Analysis of Autonomous Unmanned Aerial Systems Based on Operational Scenarios Using Value ...

17 Pages·2017·2.18 MB·English

drones
Article
Analysis of Autonomous Unmanned Aerial Systems
Based on Operational Scenarios Using
Value Modelling
AkashVidyadharan1,RobertPhilpottIII2,*,BenjaminJ.Kwasa2andChristinaL.Bloebaum2
1 InfraDroneLLC.,2411GrandAve,DesMoines,IA50312,USA;[email protected]
2 AerospaceEngineering,IowaStateUniversity,Ames,IA50011,USA;[email protected](B.J.K.);
[email protected](C.L.B.)
* Correspondence:[email protected]
Received:1November2017;Accepted:21November2017;Published:23November2017
Abstract: Inrecentyears,theuseofUAS(UnmannedAerialSystems)hasmovedbeyondtherealm
ofmilitaryoperationsandhasmadeitswayintothehandsofconsumersandcommercialindustries.
AlthoughtheapplicationsofUASincommercialindustriesarevirtuallyendless, therearemany
issuesregardingtheiroperationsthatneedtobeconsideredbeforethesevaluablepiecesofequipment
areallowedforwidespreadciviluse. Currently,UASoperationsinthepublicdomainareguided
andcontrolledbytheFAAPart107rulesafteroverwhelmingpublicpressurecausedbytheearlier
333exemption.Inordertoapproachsuchlargerissues,thispaperwillexploittheuseofvaluemodels,
whichwillhelptoquantifyhowthedifferentenvironmentalandoperationalscenariosplayarolein
UASoperationsbasedonthetaskbeingperformed. Theprimaryaimofthisresearchistousethe
attributesfromkeyfactorsoftheUASsuchastheautonomylevels(AL)andtechnologyreadiness
levels(TRL)alongwiththeiroperatingscenariofactors,suchastheenvironmentalcomplexityand
taskcomplexity,basedontheoperatingenvironmentinwhichaUASperformsitstask. Toanalyze
theperformanceofautonomousUASindifferentoperationalscenarios,thephysicalcharacteristics
andclassofaUASmaybelinkedtoitsALandTRL.Usingtheseparameters,therisksfacedbythe
UASinaparticularmissionarequantifiedandavalueisassignedtotheabstractentitiesinvolved.
AlthoughtherearemanycriticalquestionswithrespecttogoodpracticestobefollowedbyUAS
operators in order to obtain valuable data and information on the structures being scanned and
monitored,therearemanyotherchallengeswithregardstolargescaleoperationsofUASsuchasthe
ethical,legalandsocietalimplicationsthathavetobeaddressed.
Keywords: UAS;structuralhealthmonitoring;civilUASapplications;valuemodelling;operational
scenarioevaluation;value-basedsystemsengineering;valuedrivendesign
1. Introduction
TheintroductionofUASintotheNationalAirspaceSystem(NAS)comeswithalotofadvantages
and some disadvantages which translate to gains and losses for different stakeholders involved.
ThechallengesfacedbythegovernmentandmultiplestakeholderswhowishtouseUAStechnology
onalargescaleareissuestiedtosafety,policy,law,ethicsandprivacy. Thisresearchstudyinvolves
understandingthedifferentfactorsthataffectthedeploymentofUASonalargescalebytakinginto
accountoperationalconstraintsandissuesassociatedwiththem. Understandingthesefactorsfrom
theviewpointsoftheusersandoperatorsmaybeusedtoaidinamendinganddevelopingpolicies
forUASoperations,whicharedirectlyrelatedtotheongoingcontroversyregardingtheregulation
ofUAS.
Drones2017,1,5;doi:10.3390/drones1010005 www.mdpi.com/journal/drones
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WiththerecentincreaseinthenumberofUASintheskies,measureshavetobetakentoproperly
regulateandsafelycarryoutthetransitionintothisnewrealmofaerialvehicleoperation. Thisprocess
willrequirethecoordinationandinvolvementofacademia,industry,governmentagencieslikethe
FederalAviationAdministration(FAA)andDepartmentofDefense, localauthorities, andprivate
sectorsalikeinordertosuccessfullyimplementasafe,efficientandrobustsystemforcommercialUAS
operations[1–4]. Theultimategoalofsuchahighlevelpartnershipbetweentheseagencieswould
betosuccessfullyintegratesmallUAS(sUAS)operationsintotheNASinordertoenhancetheuse
ofUASforcommercialapplications,whileconsideringtheethical,legal,societalandenvironmental
implicationsofthesetechnologies. ThecurrentUASboomforcivilapplicationsmustbecautiously
carriedoutwithoutviolatinganyrightsorsafetyofindividualsortheenvironment.
