Multi UAV Cooperative Reconnaissance Based on Dynamic Programming IDQN Algorithm

Jingyi Huang, Bo Li, Chao Song, Neretin Evgeny

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper, multiple UAV cooperative reconnaissance tasks in complex environments are realized based on deep reinforcement learning. With the goal of quickly completing reconnaissance task allocation, safely avoiding obstacles and finally completing flight tasks, task allocation and trajectory planning are tightly coupled, and a multiple UAV cooperative reconnaissance algorithm based on dynamic programming multi-agent independent deep Q networks (IDQN) is proposed, Implemented collaborative reconnaissance task allocation and trajectory planning flight decision-making for multiple unmanned aerial vehicles in complex environments, and completed collaborative reconnaissance tasks for multiple unmanned aerial vehicles.

Original languageEnglish
Title of host publicationICCSI 2023 - 2023 International Conference on Cyber-Physical Social Intelligence
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages227-232
Number of pages6
ISBN (Electronic)9798350312492
DOIs
StatePublished - 2023
Event2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023 - Xi'an, China
Duration: 20 Oct 202323 Oct 2023

Publication series

NameICCSI 2023 - 2023 International Conference on Cyber-Physical Social Intelligence

Conference

Conference2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023
Country/TerritoryChina
CityXi'an
Period20/10/2323/10/23

Keywords

  • dynamic programming
  • IDQN algorithm
  • reinforcement learning
  • UAV collaborative reconnaissance

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