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dc.contributor.authorSandino, Juan
dc.contributor.authorVanegas, Fernando
dc.contributor.authorMaire, Frederic
dc.contributor.authorCaccetta, Peter
dc.contributor.authorSanderson, Conrad
dc.contributor.authorGonzalez, Felipe
dc.date.accessioned2020-10-19T23:33:43Z
dc.date.available2020-10-19T23:33:43Z
dc.date.issued2020
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs12203386
dc.identifier.urihttp://hdl.handle.net/10072/398484
dc.description.abstractResponse efforts in emergency applications such as border protection, humanitarian relief and disaster monitoring have improved with the use of Unmanned Aerial Vehicles (UAVs), which provide a flexibly deployed eye in the sky. These efforts have been further improved with advances in autonomous behaviours such as obstacle avoidance, take-off, landing, hovering and waypoint flight modes. However, most UAVs lack autonomous decision making for navigating in complex environments. This limitation creates a reliance on ground control stations to UAVs and, therefore, on their communication systems. The challenge is even more complex in indoor flight operations, where the strength of the Global Navigation Satellite System (GNSS) signals is absent or weak and compromises aircraft behaviour. This paper proposes a UAV framework for autonomous navigation to address uncertainty and partial observability from imperfect sensor readings in cluttered indoor scenarios. The framework design allocates the computing processes onboard the flight controller and companion computer of the UAV, allowing it to explore dangerous indoor areas without the supervision and physical presence of the human operator. The system is illustrated under a Search and Rescue (SAR) scenario to detect and locate victims inside a simulated office building. The navigation problem is modelled as a Partially Observable Markov Decision Process (POMDP) and solved in real time through the Augmented Belief Trees (ABT) algorithm. Data is collected using Hardware in the Loop (HIL) simulations and real flight tests. Experimental results show the robustness of the proposed framework to detect victims at various levels of location uncertainty. The proposed system ensures personal safety by letting the UAV to explore dangerous environments without the intervention of the human operator.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherMDPI AG
dc.relation.ispartofpagefrom3386
dc.relation.ispartofissue20
dc.relation.ispartofjournalRemote Sensing
dc.relation.ispartofvolume12
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchImage Processing
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchMobile Technologies
dc.subject.fieldofresearchAircraft Performance and Flight Control Systems
dc.subject.fieldofresearchSignal Processing
dc.subject.fieldofresearchAutonomous Vehicles
dc.subject.fieldofresearchApplied Statistics
dc.subject.fieldofresearchClassical Physics
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchcode080104
dc.subject.fieldofresearchcode080106
dc.subject.fieldofresearchcode080109
dc.subject.fieldofresearchcode080502
dc.subject.fieldofresearchcode090104
dc.subject.fieldofresearchcode090609
dc.subject.fieldofresearchcode091303
dc.subject.fieldofresearchcode010401
dc.subject.fieldofresearchcode0203
dc.subject.fieldofresearchcode0406
dc.subject.fieldofresearchcode0909
dc.titleUAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSandino, J; Vanegas, F; Maire, F; Caccetta, P; Sanderson, C; Gonzalez, F, UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments, Remote Sensing, 12 (20), pp. 3386
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-10-19T07:02:20Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorSanderson, Conrad


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