An Efficient Connected Swarm Deployment via Deep Learning

• Autorzy: Danilchenko Kiril , Segal Michael

Identyfikatory

DOI

10.15439/2021F001

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

In this paper, an unmanned aerial vehicles (UAVs) deployment framework based on machine learning is studied. It aims to maximize the sum of the weights of the ground users covered by UAVs while UAVs forming a connected communication graph. We focus on the case where the number of UAVs is not necessarily enough to cover all ground users. We develop an UAV Deployment Deep Neural network (mod) as a UAV's deployment deep network method. Simulation results demonstrate that mod can serve as a computationally inexpensive replacement for traditionally expensive optimization algorithms in real-time tasks and outperform the state-of-the-art traditional algorithms.

Słowa kluczowe

EN

autonomous aerial vehicles; deep learning; graph theory; multi-robot systems; optimisation

PL

autonomiczne pojazdy powietrzne; głębokie uczenie; teoria grafów; systemy wielorobotowe; optymalizacja

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 1--7
• Opis fizyczny: Bibliogr. 23 poz., il.,wykr., tab.

Twórcy

autor

Danilchenko Kiril

• School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva, Israel

autor

Segal Michael

• School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva, Israel

Bibliografia

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Uwagi

1. Track: Preface

2. Session: Invited Contributions