TheprimarygoalofthisresearchstudyistocreateavaluemodelforUASbytakingintoaccount
thetechnical,environmentalandoperationalscenariosinvolvedinordertopossiblycreateadecision
analysissimulationtoanalyzeandratedifferentUASbasedontheircomponents,autonomyleveland
technologyreadinesslevel. Thisinturnmaybeusedinthefuturetounderstandthemisalignment
betweenstakeholders’preferencesandtheregulationsduetopolicyrequirementsimposedonUAS
operations. Theresearchwillinvolveusingacombinationofvarioustoolsandframeworkssuchas
multidisciplinarydesignoptimization(MDO),value-drivendesign(VDD),anddecisionanalysis(DA)
toanalyzeandunderstandthevariousfactorsinvolvedwhiledesigningandoperatingUASonalarge
scalefortheuseofstructuralhealthmonitoring(SHM)andcivilapplications.
ThemethodologyforthisresearchwillnecessitateidentifyingdifferentclassesandtypesofUAS
thatcanbeusedforcommercialpurposesandrelatingthephysicalcharacteristicsoftheseUASto
virtualcharacteristicssuchasALandTRLinordertoevaluatethesesystemsforthepurposeofSHM,
whilestayingcompliantwiththeFAAPart107regulations. Theseevaluationmethodologiesofthe
softwareandhardwareofUAScaninturnbeusedtoratetheirperformanceandguideoperational
activitiesbasedonthetasksbeingperformedaroundthedifferentoperationalscenarios.Thiswillallow
thevaluemodeltobeusedtocorrelateaUAStoitsoperationalenvironment. Thesecharacteristics
can be represented in the form of attributes and design variables to create a value model that can
captureandincorporatethetechnicaldesignaspectsoftheUAStothelegalaspectsofdeployingUAS.
InordertoratetheperformancecapabilityoftheUASinitsoperationalscenarioandtoabidebythe
legalconstraints,theautonomylevelandtechnologyreadinesslevelsoftheassociatedandrelevant
componentsplayakeyroleinthevaluemodel.
The Federal Aviation Administration is the operational branch of the Department of
Transportation (DOT) that is tasked with all aspects of aerospace governance and safety within
theUnitedStates[5]. Part107istheregulationovertheoperationofsmallunmannedaircraftsystems
(sUAS)[6]. ThenewPart107rulesputforthbytheFAAregardingtheoperationsofsUASwillbe
usedastheprimaryplatformtosetupthedifferentscenariostouseeitherasingleUAVoraswarmof
UAVstomonitordifferenttypesofstructuresautonomouslyinordertoprovideaholisticviewofthe
conditionofthestructurebeingsurveyed. Inordertoimplementthissysteminanefficientmanner,
theauthorsapplyVDDtosystemsengineering[7]. Thismethodologywillprovideaninterdisciplinary
approach to the implementation of the system while being able to communicate the design and
operationalrequirementswithstakeholders’preferences. Examplesofsuchstakeholdersincludethe
UASpilots,operatorsandtheFAA.Thiswillhelpidentifyrelationshipsbetweenthesubsystemsof
theoverallsystemwhileincludingeconomictheoriestobetteroptimizethedesignandoperationof
UASbasedonthemissionscenario,whileincorporatingthepreferencesofthestakeholders[7].
