Edge4Drone: Managing Landings and Takeoffs in High-Density Distribution Centers

Managing high-density drone landings and takeoffs in urban aerial delivery systems is a challenging, time-critical operation. To bridge existing gaps, we propose and classify eight edge-based solutions and evaluate their performance through high-density airspace simulations encompassing all flight phases. These solutions extend the previously proposed Drone Edge Management System (DREMS) with static and dynamic dronepad allocation strategies and various control levels, including more dynamic strategies. The results demonstrate that proper sequencing significantly reduces collision rates and enhances dronepad utilization. As a contribution, we underline the importance of giving complete control to DREMS during landings and takeoffs and prioritizing landings in dynamic allocations. These insights increase operational efficiency and safety under high request rates and pave the way for more robust and efficient drone delivery systems, highlighting the potential of intelligent sequencing and adaptive control mechanisms.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-19
DOI
https://doi.org/10.5281/zenodo.15052079
Primary Topic
UAV Applications and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Edge4Drone: Managing Landings and Takeoffs in High-Density Distribution Centers

Zenodo (CERN European Organization for Nuclear Research)
UAV Applications and Optimization
article

Edge4Drone: Managing Landings and Takeoffs in High-Density Distribution Centers

article en

Abstract

Managing high-density drone landings and takeoffs in urban aerial delivery systems is a challenging, time-critical operation. To bridge existing gaps, we propose and classify eight edge-based solutions and evaluate their performance through high-density airspace simulations encompassing all flight phases. These solutions extend the previously proposed Drone Edge Management System (DREMS) with static and dynamic dronepad allocation strategies and various control levels, including more dynamic strategies. The results demonstrate that proper sequencing significantly reduces collision rates and enhances dronepad utilization. As a contribution, we underline the importance of giving complete control to DREMS during landings and takeoffs and prioritizing landings in dynamic allocations. These insights increase operational efficiency and safety under high request rates and pave the way for more robust and efficient drone delivery systems, highlighting the potential of intelligent sequencing and adaptive control mechanisms.

Zenodo (CERN European Organization for Nuclear Research)
Universidade Estadual de Campinas (UNICAMP) (BR), Universidade Federal do ABC (BR)
Fundação de Amparo à Pesquisa do Estado de São Paulo
Sustainable cities and communities
Openalex Percentile: Top 42%
UAV Applications and Optimization
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