INTELLIGENT DRONE ROUTE PLANNING IN URBAN ENVIRONMENTS: OPTIMIZATION AND SAFETY
DOI:
https://doi.org/10.37943/25ONQV4873Keywords:
drone delivery, route optimization, Traveling Salesman Problem, OpenStreetMap, wind conditions, building height estimation, urban route planning, PythonAbstract
This study develops and evaluates an intelligent route-planning model for unmanned aerial vehicles operating in urban environments with the objective of minimizing total flight time while maintaining flight safety. The proposed approach addresses a practical last-mile delivery scenario in a smart-city context and is implemented using Google OR-Tools within a constrained Traveling Salesman Problem framework.
Open geospatial data from OpenStreetMap are used to obtain building geometries, street topology, and available height-related attributes, while meteorological data from OpenWeatherMap are used to account for wind conditions in flight-time estimation. Safe cruising altitude is determined from the maximum surrounding obstacle height with an additional safety margin. If explicit building-height data are unavailable, height is approximated from the number of building levels.
A prototype system was developed to visualize routes, estimate route distance, flight time, and delivery cost, and export missions in MAVLink/QGroundControl-compatible format. Experimental scenarios for real delivery points in the Astana metropolitan area, including Astana, Kosshy, and Koyandy demonstrate the practical feasibility of the proposed open-data-driven routing approach and show improvements over a baseline sequential routing strategy.
The scientific contribution of the study lies in the integration of open geospatial building data, weather-aware flight-time estimation, and safety-oriented altitude selection into a reproducible urban UAV route-planning framework. The proposed method can support the development of safe and cost-efficient drone delivery services in dense urban environments.
Experimental scenarios with increasing numbers of delivery points demonstrated that the proposed method remains computationally efficient, with sub-second solver runtime for the tested cases for practical smart-city logistics applications.
References
Důbravová, H., Bureš, V., & Velfl, L. (2024). Review of the application of drones for smart cities. IET Smart Cities, 6(4), 312–332. https://doi.org/10.1049/smc2.12093
Garg, V., Niranjan, S., Prybutok, V., Pohlen, T., & Gligor, D. (2023). Drones in last-mile delivery: A systematic review on efficiency, accessibility and sustainability. Transportation Research Part D: Transport and Environment, 123, 103831. https://doi.org/10.1016/j.trd.2023.103831
Rajabi, M. S., Beigi, P., & Aghakhani, S. (2023). Drone delivery systems and energy management: A review and future trends. In Handbook of Smart Energy Systems (pp. 1273–1291). https://doi.org/10.1007/978-3-030-97940-9_196
Magdum, A., Nikam-Patil, V., Mokashi, V., & Waykule, J. (2020). Smart drone delivery system. International Journal of Scientific Research in Engineering and Management (IJSREM), 4(3). ISSN 2582-3930.
Adeoye, Y., Onotole, E. F., Ogunyankinnu, T., Aipoh, G., Osunkanmi, A. A., & Egbemhenghe, J. (2025). Artificial intelligence in logistics and distribution: The function of AI in dynamic route planning for transportation, including self-driving trucks and drone delivery systems. World Journal of Advanced Research and Reviews, 25(2), 155–167. https://doi.org/10.30574/wjarr.2025.25.2.0214
Kim, B.-H. (2018). Implementation of the centralized control system for drone training. International Journal of Engineering and Technology, 7(3.24), 595–599. https://doi.org/10.14419/ijet.v7i2.33.14190
Raivi, A. M., Huda, S. M. A., Alam, M. M., & Moh, S. (2023). Drone routing for drone-based delivery systems: A review of trajectory planning, charging, and security. Sensors, 23(3), 1463. https://doi.org/10.3390/s23031463
Li, J. (2024). The use of AI technology in drone delivery [Online]. AIFT – Laboratory for AI-Powered Financial Technologies, May 14, 2024. Available: https://hkaift.com/the-use-of-ai-technology-in-drone-delivery/ (accessed: 12.09.2025).
