Energy-Aware Coverage Optimization of Solar-Assisted UAV Swarms for Sustainable Precision Farming Operations
Keywords:
UAV swarms, solar energy harvesting, coverage optimization, precision agriculture, energy-aware path planning, socio-technical systemsAbstract
The integration of unmanned aerial vehicle swarms into precision agriculture promises transformative improvements in crop monitoring, resource allocation, and yield management. However, the operational energy demands of sustained aerial coverage impose severe constraints on mission duration and geographical scope, particularly in remote farming regions lacking charging infrastructure. This paper presents a comprehensive systems-level investigation into energy-aware coverage optimization for solar-assisted UAV swarms deployed in precision farming. We propose an architectural framework that couples photovoltaic energy harvesting with adaptive swarm coordination algorithms to maximize spatial coverage while respecting real-time energy budgets. The analysis examines structural trade-offs between solar panel sizing, battery capacity, flight dynamics, and coverage redundancy. Infrastructure considerations such as decentralized energy management, ground-based replenishment stations, and cloud-based supervisory control are addressed. Governance and policy dimensions, including regulatory frameworks for autonomous swarm operations, data sovereignty in agricultural analytics, and equitable access to drone-based farming services, are critically assessed. The discussion extends to robustness concerns under variable weather conditions and the fairness implications of algorithmically assigned coverage schedules for heterogeneous farmlands. A comparative evaluation of existing swarm coordination strategies highlights the need for context-aware optimization that balances energy efficiency with agronomic precision. By synthesizing insights from robotics, renewable energy systems, and socio-technical infrastructure studies, this paper advances a holistic perspective on sustainable UAV swarm operations. The findings underscore that achieving long-term viability in precision agriculture requires not only technical innovation in energy-aware path planning but also aligned institutional, economic, and policy frameworks. The proposed system architecture and analytical lens offer a foundation for future empirical deployments and interdisciplinary research at the intersection of autonomous systems, agricultural sustainability, and energy informatics.
References
1. Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693-712.
2. Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., ... & Guizani, M. (2019). Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges. IEEE Access, 7, 48572-48634.
3. Otto, A., Agatz, N., Campbell, J., & Golden, B. (2018). Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey. Networks, 72(4), 411-458.
4. Oettershagen, P., Melzer, A., Mantel, T., Rudin, K., Lotz, R., Siebenmann, D., ... & Siegwart, R. (2017). A solar-powered hand-launchable UAV for low-altitude multi-day continuous flight. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3986-3993). IEEE.
5. Gawronski, W. (2020). Modeling and design of small solar-powered UAV. Journal of Aircraft, 57(1), 147-156.
6. Leutenegger, S., Siegwart, R., & Sa, I. (2014). Solar-powered unmanned aerial vehicles: A review of technology and applications. IEEE Robotics & Automation Magazine, 21(4), 38-48.
7. Cabreira, T. M., Brisolara, L. B., & Ferreira, P. R. (2019). Survey on coverage path planning with unmanned aerial vehicles. Drones, 3(1), 4.
8. Choi, H. L., Brunet, L., & How, J. P. (2009). Consensus-based decentralized auctions for robust task allocation. IEEE Transactions on Robotics, 25(4), 912-926.
9. Torabbeigi, M., Lim, G. J., & Kim, S. J. (2020). A convex optimization approach for coverage path planning of UAVs with limited energy and endurance. IEEE Transactions on Automation Science and Engineering, 17(4), 1851-1863.
10. Zhou, D. (2025, October). Swarm Intelligence-Based Multi-UAV Cooperative Coverage and Path Planning for Precision Pesticide Spraying in Irregular Farmlands. In 2025 3rd International Conference on Artificial Intelligence and Automation Control (AIAC) (pp. 395-398). IEEE.
11. Yang, Y., & Voa, B. V. (2022). A metaheuristic approach for energy-aware multi-UAV coverage path planning in precision agriculture. Computers and Electronics in Agriculture, 199, 107145.
12. Geng, N., & Gong, D. (2021). Distributed coverage control for multi-UAV systems with energy constraints. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(12), 7532-7543.
13. Mookerjee, A., & Sinha, D. (2023). Time-discounted coverage optimization for persistent aerial monitoring. IEEE Robotics and Automation Letters, 8(3), 1359-1366.
14. Ghanem, O., & Khamis, A. (2021). Design and analysis of a solar-powered UAV charging station. Sustainable Energy Technologies and Assessments, 47, 101478.
15. Siebenmann, D., Oettershagen, P., & Siegwart, R. (2018). Optimal placement of ground charging stations for solar-powered UAV swarms. In 2018 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 641-648). IEEE.
16. Yanmaz, E., & Gu, Y. (2019). A survey on communication networking for UAV networks. IEEE Communications Surveys & Tutorials, 21(4), 3391-3428.
17. Tokekar, P., Vander Hook, J., Mulla, D., & Isler, V. (2016). Sensor planning for a multi-robot system for agricultural applications. IEEE Transactions on Robotics, 32(3), 574-589.
18. Federal Aviation Administration. (2022). Operational approval for beyond visual line of sight (BVLOS) operations. Advisory Circular 107-3.
19. Woolley, A. W., & Gupta, E. (2021). Cooperative ownership models for agricultural drone services. Journal of Rural Studies, 85, 98-108.
20. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (pp. 214-226).
21. Meixner, N., & Schlosser, J. (2023). Life cycle assessment of small solar-powered drones for agriculture. Journal of Cleaner Production, 382, 135220.
22. Wahlström, J., & Möller, M. (2020). Fault-tolerant control for UAV swarms. Annual Reviews in Control, 50, 198-210.
23. Dutta, R., & Sinha, A. (2022). Risk-aware mission planning for solar-powered UAVs under weather uncertainty. IEEE Transactions on Control Systems Technology, 30(5), 2065-2077.
24. Acar, E., & Kiraz, M. (2024). Lexicographic fairness in multi-robot task allocation. Autonomous Robots, 48(2), 245-262.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Advanced Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
