UAV Trajectory Optimization in a Post-Disaster Area Using Dual Energy-Aware Bandits †

Amr Amrallah, Ehab Mahmoud Mohamed, Gia Khanh Tran, Kei Sakaguchi

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Over the past few years, with the rapid increase in the number of natural disasters, the need to provide smart emergency wireless communication services has become crucial. Unmanned aerial Vehicles (UAVs) have gained much attention as promising candidates due to their unprecedented capabilities and broad flexibility. In this paper, we investigate a UAV-based emergency wireless communication network for a post-disaster area. Our optimization problem aims to optimize the UAV’s flight trajectory to maximize the number of visited ground users during the flight period. Then, a dual cost-aware multi-armed bandit algorithm is adopted to tackle this problem under the limited available energy for both the UAV and ground users. Simulation results show that the proposed algorithm could solve the optimization problem and maximize the achievable throughput under these energy constraints.

Original languageEnglish
Article number1402
JournalSensors
Volume23
Issue number3
DOIs
StatePublished - Feb 2023

Keywords

  • cost subsidy
  • multi-armed bandit
  • post-disaster
  • reinforcement learning
  • trajectory optimization
  • unmanned aerial vehicle

Fingerprint

Dive into the research topics of 'UAV Trajectory Optimization in a Post-Disaster Area Using Dual Energy-Aware Bandits †'. Together they form a unique fingerprint.

Cite this