Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91622
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dc.contributorDepartment of Aeronautical and Aviation Engineering-
dc.creatorHuang, H-
dc.creatorSavkin, AV-
dc.date.accessioned2021-11-23T06:06:12Z-
dc.date.available2021-11-23T06:06:12Z-
dc.identifier.urihttp://hdl.handle.net/10397/91622-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Huang, H.; Savkin, A.V., Optimal Deployment of Charging Stations for Aerial Surveillance by UAVs with the Assistance of Public Transportation Vehicles. Sensors 2021, 21, 5320 is available at https://doi.org/10.3390/s21165320en_US
dc.subjectAdvances in robotic applicationsen_US
dc.subjectCharging stationsen_US
dc.subjectDronesen_US
dc.subjectPublic transportation vehiclesen_US
dc.subjectRobot sensingen_US
dc.subjectSurveillance and monitoringen_US
dc.subjectUnmanned aerial vehicle (UAV)en_US
dc.subjectVision-based sensingen_US
dc.titleOptimal deployment of charging stations for aerial surveillance by UAVs with the assistance of public transportation vehiclesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume21-
dc.identifier.issue16-
dc.identifier.doi10.3390/s21165320-
dcterms.abstractTo overcome the limitation in flight time and enable unmanned aerial vehicles (UAVs) to survey remote sites of interest, this paper investigates an approach involving the collaboration with public transportation vehicles (PTVs) and the deployment of charging stations. In particular, the focus of this paper is on the deployment of charging stations. In this approach, a UAV first travels with some PTVs, and then flies through some charging stations to reach remote sites. While the travel time with PTVs can be estimated by the Monte Carlo method to accommodate various uncertainties, we propose a new coverage model to compute the travel time taken for UAVs to reach the sites. With this model, we formulate the optimal deployment problem with the goal of minimising the average travel time of UAVs from the depot to the sites, which can be regarded as a reflection of the quality of surveillance (QoS) (the shorter the better). We then propose an iterative algorithm to place the charging stations. We show that this algorithm ensures that any movement of a charging station leads to a decrease in the average travel time of UAVs. To demonstrate the effectiveness of the proposed method, we make a comparison with a baseline method. The results show that the proposed model can more accurately estimate the travel time than the most commonly used model, and the proposed algorithm can relocate the charging stations to achieve a lower flight distance than the baseline method.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSensors, 2021, v. 21, no. 16, 5320-
dcterms.isPartOfSensors-
dcterms.issued2021-
dc.identifier.scopus2-s2.0-85111924092-
dc.identifier.pmid34450760-
dc.identifier.eissn1424-8220-
dc.identifier.artn5320-
dc.description.validate202111 bchy-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Othersen_US
dc.description.pubStatusPublisheden_US
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