2. UnmannedAircraftSystemModel
The following UAS model will lay the groundwork for the formulation of a multi-objective
optimizationfunction. Atwotieredbreakdownofamulti-copterUASmodelhasbeendevelopedasa
partofthisresearchinordertoserveasatestbedfortheexecutionofdifferentoperationalscenarios
requiredtoperformdifferenttaskscriticaltoSHM.ThehierarchicalmodelshowninFigure1describes
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atwo-tierhierarchicaldecompositionforamulti-copterUAS.Thesimplifiedmodelofamulti-copter
describes a two-tier hierarchical decomposition for a multi-copter UAS. The simplified model of a
UAShdaesscfirivbeesp ar imtwaor-ytiesru hbiseyrasrtcehmicsali ndeitcsomfirpsotstiiteiorn: pforro pa umlsuilotin-c,oppotewr eUr,AGSN. TChe( GsirmopulnifdiedN mavoidgealt ioof na and
multi-copter UAS has five primary subsystems in its first tier: propulsion, power, GNC (Ground
Controml)u,lstit-rcuocpttuerre UsAanSd hpaas yfilvoea dp.riTmhaeryse scuobnsdysttieemros fisnu ibtss yfisrtsetm tiserc: opmroppruislesioonf,m pootwoersr,, bGaNttCer (yG,IrMouUnd,G PS
Navigation and Control), structures and payload. The second tier of subsystems comprise of motors,
Navigation and Control), structures and payload. The second tier of subsystems comprise of motors,
(unitedstatesimplementationofaGNSS),flightcomputer,3DFPVcamera,arms,bodyandlanding
battery, IMU, GPS (united states implementation of a GNSS), flight computer, 3D FPV camera, arms,
battery, IMU, GPS (united states implementation of a GNSS), flight computer, 3D FPV camera, arms,
gearb[8o]d.yS ainncde lathndisinisg ageraerl a[8ti]v. Seilnycela trhgise ims ao rdeelal,tievaeclyh loarfgteh meopdrieml, eaarcyhd oifs tchipe lpinriemsaarny dditshciepilrinseusb asnyds tems
body and landing gear [8]. Since this is a relatively large model, each of the primary disciplines and
nametlhye,irG sNubCs,yssttermucst unaremse,lpy,o GwNerC,,p srtorupcutulsreios,n paonwderp, apyrolopaudlsiwonil lanindt epraayclotawd iwthilel ainctheroatcht ewr.ithIn eoacrhd erto
their subsystems namely, GNC, structures, power, propulsion and payload will interact with each
captuortehethr.e Inc oourdpelrin tog scaopfttuhree stehei nctoeurpalcitnigosn osf, tahDesSeM intwerialcltbioenus,s ae dDSaMss whoilwl bne iunseFdi gaus rseho2w. n in Figure
other. In order to capture the couplings of these interactions, a DSM will be used as shown in Figure
2.
2.

FiguFrFiegig1uu.rerHe 1 i1.e .Hr Haiericrearharicrchcahilciacdale ldc deocemocompmoppsoiostisitioitonionno o fof afa a mm muuullttliti--ic-cocooppptettere rrU UUAAASS S( U((UnUnmnmamannnannedend eA dAereAiraieal rlS iSyaysltsSetymems)t.)e . m).

FigFuigreur2e. 2D. Deseisgignns sttrruuccttuurree mmaattrriixx oof faa mmulutilrtoirtoorto UrAUSA. S.
Figure 2. Design structure matrix of a multirotor UAS.
A Design Structure Matrix (DSM) is a representation of a system decomposition that illustrates
ADesAig DneSsitgrnu cSttururcetuMrea tMriaxtr(iDx S(DMS)Mis) ias rae rperperseesenntatattiioonn ooff aa ssyysstteemm ddeceocmompopsoitsioitni othnatth ilaltusiltlruatsetsr ates
the relationships of the identified entities (subsystems) that make up an entire system of interest
thereltahteio rneslahtiiponssohfipths eoifd tehne tiidfieendtiefinetdit ieensti(tsieusb s(syusbtesymstse)mths)a tthmata kmeaukpe uanp eann tiernetisrye sstyemsteomf ionf tienrteesrtesbta sed
based on the interface media of interest such as, material interfaces, energy interfaces, etc. These
onthebiansteedr foanc ethme eindtiearfoafcein mteerdeisat osuf cinhtearse,smt sautcehr iaasl,i nmtaetrefraicael si,netenrefarcgeys,i nenteerrfgayc einst,eertfca.ceTsh, eestce. iTnhteesrefa ces
interfaces are commonly represented as feed-forwards and feed-backs between subsystems to
interfaces are commonly represented as feed-forwards and feed-backs between subsystems to
are commonly represented as feed-forwards and feed-backs between subsystems to represent
connectivity [9–12]. The DSM shown has the capability to visualize the connections between the
subsystemsascouplings,feedforwardsandfeedbacks[13]. Thismakesiteasierfortheusertomodel
