In EDS ansehen

Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration

Gespeichert in:
Bibliographische Detailangaben
Titel: Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration
Autoren: Haojie Zhu, Mou Chen, Zengliang Han, Mihai Lungu
Quelle: Aerospace, Vol 10, Iss 3, p 309 (2023)
Verlagsinformationen: MDPI AG
Publikationsjahr: 2023
Bestand: Directory of Open Access Journals: DOAJ Articles
Schlagwörter: UAH, IPSO, IRL, fire-control command, swarm intelligence, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Beschreibung: This paper concerns the fire-control command calculation (FCCC) of an unmanned autonomous helicopter (UAH). It determines the final effect of the UAH attack. Although many different FCCC methods have been proposed for finding optimal or near-optimal fire-control execution processes, most are either slow in calculational speed or low in attack precision. This paper proposes a novel inverse reinforcement learning (IRL) FCCC method to calculate the fire-control commands in real time without losing precision by considering wind disturbance. First, the adaptive step velocity-verlet iterative algorithm-based ballistic determination method is proposed for calculation of the impact point of the unguided projectile under wind disturbance. In addition, a swarm intelligence demonstration (SID) model is proposed to demonstrate teaching; this model is based on an improved particle swarm optimization (IPSO) algorithm. Benefiting from the global optimization capability of the IPSO algorithm, the SID model often leads to an exact solution. Furthermore, a reward function neural network (RFNN) is trained according to the SID model, and a reinforcement learning (RL) model using RFNN is used to generate the fire-control commands in real time. Finally, the simulation results verify the feasibility and effectiveness of the proposed FCCC method.
Publikationsart: article in journal/newspaper
Sprache: English
Relation: https://www.mdpi.com/2226-4310/10/3/309; https://doaj.org/toc/2226-4310; https://doaj.org/article/684280d4bdc74b3a99768903f3fce1ea
DOI: 10.3390/aerospace10030309
Verfügbarkeit: https://doi.org/10.3390/aerospace10030309
https://doaj.org/article/684280d4bdc74b3a99768903f3fce1ea
Dokumentencode: edsbas.6230CB08
Datenbank: BASE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://doi.org/10.3390/aerospace10030309#
    Name: EDS - BASE (s4221598)
    Category: fullText
    Text: View record from BASE
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Zhu%20H
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edsbas
DbLabel: BASE
An: edsbas.6230CB08
RelevancyScore: 944
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 943.653564453125
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Haojie+Zhu%22">Haojie Zhu</searchLink><br /><searchLink fieldCode="AR" term="%22Mou+Chen%22">Mou Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Zengliang+Han%22">Zengliang Han</searchLink><br /><searchLink fieldCode="AR" term="%22Mihai+Lungu%22">Mihai Lungu</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Aerospace, Vol 10, Iss 3, p 309 (2023)
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: MDPI AG
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2023
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: Directory of Open Access Journals: DOAJ Articles
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22UAH%22">UAH</searchLink><br /><searchLink fieldCode="DE" term="%22IPSO%22">IPSO</searchLink><br /><searchLink fieldCode="DE" term="%22IRL%22">IRL</searchLink><br /><searchLink fieldCode="DE" term="%22fire-control+command%22">fire-control command</searchLink><br /><searchLink fieldCode="DE" term="%22swarm+intelligence%22">swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Motor+vehicles%2E+Aeronautics%2E+Astronautics%22">Motor vehicles. Aeronautics. Astronautics</searchLink><br /><searchLink fieldCode="DE" term="%22TL1-4050%22">TL1-4050</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: This paper concerns the fire-control command calculation (FCCC) of an unmanned autonomous helicopter (UAH). It determines the final effect of the UAH attack. Although many different FCCC methods have been proposed for finding optimal or near-optimal fire-control execution processes, most are either slow in calculational speed or low in attack precision. This paper proposes a novel inverse reinforcement learning (IRL) FCCC method to calculate the fire-control commands in real time without losing precision by considering wind disturbance. First, the adaptive step velocity-verlet iterative algorithm-based ballistic determination method is proposed for calculation of the impact point of the unguided projectile under wind disturbance. In addition, a swarm intelligence demonstration (SID) model is proposed to demonstrate teaching; this model is based on an improved particle swarm optimization (IPSO) algorithm. Benefiting from the global optimization capability of the IPSO algorithm, the SID model often leads to an exact solution. Furthermore, a reward function neural network (RFNN) is trained according to the SID model, and a reinforcement learning (RL) model using RFNN is used to generate the fire-control commands in real time. Finally, the simulation results verify the feasibility and effectiveness of the proposed FCCC method.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article in journal/newspaper
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: https://www.mdpi.com/2226-4310/10/3/309; https://doaj.org/toc/2226-4310; https://doaj.org/article/684280d4bdc74b3a99768903f3fce1ea
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.3390/aerospace10030309
– Name: URL
  Label: Availability
  Group: URL
  Data: https://doi.org/10.3390/aerospace10030309<br />https://doaj.org/article/684280d4bdc74b3a99768903f3fce1ea
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsbas.6230CB08
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.6230CB08
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/aerospace10030309
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: UAH
        Type: general
      – SubjectFull: IPSO
        Type: general
      – SubjectFull: IRL
        Type: general
      – SubjectFull: fire-control command
        Type: general
      – SubjectFull: swarm intelligence
        Type: general
      – SubjectFull: Motor vehicles. Aeronautics. Astronautics
        Type: general
      – SubjectFull: TL1-4050
        Type: general
    Titles:
      – TitleFull: Inverse Reinforcement Learning-Based Fire-Control Command Calculation of an Unmanned Autonomous Helicopter Using Swarm Intelligence Demonstration
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Haojie Zhu
      – PersonEntity:
          Name:
            NameFull: Mou Chen
      – PersonEntity:
          Name:
            NameFull: Zengliang Han
      – PersonEntity:
          Name:
            NameFull: Mihai Lungu
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-locals
              Value: edsbas
            – Type: issn-locals
              Value: edsbas.oa
          Titles:
            – TitleFull: Aerospace, Vol 10, Iss 3, p 309 (2023
              Type: main
ResultId 1