WIND AND PAYLOAD AWARE FEASIBILITY MODELLING FOR UAV MOUNTAIN RESUPPLY: A REPRODUCIBLE BUCEGI MOUNTAINS CASE STUDY
Abstract
We present a reproducible, equation-based framework for UAV resupply in mountainous terrain, where feasibility is driven by coupled headwind time and payload energy effects. A compact multirotor power model, wind-aware segment timing, and an explicit battery-reserve policy form a single feasibility test, visualized as headwind payload envelopes and energy sensitivity curves. The method is demonstrated on a Bucegi Mountains network (Sinaia depot, Piatra Arsă micro-hub, Babele and Omu) and yields payload-tier go/no-go rules, including a sharp wind cliff as headwind approaches the commanded-airspeed limit (tested to 6 m/s).
Full Text:
PDFReferences
Options Consultancy Services, Flying medicines over mountains: Nepal drones last mile delivery innovation, Options Consultancy Services, 2021. [Online]. Available: https://options.co.uk/wp-content/uploads/2021/04/Flying-medicines-over-mountains-Nepal-drones-last-mile-delivery-innovation.pdf
P. Thapa and M. Thapa, Operational efficiency and environmental impact of UAV-based vaccine delivery in remote areas of Nepal, Res., vol. 2, no. 2, p. 100207, Jun. 2025, doi: 10.1016/j.nexres.2025.100207.
J. Chute, UNICEF Guidance on the Use of Drones in Supply Chains, UNICEF Supply Division, 2021. [Online]. Available: https://www.unicef.org/supply/media/10326/file/UNICEF-Guidance-on-use-of-drones-in-supply-chains.pdf
A. M. Knoblauch et al., Bi-directional drones to strengthen healthcare provision: experiences and lessons from Madagascar, Malawi and Senegal, BMJ Glob. Health, vol. 4, no. 4, p. e001541, Jul. 2019, doi: 10.1136/bmjgh-2019-001541.
IDinsight, Zipline Brief (Digital v3), IDinsight, 2022. [Online]. Available: https://www.idinsight.org/wp-content/uploads/2022/06/Zipline-Brief-FINAL-Digital-v3.pdf
M. P. Nisingizwe et al., Effect of unmanned aerial vehicle (drone) delivery on blood product delivery time and wastage in Rwanda: a retrospective, cross-sectional study and time series analysis, Lancet Glob. Health, vol. 10, no. 4, pp. e564–e569, Apr. 2022, doi: 10.1016/S2214-109X(22)00048-1.
G. Minculete, Military Logistics Drones: The Innovative Solution for Transportation Challenges on the Battlefield, Land Forces Acad. Rev., vol. 30, no. 2(118), pp. 342–354, 2025, doi: 10.2478/raft-2025-0033.
European Commission, Commission Implementing Regulation (EU) 2021/664 of 22 April 2021 on a regulatory framework for the U-space (Text with EEA relevance). Apr. 2021. [Online]. Available: https://eur-lex.europa.eu/eli/reg_impl/2021/664/oj/eng
International Civil Aviation Organization, Unmanned Aircraft Systems Traffic Management (UTM) – A Common Framework with Core Principles for Global Harmonization, International Civil Aviation Organization, 2023. [Online]. Available: https://www.icao.int/sites/default/files/left-menu-pdfs/UTM%20Framework%20Edition%204.pdf
M. Beigi, M. Rajabi, and M. Aghakhani, An Overview of Drone Energy Consumption Factors and Models. 2022. doi: 10.48550/arXiv.2206.10775.
K. Dorling, J. Heinrichs, G. G. Messier, and P. Magierowski, Vehicle Routing Problems for Drone Delivery, IEEE Trans. Syst. Man Cybern. Syst., vol. 47, no. 1, pp. 70–85, 2017, doi: 10.1109/TSMC.2016.2582745.