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Drones 2017, 1, 5 4 of 17
andarneparleyszeentt hceonsnyesctteimvit,ya n[9d–1s2h]o. wThset hDeSdMe psehnodwenn chiaess tahme ocnapgatbhileitysu tbos yvsistuemalisz.e Uthsien gcotnhniescDtioSnMs and
thehbieertwarecehni cthael bsurebasyksdteomwsn asm cooudpellisn,gws, efeceadn folirswtaorudts athned pfeeerdfboarcmksa n[1c3e]. aTthtrisib muateksest hita etaasireer afofrfe tchtee dby
user to model and analyze the system, and shows the dependencies among the subsystems. Using
thefirsttiersubsystemsasshowninFigure3. Asseenfromthefigurebelow,propulsionaffectsthe
this DSM and the hierarchical breakdown models, we can list out the performance attributes that are
altitude,enduranceandpayloadmassthatcanbecarriedbytheUAS;powerfromthebatteryaffects
affected by the first tier subsystems as shown in Figure 3. As seen from the figure below, propulsion
enduranceandrange; GNCaffectsrange,stabilityandautonomylevel,whilethestructureaffects
affects the altitude, endurance and payload mass that can be carried by the UAS; power from the
payloadvolume,payloadmass,proximityandmaneuverability. ThepayloadattachedtotheUAS
battery affects endurance and range; GNC affects range, stability and autonomy level, while the
canbsetrruecmtuorev eadffebcatss epdaoynloaitds mvoilsusmioen, ppraoyfilolaeda nmdastas,s kp,rothxeimreitbyy aanffde cmtianngeuthveercaabpilaitbyi. liTtyhea npdaytlaosakds that
canbaettpacehrfeodr mto etdheb UyAthSe cUanA bSe. Trehmeopvaeydl obaadsedat tornib iutst emsicssainonb eprcohfialne gaendd btaasske,d thoenretbhye atfyfpecetsinogf tsheen sing
equipcmapeanbtilitthya atnwd itlalsbkes tahtatat cchane dbet opethrfeorUmAedS bfoyr thSeH UMA.S. The payload attributes can be changed based
on the types of sensing equipment that will be attached to the UAS for SHM.

FiguFriegu3r.eM 3.u Mltui-lctio-cpotperteUr UAASSb brereaakkddoowwnn aanndd ccoonnnnecetcitoino ntot poeprfeorrfmoramncaen actetraibtutrtiebsu. tes.
In order to set up the optimization process, we needed to develop an objective function that
In order to set up the optimization process, we needed to develop an objective function that
captures the goals of the stakeholders and abide by the legal constraints set by the FAA. Equation (1)
capturesthegoalsofthestakeholdersandabidebythelegalconstraintssetbytheFAA.Equation(1)is
is the formulation for the minimization of mass approach. The mass is assumed to be a summation
theformulationfortheminimizationofmassapproach. Themassisassumedtobeasummationofall
of all of the subsystems that have a mass which could affect the system.
ofthesubsystemsthathaveamasswhichcouldaffectthesystem.
(cid:3015)
(cid:1865)(cid:1861)(cid:1866) (cid:1858)(cid:4666)(cid:1876)(cid:4667)=(cid:1839)(cid:1853)(cid:1871)(cid:1871) = (cid:3533)(cid:1839)(cid:1853)(cid:1871)(cid:1871) (1)
(cid:3047)(cid:3042)(cid:3047)(cid:3028)(cid:3039) N (cid:3002)(cid:3036)
min f(x) = Mass = (cid:3036)∑(cid:2880)(cid:2869) Mass (1)
total Ai
Side constraints are developed to fit within physicali =b1ounds, such as to keep the mass of the
UAS from dropping below zero or making it too heavy for takeoff. Inequality constraints, on the
Sideconstraintsaredevelopedtofitwithinphysicalbounds,suchastokeepthemassoftheUAS
other hand, are based directly on the FAA Part 107 rules for UAS operations, such as maintaining a
fromtodtraol pmpaisnsg obf ethloe wUAzeS rtoo obre mlesask tihnagn i5t5t olbos.h, emaavxyimfourmt aspkeeeodf fl.esIsn ethqauna 1li0t0y mcopnhs, tmraaixnimts,uomn fltihgehto ther
handa,latirtuedbea osef d40d0 ifrte. catblyovoen gtrhoeunFdA lAevPela arntd1 0fl7igrhutl emsisfsoironUsA mSuostp beer actairorniesd, souuct hwaitshimn aviinsutaail nliinneg oaf total
masssoigfhtth [e14U].A TShteoseb ceolnesstsratihnatsn u5s5edlb isn. ,tmhea txriamdiutimonsapl eMeDdOle sfosrtmhuanlat1io0n0 mrepprhe,semnat xthime udemsirfleisg ohft tahleti tude
of400staftk.eahbooldveer, gwrhoiucnh dinl tehvies lcaasned isfl thigeh gtomveirsnsmioennst,m anuds thibgehlciagrhrtise tdheo ruetgwionitsh iinn thveis dueasliglinn sepoacfes tihgahtt [14].
