computational experiments were performed to investigate the problem and the presented model using the GAMS software.

Keywords:

Unmanned aerial vehicle, Mission planning, Drone, Aerial monitoring, Vehicle routing problem, Mathematical model

References

  1. [1] Zhen, L., Li, M., Laporte, G., & Wang, W. (2019). A vehicle routing problem arising in unmanned aerial monitoring. Computers & operations research, 105, 1–11. https://doi.org/10.1016/j.cor.2019.01.001
  2. [2] Ristorto, G., D’Incalci, P., Gallo, R., Mazzetto, F., & Guglieri, G. (2017). Mission planning for the estimation of the field coverage of unmanned aerial systems in monitoring mission in precision farming. Chemical engineering transactions, 58, 649–654. https://doi.org/10.3303/CET1758109
  3. [3] Merino, L., Caballero, F., Martínez-de Dios, J. R., Ferruz, J., & Ollero, A. (2006). A cooperative perception system for multiple UAVs: Application to automatic detection of forest fires. Journal of field robotics, 23(3–4), 165–184. https://doi.org/10.1002/rob.20108
  4. [4] Alotaibi, K. A., Rosenberger, J. M., Mattingly, S. P., Punugu, R. K., & Visoldilokpun, S. (2018). Unmanned aerial vehicle routing in the presence of threats. Computers & industrial engineering, 115, 190–205. https://doi.org/10.1016/j.cie.2017.10.030
  5. [5] Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M. D., & Camacho, D. (2017). Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms. Soft computing, 21(17), 4883–4900. https://doi.org/10.1007/s00500-016-2376-7
  6. [6] Mohsan, S. A. H., Othman, N. Q. H., Li, Y., Alsharif, M. H., & Khan, M. A. (2023). Unmanned aerial vehicles (UAVs): Practical aspects, applications, open challenges, security issues, and future trends. Intelligent service robotics, 16(1), 109–137. https://doi.org/10.1007/s11370-022-00452-4
  7. [7] Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management science, 6(1), 80–91. https://doi.org/10.1287/mnsc.6.1.80
  8. [8] Liang, J., Zhong, H., Wang, Y., Chen, Y., Zeng, J., & Mao, J. (2023). Adaptive force tracking impedance control for aerial interaction in uncertain contact environment using barrier function. IEEE transactions on automation science and engineering, 21(3), 4720–4731. https://doi.org/10.1109/TASE.2023.3301023
  9. [9] Carvajal, C. P., Andaluz, V. H., Roberti, F., & Carelli, R. (2023). Path-following control for aerial manipulators robots with priority on energy saving. Control engineering practice, 131, 105401. https://doi.org/10.1016/j.conengprac.2022.105401
  10. [10] Han, Y., Li, J., Liu, Z., Liu, C., & Tian, J. (2020). Metaheuristic algorithm for solving the multi-objective vehicle routing problem with time window and drones. International journal of advanced robotic systems, 17(2), 1729881420920031. https://doi.org/10.1177/1729881420920031
  11. [11] Thibbotuwawa, A., Bocewicz, G., Radzki, G., Nielsen, P., & Banaszak, Z. (2020). UAV mission planning resistant to weather uncertainty. Sensors, 20(2), 515. https://doi.org/10.3390/s20020515
  12. [12] Sanjari-Parizi, M., Pouralihasani, H., & Meymand, A. Z. (2024). Multi-objective optimization for combined ground and aerial delivery systems using drones. In international conference on industrial and systems engineering. https://www.researchgate.net/publication/394195466_Multi-objective_optimization_for_combined_ground_and_aerial_delivery_systems_using_drones
  13. [13] Hayat, S., Yanmaz, E., Bettstetter, C., & Brown, T. X. (2020). Multi-objective drone path planning for search and rescue with quality-of-service requirements. Autonomous robots, 44(7), 1183–1198. https://doi.org/10.1007/s10514-020-09926-9
  14. [14] Yuan, S., Ota, K., Dong, M., & Zhao, J. (2022). A path planning method with perception optimization based on sky scanning for UAVs. Sensors, 22(3), 891. https://doi.org/10.3390/s22030891
  15. [15] Huang, Y., & Hammad, A. (2024). Simulation-based optimization of path planning for camera-equipped uavs that considers the location and time of construction activities. Remote sensing, 16(13), 2445. https://doi.org/10.3390/rs16132445
  16. [16] Chang, B., Hwang, B., Lim, W., Kim, H., Kang, W., Park, Y. S., & Ko, D. W. (2025). Enhancing wildlife detection using thermal imaging drones: Designing the flight path. Drones, 9(1), 52. https://doi.org/10.3390/drones9010052

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Published

2026-03-25

Issue

Vol. 3 No. 1 (2026): Research Annals of Industrial and Systems Engineering

Section

Articles

How to Cite

Sanjari-Parizi, M., Pouralihasani, H. ., & Saadati-Parizi, M. (2026). Optimal Multi-UAV Path Planning for Monitoring in Windy Conditions and under Uncertainty. Research Annals of Industrial and Systems Engineering, 3(1), 65-75. https://doi.org/10.22105/raise.v3i1.84

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