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Modeling Drone Deliveries Using Petri Nets: An Evaluation on Collision Recovery and Energy Efficiency

Authors: Leonel Feitosa; Vandirleya Barbosa; Luis Guilherme Silva; Iure Fé; Fabíola Martins Campos de Oliveira; Luiz Fernando Bittencourt; Huber Flores; +1 Authors

Modeling Drone Deliveries Using Petri Nets: An Evaluation on Collision Recovery and Energy Efficiency

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 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, mean mission time, and drop probability. Additionally, energy consumption and carbon footprint metrics were investigated to assess the environmental impact of drone operations. The model incorporates factors such as strategic recharging points and collision probability, providing insights into drone performance under high-demand scenarios.

Keywords

RS.C3LA, WP.CL5

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green