Thesearceo dnesetmraeidn tinsfueasseidblein tot hueset rwahdiiltei odnesailgMninDgO thfeo frimxeud lwatiinogn UrAepSr. esentthedesiresofthestakeholder,
The stakeholders in this setup will represent the government (FAA), UAS designer and the UAS
whichinthiscaseisthegovernment,andhighlightstheregionsinthedesignspacethataredeemed
operator. Based on their preferences, each stakeholder will have different values, so the value given
infeasibletousewhiledesigningthefixedwingUAS.
only makes sense to the stakeholder. The true preferences of the stakeholders interested in taking
The stakeholders in this setup will represent the government (FAA), UAS designer and the
advantage of UAS can be obtained by analyzing the viewpoints of stakeholders obtained from
UASoperator. Basedontheirpreferences,eachstakeholderwillhavedifferentvalues,sothevalue
multiple sources as demonstrated in Table 1. This table is a representation of the true preferences of
givenUoAnSl yopmeraaktoerss fsreonms editfofetrhenet sitnadkueshtroields;e trh.e Tdhatea tprureesepnrteedfe irne nthcee staobflet ihse exsttraakcetehdo flrdoemrs thine tNerTeIsAte d in
takincgoandvevnaendt amgeulotif-sUtaAkSehcoalndebre doibsctuaisnsieodnsb yduaen atloy zai npgrethsiedevnietiwal pmoienmtsoroafnsdtuakme hisosludeedr sboyb tfaoirnmeedr from
multiPprleesisdoeuntr cOebsaamsad foerm “oPnrosmtroatitnegd EicnonToambicle C1om. pTehtiitsivteanbeslse Wishiale rSeapfergeusaernditnagt iPornivoacfyt, hCeivtirl uReighptrse, faenrde nces
of UACiSvilo Lpieberrattieosr isn fDroommesdticif Ufesree onf tUinnmdaunsnterdi eAsi;rctrhafet Sdyasttaemps”re [s1e5n]. tTehdisi mnetehteintga cbolmepisriseexdt roafc mteedmfbreorms the
NTIAconvenedmulti-stakeholderdiscussionsduetoapresidentialmemorandumissuedbyformer
PresidentObamafor“PromotingEconomicCompetitivenessWhileSafeguardingPrivacy,CivilRights,and
CivilLibertiesinDomesticUseofUnmannedAircraftSystems”[15]. Thismeetingcomprisedofmembers
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andentitiesfromacademia,industry,governmentsectorsandcivilsociety,therebygivingaholistic
viewoftheircombinedopinionsforthedevelopmentofregulationwithregardstoUAS.
Drones 2017, 1, 5 5 of 17
Table1.PreferencesofUASoperatorsfromvariousindustries.
and entities from academia, industry, government sectors and civil society, thereby giving a holistic
view of their comFbairnmeder osp&inAiogns for the development of regulationU wniitvhe rrseigtayrds to UAMSe.d iaHouses&
Energy&Construction
Associations Researchers Journalists
Table 1. Preferences of UAS operators from various industries.
Precision Inspection& Research&
Operation Photojournalism
FAagrrmiceurlstu &re Ag MEaninetregnya n&c e UnEivdeurcsaittyio n Media Houses &

Associations PoCwoenrslitnruecintisopne ct Researchers Journalists
Photos—RGB,IR,
Precision Inspection & Research &
Operation NDVI Gasleaksdetection ToolforR&Din PhoPthoojotourannadlivsimde o
Task Agriculture Maintenance Education
Thermography multiplefields footagefornews
Power line inspect
Photos—RGB, IR,
Spraying Gas leaSkHs Mdetection Tool for R&D in Photo and video
Task NDVI
Environment RuralFarmland UrbTahner&mRougrraalpahrye as muClotinpfilen efidelzdosn es fooBtuagsye uforrb annewarse as
Spraying SHM
Ownershipof Currentrulesaretoo Noreqstodisclose
Environment Ruscraanl Fdaartma.land Urbaconn &st rRauinrainl gareas ConTfrienaetdin zsotintueste s Busy urdbeatna ialsr.eas
Issues differentfrom
Ownership of Current rules are too
Intrusionover Privacylawsneedtobe Tcroematm inersctiitaulteenst itiesNoT rimeqes ftoor dtriascinloinseg &
scan data. constraining
privateland. loosened registration
different from details.