R. Liu, H. Shin, M. Seo, and A. Tsourdos, Energy-aware Route Planning for Drone Delivery Systems, in CEAS EuroGNC 2022 – Conference on Guidance, Navigation and Control, Berlin, Germany, May 2022. [Online]. Available: https://eurognc.ceas.org/archive/EuroGNC2022/pdf/CEAS-GNC-2022-045.pdf
W. Liu, W. Li, Q. Zhou, Q. Die, and Y. Yang, The Optimization of the ‘UAV-Vehicle’ Joint Delivery Route Considering Mountainous Cities, PLOS ONE, vol. 17, no. 3, p. e0265518, Mar. 2022, doi: 10.1371/journal.pone.0265518.
M. Østbø, P. Osen, G. Rokseth, O. V. Homleid, and T. Sevaldrud, Exploiting Meteorology to Enhance the Efficiency and Safety of UAV Operations, Norwegian Defence Research Establishment (FFI), FFI/RAPPORT-2004/00981, Mar. 2004. [Online]. Available: https://www.ffi.no/publikasjoner/arkiv/exploiting-meteorology-to-enhance-the-efficiency-and-safety-of-uav-operations
A. Oakey, T. Cherrett, J. Green, and J. Dickinson, Quantifying Weather Tolerance Criteria for Delivery Drones – A UK Case Study, in 2023 International Conference on Unmanned Aircraft Systems (ICUAS), Warsaw, Poland, Jun. 2023, pp. 1005–1012. doi: 10.1109/ICUAS57906.2023.10156144.
L. Palazzetti, C. M. Pinotti, and G. Rigoni, A Run in the Wind: Favorable Winds Make the Difference in Drone Delivery, in 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), Jul. 2021, pp. 109–116. doi: 10.1109/DCOSS52077.2021.00031.
F. Yacef, N. Rizoug, O. Bouhali, and M. Hamerlain, Optimization of Energy Consumption for Quadrotor UAV, in Proceedings of the 9th International Micro Air Vehicle Conference and Competition (IMAV 2017), 2017. [Online]. Available: https://www.imavs.org/papers/2017/31.pdf
S. Ito et al., Load and Wind Aware Routing of Delivery Drones, Drones, vol. 6, no. 2, p. 50, 2022, doi: 10.3390/drones6020050.
F. Betti Sorbelli, F. Corò, S. K. Das, and C. M. Pinotti, Energy-Constrained Delivery of Goods with Drones Under Varying Wind Conditions. 2020. doi: 10.48550/arXiv.2012.08602.
C. Cheng, Y. Adulyasak, L.-M. Rousseau, and M. Sim, Robust Drone Delivery with Weather Information, 2020, [Online]. Available: https://hanalog.ca/wp-content/uploads/2020/07/7897.pdf
ESSP and EUSPA, EGNOS Guidelines for Low-Risk UAS Operations, European Satellite Services Provider (ESSP) and European Union Agency for the Space Programme (EUSPA), Version 01-00, Mar. 2025. [Online]. Available: https://egnos.gsc-europa.eu/sites/default/files/2025-03/Guidelines%20for%20EGNOS%20in%20low-risk%20UAS%20operations.pdf
P. Brown, M. Evans, D. Hunt, J. McIntosh, B. Pender, and J. Ramagge, Introduction to Coordinate Geometry: A guide for teachers – Years 9–10 (TIMES Project, Module 29). Australian Mathematical Sciences Institute (AMSI) / ICE-EM, The University of Melbourne, 2011. [Online]. Available: https://amsi.org.au/teacher_modules/pdfs/Introduction_to_coordinate_geometry.pdf
K. Dawidowicz, Analysis of Height Determination Using the ASG-EUPOS NAWGEO Service, Tech. Sci., vol. 16, no. 1, pp. 19–39, 2013, [Online]. Available: https://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-97e16ee3-a626-433d-be22-d5903ca18533/c/Dawidowicz.pdf
O. Shatalov, Section 1.2: The Dot Product. 2012. [Online]. Available: https://people.tamu.edu/~shatalov68/151LN_section1.2.pdf
R. W. Beard and T. W. McLain, Guidance and Control of Autonomous Miniature Air Vehicles. 2010. [Online]. Available: https://www.et.byu.edu/~beard/classes/ece674/uavbook.pdf
H. Gong, B. Huang, B. Jia, and H. Dai, “Modelling Power Consumptions for Multi-rotor UAVs.” Sep. 2022. doi: 10.48550/arXiv.2209.04128.
