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Deep Reinforcement Learning for Persistent Cruise Control in UAV-aided Data Collection

2021-10-04Conference object Open access
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dc.contributor.authorKurunathan, John Harrison
dc.contributor.authorLi, Kai
dc.contributor.authorNi, Wei
dc.contributor.authorTovar, Eduardo
dc.contributor.authorDressler, Falko
dc.date.accessioned2021-11-02T10:42:58Z
dc.date.available2021-11-02T10:42:58Z
dc.date.issued2021-10-04
dc.description.abstractAutonomous UAV cruising is gaining attention dueto its flexible deployment in remote sensing, surveillance, andreconnaissance. A critical challenge in data collection with theautonomous UAV is the buffer overflows at the ground sensorsand packet loss due to lossy airborne channels. Trajectoryplanning of the UAV is vital to alleviate buffer overflows as wellas channel fading. In this work, we propose a Deep DeterministicPolicy Gradient based Cruise Control (DDPG-CC) to reducethe overall packet loss through online training of headings andcruise velocity of the UAV, as well as the selection of the groundsensors for data collection. Preliminary performance evaluationdemonstrates that DDPG-CC reduces the packet loss rate byunder 5% when sufficient training is provided to the UAV.pt_PT
dc.description.sponsorshipThis work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDP/UIDB/04234/2020); also by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by national funds through the FCT, within project ARNET (POCI01-0145-FEDER-029074).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/LCN52139.2021.9525022pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/18804
dc.language.isoengpt_PT
dc.relationUIDP/UIDB/04234/2020pt_PT
dc.relationPOCI01-0145-FEDER-029074pt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9525022pt_PT
dc.subjectUAV-aided WSNpt_PT
dc.subjectAutonomous UAVpt_PT
dc.subjectCruise controlpt_PT
dc.subjectDeep reinforcement learningpt_PT
dc.titleDeep Reinforcement Learning for Persistent Cruise Control in UAV-aided Data Collectionpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceEdmonton, AB, Canadapt_PT
oaire.citation.title2021 IEEE 46th Conference on Local Computer Networks (LCN)pt_PT
person.familyNameKurunathan
person.familyNameLi
person.familyNameTovar
person.givenNameJohn Harrison
person.givenNameKai
person.givenNameEduardo
person.identifier1490257
person.identifier.ciencia-id4E1B-CFFC-07A8
person.identifier.ciencia-idEE10-B822-16ED
person.identifier.ciencia-id6017-8881-11E8
person.identifier.orcid0000-0002-1270-1213
person.identifier.orcid0000-0002-0517-2392
person.identifier.orcid0000-0001-8979-3876
person.identifier.scopus-author-id7006312557
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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relation.isAuthorOfPublication21f3fb85-19c2-4c89-afcd-3acb27cedc5e
relation.isAuthorOfPublication80b63d8a-2e6d-484e-af3c-55849d0cb65e
relation.isAuthorOfPublication.latestForDiscoveryf16021ed-349d-4a21-be66-1ee3ef42b8c4

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