Issues
InTtrrauisnioinng over Privacy laws need to commercial
Specialoperations
Requirements progprraivmasten elaendded. exbeem lopotisoennsefdo r reegnuUtiltsaieetisIo RnBsfor Timprreei Nvfgoaiorsc tyatrrdaradetiigniotuiinnol agnt ai&oln s
UASmusthave inspection& schoolsnotFAA fordroneuse
Training Special operations
IDfortracking. maintenance Use IRB No additional
programs needed exemptions for
Requirements regulations for privacy regulations
UAS must have inspection &
InTable1,theexamplesofagriculturalassociationsrepresscehnotionlsg nfoart mFAerAs ,energfyora nddrocnoen ussteru ction
ID for tracking. maintenance
companies, university researchers representing academia and media houses representing the
In Table 1, the examples of agricultural associations representing farmers, energy and
journalist’s point of view are used. These potential UAS operators have different operations and
construction companies, university researchers representing academia and media houses
tasksbasedontheirmissionscenario,whetheritisscanningafield,sprayingpesticidesorfertilizers
representing the journalist’s point of view are used. These potential UAS operators have different
onfarmland,inspectingtanksforgasleaksorforcapturingfootageofaneventtoshowontelevision.
operations and tasks based on their mission scenario, whether it is scanning a field, spraying
Duetothenatureofthedifferentoperations,therequirements,needsandissuesfacedbyeachofthe
pesticides or fertilizers on farmland, inspecting tanks for gas leaks or for capturing footage of an event
operatorsalsovaryfromcase-to-casebasedontheoperationalenvironmentandtaskstobeperformed.
to show on television. Due to the nature of the different operations, the requirements, needs and
UASisdsueessi gfnaecresd pblayy eaacchri toicf althreo leopinerathtoersc yacllseo ovfadreys ifgronmin gcathsee-tUo-AcaSsew bitahsesdu ffionc ietnhet AoLpearnatdioTnaRlL to
overesneveiraonndmpenots asnibdl ytarseksst rtoic btet hpeesrefoormpeerda. tUoArsSf droemsigvneiorsla ptlianyg at hcreitricualle rsoalen idn rtehge uclyactlieo onfs dpeusitgnfoinrtgh by
theFthAeA U.ATSh iwsirthel asutifofincisehnitp AbLe tawnde eTnRtLh teop orvimersaerey asntdak peohsosilbdleyr rseisntritcht itshceases eo,pnearmatoerlsy ,frtohme gvoiovleartinnmg ent,
opertahteo rrualensd adndes rieggnuelratiisonshs opwutn foinrthF ibgyu trhee4 F.ATAhe. Tmhiosd reellaftoiornusnhdipe rbsettawnedeinn tgheth perirmeqauryir setdakAehLoaldnedrsT RL
forainU tAhiSs bcaassee, dnaomnetlays, ktshea ngdovoeprnemraetniot,n oapleernavtoirr oannmd ednetssigwneilrl ibs eshdoiswcnu sisne Fdiginurteh 4e. fTohlleo wmiondgelc fhoarp ter
understanding the required AL and TRL for a UAS based on tasks and operational environments will
usingtheexampleofSHM.
be discussed in the following chapter using the example of SHM.

Figure4.PrimarystakeholdersinUASoperations.
Figure 4. Primary stakeholders in UAS operations.
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Drones 2017, 1, 5 6 of 17
33..V VaalulueeM MooddeellF Foormrmuulalatitoionn
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laseurnvcehs paas da floarunthceh cpraeadt ifoonr tohfer icsrkeaatniodnb oafr griasikn amnodd bealsr.gain models.
