UAVs are increasingly being employed to carry out surveillance, parcel delivery, communication-support and other specific tasks. In certain cases, the same geographical area may be in need of multiple services, which can be carried out by the same drones. In this paper, we propose and investigate a joint planning of multitask missions leveraging a fleet of UAVs equipped with a standard set of accessories enabling heterogeneous tasks. To this end, an optimization problem is formulated yielding the optimal joint planning and deriving the resulting quality of the delivered tasks. In addition, a heuristic solution is developed for large-scale environments to cope with the increased complexity of the optimization framework. The developed joint planning of multitask missions is applied to a specific post-disaster recovery scenario of a flooding in the San Francisco area. The results show the effectiveness of the proposed solutions and the potential savings in the number of UAVs needed to carry out all the tasks with the required level of quality.
Multiservice UAVs for Emergency Tasks in Post-Disaster Scenarios / Malandrino, Francesco; Rottondi, CRISTINA EMMA MARGHERITA; Chiasserini, Carla Fabiana; Bianco, Andrea; Stavrakakis, Ioannis. - STAMPA. - (2019). (Intervento presentato al convegno ACM MobiHoc 2019 Workshop FIrst REsponders network in emergency scenarios (IFIRE@MobiHoc19) tenutosi a Catania (Italia) nel July 2019) [10.1145/3331053.3335032].
Multiservice UAVs for Emergency Tasks in Post-Disaster Scenarios
Cristina Rottondi;Carla Fabiana Chiasserini;Andrea Bianco;
2019
Abstract
UAVs are increasingly being employed to carry out surveillance, parcel delivery, communication-support and other specific tasks. In certain cases, the same geographical area may be in need of multiple services, which can be carried out by the same drones. In this paper, we propose and investigate a joint planning of multitask missions leveraging a fleet of UAVs equipped with a standard set of accessories enabling heterogeneous tasks. To this end, an optimization problem is formulated yielding the optimal joint planning and deriving the resulting quality of the delivered tasks. In addition, a heuristic solution is developed for large-scale environments to cope with the increased complexity of the optimization framework. The developed joint planning of multitask missions is applied to a specific post-disaster recovery scenario of a flooding in the San Francisco area. The results show the effectiveness of the proposed solutions and the potential savings in the number of UAVs needed to carry out all the tasks with the required level of quality.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2733058
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