Physics LibreTexts, Power. [Online]. Available: https://phys.libretexts.org/Bookshelves/University_Physics/Physics_(Boundless)/06%3A_Uniform_Circular_Motion_and_Gravitation/6.06%3A_Power
P. Munari, T. Dollevoet, and R. Spliet, A generalized formulation for vehicle routing problems, Sep. 2017, [Online]. Available: https://arxiv.org/pdf/1606.01935
C. Kulkarni, E. Hogge, and C. C. Quach, Remaining Flying Time Prediction Implementing Battery Prognostics Framework for Electric UAV’s, in AIAA Propulsion and Energy Forum, Cincinnati, OH, USA, Jul. 2018. [Online]. Available: https://ntrs.nasa.gov/citations/20180004466
M. Gao, C. H. Hugenholtz, T. A. Fox, M. Kucharczyk, T. E. Barchyn, and P. R. Nesbit, Weather Constraints on Global Drone Flyability, Sci. Rep., vol. 11, no. 1, p. 12092, 2021, doi: 10.1038/s41598-021-91325-w.
Google Maps, Coordinate point: 45°21’00.0"N 25°33’05.0"E (45.35, 25.55139). 2026. [Online]. Available: https://www.google.com/maps/place/45%C2%B021'00.0%22N+25%C2%B033'05.0%22E/@45.35,25.5488151,17z/data=!3m1!4b1!4m4!3m3!8m2!3d45.35!4d25.55139?entry=ttu&g_ep=EgoyMDI2MDExMy4wIKXMDSoASAFQAw%3D%3D
Google Maps, Coordinate point: 45°23’01.7"N 25°29’15.9"E (45.383811, 25.487745). 2026. [Online]. Available: https://www.google.com/maps/place/45%C2%B023'01.7%22N+25%C2%B029'15.9%22E/@45.383811,25.4851701,17z/data=!3m1!4b1!4m4!3m3!8m2!3d45.383811!4d25.487745?entry=ttu&g_ep=EgoyMDI2MDExMy4wIKXMDSoASAFQAw%3D%3D
Google Maps, Coordinate point: 45°24’22.2"N 25°28’19.2"E (45.40617, 25.47201). 2026. [Online]. Available: https://www.google.com/maps/place/45%C2%B024'22.2%22N+25%C2%B028'19.2%22E/@45.40617,25.4694351,17z/data=!3m1!4b1!4m4!3m3!8m2!3d45.40617!4d25.47201?entry=ttu&g_ep=EgoyMDI2MDExMy4wIKXMDSoASAFQAw%3D%3D
Google Maps, Coordinate point: 45°26’45.0"N 25°27’22.7"E (45.4458194, 25.4563111). 2026. [Online]. Available: https://www.google.com/maps/place/45%C2%B026'45.0%22N+25%C2%B027'22.7%22E/@45.4458194,25.4537362,17z/data=!3m1!4b1!4m4!3m3!8m2!3d45.4458194!4d25.4563111?entry=ttu&g_ep=EgoyMDI2MDExMy4wIKXMDSoASAFQAw%3D%3D
DJI, Matrice 350 RTK – Specs.Available: https://enterprise.dji.com/matrice-350-rtk/specs
DJI, TB65 Intelligent Flight Battery – Specs. Available: https://enterprise.dji.com/tb65/specs
Refbacks
- There are currently no refbacks.