TThheep prrimimaarryy ppaarraammeetteerrss iinnvvoollvveedd iinn tthhee ooppeerraattiioonnss iinnvvoollvviinngg aa UUAASS hheerreeiinn aarree iiddeennttiififieedd aass::
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smceinsasiroion isscdenefiarnieod isb ydtehfienmede tbeyo rtohleo gmiceatlecoornoldoigtiiocnals caonnddtihtieonstsr uacntdu rtehtey pster;utchteurme istysipoen; othbeje cmtiivsseioisn
doebfijnecetdivbey tihs eddeaftianaecdq ubiysi titohne mdeathtao daucqseudisaitniodnm misseitohnotda skusseadn datnhde UmAiSsscihoanr atcatesrkiss tiacnsdar ethdeiv iUdeAdS
bcyhtahraecirtesriizset,iccsl aassre, AdLivainddedT RbLy. Tthheeirs tsriuzcet,u crelatsysp, eAaLn danthde TdRatLa. aTcqhue issittriuonctumreet htyopdew ailnldh atvhee adsaetta
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aignr ioctuhletur raepapnlidcapthioontos jsouucrhn aalsi spmre,cbiusitoinn atghrisicsutultduyre, wanedw pilhloutsoejotuherneaxlaismmp, lbeuotf iSnH thMis tsotuudnyd,e wrseta wnidllt uhsee
etfhfeec etsxoamftphleeo opfe SrHatMion taol uscnednearrsitoanond tthhee UefAfeSc.tsT hoifs thexea omppelreatwioinllabl escuesneadritoo odnem thoen UstrAaSte. Tcohmis pelxeaxmitipesle
inwviolll vbeed uinsetdh etop rdoecmesosnosftUraAteS cboamsepdleSxHitMiesa nindvholovwedim inp rtohvee dprpoecrefsosr mofa nUcAemS abyasbeeda SchHieMv eadn.d how
improved performance may be achieved.

FFigiguurree5 5..P Paarraammeeteterrssf oforrt htheeo oppeerraatitoionnaalls scceennaarrioioa annddU UAASSr aratitningg..
33.1.1..V VaalulueeM Mooddeellf oforrO OppeerraatitoionnaallS Scceennaarrioio
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sscceennaarrioio:: iinnffrraassttrruuccttuurree ttyyppee aanndd ddaattaaa accqquuisisitiitoionn tytyppee.. TThheessee mmeettrriiccss aarree ssuuppppoorrtteedd bbyyw weeigighhtetedd
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Drones2017,1,5 7of17
Drones 2017, 1, 5 7 of 17

FFiigguurree 66.. MMooddeell rreepprreesseennttiinngg ccoommppoossiittiioonn ooff eennvviirroonnmmeenntt aanndd ttaasskk ccoommpplleexxiittiieess..
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iinnffrraassttrruuccttuurree ttyyppee wwhheerree tthhee UUAASS wwiillll bbee ppeerrffoorrmmiinngg aanndd wwiillll bbee eevvaalluuaatteedd bbaasseedd oonn tteemmppeerraattuurree,,
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ttohet haeuatountoonmoym oyf othfeth UeAUSA. S .
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tinhferyasitnrculcutdueres aiznedo tfhtehye instcrluucdteu sriez,ep ohfy tshiec asltroubcsttuarcel,e pshayrosiucnald otbhsetasctlreusc aturorue,nwd ethaeth setrruwcthuerne,t wheeaUthAeSr
owpheernat tiohne sUwAilSl obpeecroantdiouncst ewdi,lgl eboeg croanpdhuycotefdth, egesuogrrroaupnhdyi nogf tlhoeca stuiornroaunnddfiancgt olorscathtiaotnm aanydc faaucstoerssi gthnaatl
imntaeyr fcearuensec esitgontahle ilnintekrfbeertewnceee ntot hteheU lAinSka bnedtwtheeenp itlohte oUrAgrSo uanndd sthtaet ipoinlo.tT ohre sgeroauttnridb ustteatsioconr. eTshweislel
fauttrrtihbeurteb escroatreeds wfroilml fu1rttohe1r0 bbea sreadtedon frtohmec 1o mtop 1a0r abtaivseedim onp atchtei tcohmaspoanraetaivche iomfpthaecta titt rhibaus toens. each of
the attributes.
Drones2017,1,5 8of17
Table2.Listofinfrastructuretypeswithattributesandmetrics.
