Yazar
Yanmaz, Evsen, Guven, Islam
Basım Tarihi
2024-07-01
Basım Yeri
-
Elsevier
Konu
Evolutionary algorithms, Multi-objective optimization, Maintaining connectivity, Multi-uav path planning, Unmanned aerial vehicles, Drone networks
Tür
Süreli Yayın
Dil
İngilizce
Dijital
Evet
Yazma
Hayır
Kütüphane
Özyeğin Üniversitesi
Demirbaş Numarası
1570-8705
Kayıt Numarası
f24db06f-2e9a-4627-96ba-fa43aac40b9a
Lokasyon
Electrical & Electronics Engineering
Tarih
2024-07-01
Notlar
TÜBİTAK
Örnek Metin
In this paper, we assume that a team of drones equipped with sensing and networking capabilities explore an unknown area via onboard sensors for surveillance, monitoring, target search or data collection purposes and deliver the sensed data to a ground control station (GCS) over multi -hop links. We propose a multidrone path planner that jointly optimizes area coverage time and connectivity among the drones. We propose a novel connectivity metric that includes not only percentage connectivity of the drones to GCS, but also the maximum duration of consecutive time that the drones are disconnected from the GCS. To solve this optimization formulation, we propose a multi -objective evolutionary algorithm with novel operations. We use our solver to test single, two and many objective path planning problems and compare our Pareto-optimal solutions to benchmark weighted -sum based solutions. We show that as opposed to the single solution that weighted -sum methods provide based on prior information from the user, the proposed evolutionary multiobjective optimizers can provide a diverse set of solutions that cover a range of mission time and connectivity performance illustrating the trade-off between these conflicting objectives. The end -user can then choose the best path solution based on the mission priorities during operation.
DOI
10.1016/j.adhoc.2024.103520
Cilt
160
