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Social Drone Sharing to Increase UAV Patrolling Autonomy in Pre- and Post-Emergency Scenarios.
Bisio, Isabella-Sole; Morando, Luca; Recchiuto, Carmine Tommaso; Sgorbissa, Antonio.
Afiliación
  • Bisio IS; DIBRIS Department, University of Genova, Genova, Italy.
  • Morando L; DIBRIS Department, University of Genova, Genova, Italy.
  • Recchiuto CT; DIBRIS Department, University of Genova, Genova, Italy.
  • Sgorbissa A; DIBRIS Department, University of Genova, Genova, Italy.
Front Robot AI ; 9: 820239, 2022.
Article en En | MEDLINE | ID: mdl-35445081
Multirotor drones are becoming increasingly popular in a number of application fields, with a unique appeal to the scientific community and the general public. Applications include security, monitoring and surveillance, environmental mapping, and emergency scenario management: in all these areas, two of the main issues to address are the availability of appropriate software architectures to coordinate teams of drones and solutions to cope with the short-term battery life. This article proposes the novel concepts of Social Drone Sharing (SDS) and Social Charging Station (SCS), which provide the basis to address these problems. Specifically, the article focuses on teams of drones in pre- and post-event monitoring and assessment. Using multirotor drones in these situations can be difficult due to the limited flight autonomy when multiple targets need to be inspected. The idea behind the SDS concept is that citizens can volunteer to recharge a drone or replace its batteries if it lands on their property. The computation of paths to inspect multiple targets will then take into account the availability of SCSs to find solutions compatible with the required inspection and flight times. The main contribution of this article is the development of a cloud-based software architecture for SDS mission management, which includes a multi-drone path-optimization algorithm taking the SDS and SCS concepts into account. Experiments in simulation and a lab environment are discussed, paving the path to a larger trial in a real scenario.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Robot AI Año: 2022 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Robot AI Año: 2022 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Suiza