Aerial Deliveries by Drones: A Performance Evaluation Considering Drone Collisions and Repair Logistics

• Leonel Feitosa UFPI http://orcid.org/0000-0002-9972-4679
• Vandirleya Barbosa UFPI
• Luiz Fernando Bittencourt UNICAMP https://orcid.org/0000-0001-6305-9059
• Fabíola M. C. de Oliveira UFABC
• José Valdemir R. Junior UFPI
• Francisco Airton Silva UFPI https://orcid.org/0000-0002-8211-6060

DOI: https://doi.org/10.5753/sbrc.2025.6317

Abstract

The growing adoption of drones for goods delivery has emerged as a potentially viable solution. By operating through aerial routes, drones significantly reduce delivery times and expand the operational reach. However, covering large areas requires prolonged flights, leading to high battery consumption and an increased risk of collisions, particularly in densely populated regions. This study presents a Stochastic Petri Net model to evaluate drone performance, focusing on metrics such as utilization, delivery rate, and mean mission time. The model incorporates factors such as strategic recharging points and collision probability, providing insights into drone performance under high-demand scenarios. Additionally, a sensitivity analysis was conducted using the Design of Experiments to identify the most influential factors impacting drone performance, enabling a deeper understanding and optimization of the system.

Keywords: Drones, Deliveries, Collisions, Petri Nets

References

Antony, J. Design of experiments: application concepts, examples, state of the art. The TQM Journal, 30(3):294–319, 2018.

Bause, F. and Kritzinger, P. S. Stochastic petri nets, volume 1. Vieweg Wiesbaden, 2002.

Behroozi, M. and Ma, D. Last mile delivery with drones and sharing economy. arXiv preprint arXiv:2308.16408, 2023.

Bhuiyan, T. H., Walker, V., Roni, M., and Ahmed, I. Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products. Expert Systems with Applications, 252:124172, 2024.

de Oliveira, F. M. C., Bittencourt, L. F., Bianchi, R. A. C., and Kamienski, C. A. Drones in the Big City: Autonomous Collision Avoidance for Aerial Delivery Services. IEEE Transactions on Intelligent Transportation Systems, 25(5):4657–4674, 2024.

Devos, A., Ebeid, E., and Manoonpong, P. Development of autonomous drones for adaptive obstacle avoidance in real world environments. In 2018 21st Euromicro conference on digital system design (DSD), pages 707–710. IEEE, 2018.

Dhote, J. and Limbourg, S. Designing unmanned aerial vehicle networks for biological material transportation–the case of Brussels. Computers & Industrial Engineering, 148:106652, 2020.

Du, L., Li, X., Gan, Y., and Leng, K. Optimal model and algorithm of medical materials delivery drone routing problem under major public health emergencies. Sustainability, 14(8):4651, 2022.

Frizo Drone (2024). Reparos e Manutenção de Drones DJI. [link]. Acessado em: 21 jan. 2025.

Girault, C. and Valk, R. Petri nets for systems engineering: a guide to modeling, verification, and applications. Springer Science & Business Media, 2013.

Hicks, A. Medrone. British Medical Journal, 1(5129):1121, 1959.

Huang, C., Ming, Z., and Huang, H. Drone stations-aided beyond-battery-lifetime flight planning for parcel delivery. IEEE Transactions on Automation Science and Engineering, 20(4):2294–2304, 2023.

Iqbal, D. and Buhnova, B. Model-based approach for building trust in autonomous drones through digital twins. In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 656–662. IEEE, 2022.

Jain, R. The art of computer systems performance analysis: techniques for experimental design, measurement, simulation, and modeling. John Wiley & Sons, 1990.

JOUAV (2024). How Much Does A Drone Cost in 2024 Here’s a Price Breakdown. [link]. Acessado em: 21 jan. 2025.

Lord Drone (2024). Especialistas em Assistência Técnica DJI – Lord Care. [link]. Acessado em: 21 jan. 2025.

Maciel, P., Matos, R., Silva, B., Figueiredo, J., Oliveira, D., Fé, I., Maciel, R., and Dantas, J. Mercury: Performance and dependability evaluation of systems with exponential, expolynomial, and general distributions. In 2017 IEEE 22nd Pacific Rim international symposium on dependable computing (PRDC), pages 50–57. IEEE, 2017.

Mahulea, C., Recalde, L., and Silva, M. Observability of continuous petri nets with infinite server semantics. Nonlinear Analysis: Hybrid Systems, 4(2):219–232, 2010.

Mateen, F. J., Leung, K. B., Vogel, A. C., Cisse, A. F., and Chan, T. C. A drone delivery network for antiepileptic drugs: a framework and modelling case study in a low-income country. Transactions of The Royal Society of Tropical Medicine and Hygiene, 114(4):308–314, 2020.

Namiki, K. Modeling and simulation for optimizing drone operation rate by combining hybrid policies. In 2022 IEEE Global Conference on Life Sciences and Technologies (LifeTech), pages 162–166. IEEE, 2022.

Rahman, M. S., Khalil, I., and Atiquzzaman, M. Blockchain-powered policy enforcement for ensuring flight compliance in drone-based service systems. IEEE Network, 35(1):116–123, 2021.

Shi, Y., Lin, Y., Li, B., and Li, R. Y. M. A bi-objective optimization model for the medical supplies’ simultaneous pickup and delivery with drones. Computers & Industrial Engineering, 171:108389, 2022.

Silva, F. A., Barbosa, V., Sabino, A., Lima, L. N., Fé, I., Rego, P., and Bittencourt, L. F. Entregas Aéreas por Drones Cooperativos: Uma Avaliação de Desempenho Considerando Pontos de Recarga de Bateria. In Anais do LI Seminário Integrado de Software e Hardware (SEMISH). SBC, 2024.

Silva, L. G., Cardoso, I., Brito, C., Barbosa, V., Nogueira, B., Choi, E., Nguyen, T. A., Min, D., Lee, J. W., and Silva, F. A. Urban advanced mobility dependability: A model-based quantification on vehicular ad hoc networks with virtual machine migration. Sensors, 23(23):9485, 2023.

Statista Research Department (2024). Global drone delivery market size 2021-2027. [link]. Acessado em: 9 nov. 2024.