Daud, S. M. S. M., Yusof, M. Y. P. M., Heo, C. C., Khoo, L. S., Singh, M. K. C., Mahmood, M. S., & Nawawi, H. (2022). Applications of drone in disaster management: A scoping review. Science & Justice, 62(1), 30–42. https://doi.org/10.1016/j.scijus.2021.11.002
Rinaldi, M., Primatesta, S., & Guglieri, G. (2025). Low-noise path planning for urban drone missions: Acoustic ray tracing and DDPG algorithm. The Aeronautical Journal, 129(1320), 1–23. https://doi.org/10.1017/aer.2025.10054
Sarhan, S., Rinaldi, M., Primatesta, S., & Guglieri, G. (2025). Noise-aware UAV path planning in urban environment with reinforcement learning. Engineering Proceedings, 90(1), 3. https://doi.org/10.3390/engproc2025090003
Rienecker, H., Hildebrand, V., & Pfifer, H. (2023). Energy optimal 3D flight path planning for unmanned aerial vehicle in urban environments. CEAS Aeronautical Journal, 14, 621–636. https://doi.org/10.1007/s13272-023-00666-x
Choudhury, S., Solovey, K., Kochenderfer, M. J., & Pavone, M. (2020, June 11). Efficient large-scale multi-drone delivery using transit networks (arXiv:1909.11840v4) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1909.11840
Fu, Y., An, W., Su, X., & Song, B. (2025). Real-Time Wind Estimation for Fixed-Wing UAVs. Drones, 9(8), 563. https://doi.org/10.3390/drones9080563
Sziroczak, D., Rohacs, D., & Rohacs, J. (2022). Review of using small UAV-based meteorological measurements for road weather management. Progress in Aerospace Sciences, 134, 100859. https://doi.org/10.1016/j.paerosci.2022.100859
Adkins, K. A., Becker, W., Ayyalasomayajula, S., Lavenstein, S., Vlachou, K., Miller, D., Compere, M., Krishnan, A. M., & Macchiarella, N. (2023). Hyper-local weather predictions with the enhanced general urban area microclimate predictions tool. Drones, 7, 428. https://doi.org/10.3390/drones7070428
Lee, S., Kim, E., & Baik, H. (2024). Path planning for urban air mobility considering weather conditions. In Proceedings of the 2024 AIAA DATC / IEEE 43rd Digital Avionics Systems Conference (DASC), September 29, 2024, pp. 1–6. https://doi.org/10.1109/DASC62030.2024.10749551
Benoit, A., & Asef, P. (2025, March 5). Navigating intelligence: A survey of Google OR-Tools and machine learning for global path planning in autonomous vehicles (arXiv Preprint arXiv:2503.03338). https://doi.org/10.48550/arXiv.2503.03338
Tenkanen, H., Heikinheimo, V., & Whipp, D. (2020–2025). Retrieving OpenStreetMap data. In Python for Geographic Data Analysis (Part II, Chapter 9). Retrieved from https://pythongis.org/part2/chapter-09/nb/00-retrieving-osm-data.html
Kang, L., Wang, Q., & Yan, H. W. (2018). Building extraction based on OpenStreetMap tags and very high spatial resolution image in urban area. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3, 715-718. https://doi.org/10.5194/isprs-archives-XLII-3-715-2018
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Articles are open access under the Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish a manuscript in this journal agree to the following terms:
- The authors reserve the right to authorship of their work and transfer to the journal the right of first publication under the terms of the Creative Commons Attribution License, which allows others to freely distribute the published work with a mandatory link to the the original work and the first publication of the work in this journal.
- Authors have the right to conclude independent additional agreements that relate to the non-exclusive distribution of the work in the form in which it was published by this journal (for example, to post the work in the electronic repository of the institution or publish as part of a monograph), providing the link to the first publication of the work in this journal.
- Other terms stated in the Copyright Agreement.