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dc.contributor.authorSandino, Juan
dc.contributor.authorMaire, Frederic
dc.contributor.authorCaccetta, Peter
dc.contributor.authorSanderson, Conrad
dc.contributor.authorGonzalez, Felipe
dc.date.accessioned2021-11-16T04:28:04Z
dc.date.available2021-11-16T04:28:04Z
dc.date.issued2021
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs13214481
dc.identifier.urihttp://hdl.handle.net/10072/410133
dc.description.abstractRecent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner; (2) offboard mode, which runs the POMDP-based planner across the flying area; and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR.
dc.description.peerreviewedYes
dc.languageen
dc.publisherMDPI AG
dc.relation.ispartofpagefrom4481
dc.relation.ispartofissue21
dc.relation.ispartofjournalRemote Sensing
dc.relation.ispartofvolume13
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchMachine learning
dc.subject.fieldofresearchPhotogrammetry and remote sensing
dc.subject.fieldofresearchField robotics
dc.subject.fieldofresearchIntelligent robotics
dc.subject.fieldofresearchImage processing
dc.subject.fieldofresearchSignal processing
dc.subject.fieldofresearchAutonomous vehicle systems
dc.subject.fieldofresearchDisaster and emergency management
dc.subject.fieldofresearchComputer vision
dc.subject.fieldofresearchAircraft performance and flight control systems
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode4611
dc.subject.fieldofresearchcode401304
dc.subject.fieldofresearchcode400706
dc.subject.fieldofresearchcode460205
dc.subject.fieldofresearchcode460306
dc.subject.fieldofresearchcode400607
dc.subject.fieldofresearchcode400703
dc.subject.fieldofresearchcode350703
dc.subject.fieldofresearchcode460304
dc.subject.fieldofresearchcode400103
dc.titleDrone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSandino, J; Maire, F; Caccetta, P; Sanderson, C; Gonzalez, F, Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty, Remote Sensing, 13 (21), pp. 4481
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-11-16T04:14:13Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2021 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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