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Deep Reinforcement Learning for Real-Time Trajectory Planning in UAV Network

2020Conference object Restricted access
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dc.contributor.authorLi, Kai
dc.contributor.authorNi, Wei
dc.contributor.authorTovar, Eduardo
dc.contributor.authorGuizani, Mohsen
dc.date.accessioned2020-10-30T10:47:07Z
dc.date.embargo2120
dc.date.issued2020
dc.description.abstractIn Unmanned Aerial Vehicle (UAV)-enabled wireless powered sensor networks, a UAV can be employed to charge the ground sensors remotely via Wireless Power Transfer (WPT) and collect the sensory data. This paper focuses on trajectory planning of the UAV for aerial data collection and WPT to minimize buffer overflow at the ground sensors and unsuccessful transmission due to lossy airborne channels. Consider network states of battery levels and buffer lengths of the ground sensors, channel conditions, and location of the UAV. A flight trajectory planning optimization is formulated as a Partial Observable Markov Decision Process (POMDP), where the UAV has partial observation of the network states. In practice, the UAV-enabled sensor network contains a large number of network states and actions in POMDP while the up-to-date knowledge of the network states is not available at the UAV. To address these issues, we propose an onboard deep reinforcement learning algorithm to optimize the realtime trajectory planning of the UAV given outdated knowledge on the network states.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/IWCMC48107.2020.9148316pt_PT
dc.identifier.issn2376-6506
dc.identifier.urihttp://hdl.handle.net/10400.22/16381
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationARNET, ref. POCI-01-0145-FEDER-029074pt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9148316pt_PT
dc.subjectWireless sensor networkspt_PT
dc.subjectUnmanned aerial vehiclespt_PT
dc.subjectTrajectory planningpt_PT
dc.subjectWireless power transferpt_PT
dc.subjectDeep reinforcement learningpt_PT
dc.titleDeep Reinforcement Learning for Real-Time Trajectory Planning in UAV Networkpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceOnlinept_PT
oaire.citation.endPage963pt_PT
oaire.citation.startPage958pt_PT
oaire.citation.titleProceedings of the 16th International Conference on Wireless Communications & Mobile Computing (IWCMC 2020)pt_PT
person.familyNameLi
person.familyNameTovar
person.givenNameKai
person.givenNameEduardo
person.identifier.ciencia-idEE10-B822-16ED
person.identifier.ciencia-id6017-8881-11E8
person.identifier.orcid0000-0002-0517-2392
person.identifier.orcid0000-0001-8979-3876
person.identifier.scopus-author-id7006312557
rcaap.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication21f3fb85-19c2-4c89-afcd-3acb27cedc5e
relation.isAuthorOfPublication80b63d8a-2e6d-484e-af3c-55849d0cb65e
relation.isAuthorOfPublication.latestForDiscovery80b63d8a-2e6d-484e-af3c-55849d0cb65e

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