InfrastructureType Metric
OpenUnpavedRuralRoads 1
PavedUrbanRoads 2
Highways 3
Houses/SmallBuildings 4
Skyscrapers 5
Dams 6
WindTurbines 7
RegularBeam&TrussBridges 8
Off-ShorePlatforms 9
SuspensionBridgeSuperstructures 10
Havingthesemetricsandattributesfortheseinfrastructuretypessetup,afunctioniscreatedthat
canprovideavaluebycombiningthesemetrics,attributesandweights. Inthismodel,equalweights
areassignedtoallthefiveattributesforthetypesofinfrastructure,althoughtheweightsassignedto
eachoftheattributescanbechangedbasedonitsimportancecomparedtootherattributes. Itmustbe
notedthatthetotalsumoftheweightsofallattributesmustadduptobe1. Eachoftheseattributes
alsohasitsownsetmetricsrangingfrom1to10,andthiswillbebasedontheimpactofthatparticular
attributeontheinfrastructuretype. Forexample,themetricvalueforthe‘sizeofstructure’attribute
forawindturbinedependsonthecomparativesizeofthewindturbinebeinginspectedwithregard
tootherwindturbines.
n
∑
E(x) = M w ×A (2)
E i i
i=0
Oncetheinfrastructurebeingscannedisidentifiedandallthemetricsandweightshavebeen
assigned to the attributes, a mathematical function is formulated as seen in Equation (2), which
quantifiestheenvironmentalcomplexity. Thiswasdonebymultiplyingtheinfrastructuretypemetric
(M )withthesummationoftheproductbetweentheassignedattributeweights(w)andtheattribute
E i
metric(A). Thisformulaquantifiestheenvironmentalcomplexityvaluefromaminimumof1toa
i
maximumvalueof100asshowninEquation(2).
The next parameter explored in detail with regards to the operational scenario is the data
acquisitionmethod. Table3wasputtogetherwithalistofdifferenttypesofdatacollectionmethods
thatmaybeperformedbytheUASasapartofitsSHMtasks. Thistableofdataacquisitionmethods
hasasetofmetricsrangingfrom1to10forSHMtasksthattheUASperformsbasedonitscomplexity.
Thevalueofthemetricsincreasesasthedifficultyofthetasksincreasesfromdistantlowresolution
imagerytoultrasoniccontactNDE.Thesetasksaresubdividedbasedonthetypeofdataitobtains
such: Imagery, 3D modelling, non-contact NDE and contact NDE. Each of these divisions of data
acquisitionmethodshasitsownsetofattributesbasedonthewaydataiscollected.
Table3.Listofdataacquisitionmethodswithmetrics.
DataAcquisitionType DataAcquisitionMethod Metric
Imagery DistantLowResolutionAerialImagery 1
Imagery Close-UpAerialImagery(<2m) 2
Imagery DistantHDAerialImagery(18MP+) 3
Imagery Close-UpHDAerialImagery 4
3DModelling 3DPhotogrammetryRendering 5
3DModelling LightDetection&Ranging(LiDAR) 6
Non-ContactNDE Non-ContactNDE:InfraredThermography 7
Non-ContactNDE Non-ContactNDE:GPR(GroundPenetratingRadar) 8
Non-ContactNDE Non-ContactNDE:LaserShearography 9
ContactNDE ContactNDE:Ultrasound 10
Drones2017,1,5 9of17
Similartotheenvironmentalcomplexity,thedataacquisitionmethodsarecorrelatedtoattributes
that contribute to the complexity of the data acquisition methods seen in Table 4. There are four
differentsetsofattributes,oneforeachdataacquisitionclass.
Table4.Listofattributeswithweightsfordataacquisitionmethods.
Imagery 3DModelling Non-ContactNDE ContactNDE
Attributes Weight Attributes Weight Attributes Weight Attributes Weight
Weight 0.3 Weight 0.3 Weight 0.3 Weight 0.3
Composition 0.2 Precision 0.3 Accuracy 0.3 Precision 0.3
Proximity 0.3 PointDensity 0.2 Proximity 0.2 Penetration 0.2
Stability 0.2 AcquisitionTime 0.2 AcquisitionTime 0.2 ScannedArea 0.2
Oncethemetricsandattributeweightsforthedifferentdataacquisitionmethodshavebeenset
up,afunctioniscreatedthatcanprovideuswithavaluebycombiningthesemetrics,attributesand
weights. In this model, different weights are assigned to all the attributes for the data acquisition
methodsbasedontheirrelativeimportancecomparedtootherattributes. Thetotalsumoftheweights
ofalltheattributesofeachdataacquisitionclassmustaddupto1. Theattributeweightsofallfour
classes are further rated by metrics ranging from 1 to 10 based on the comparative impact of that
particularattributeonthedataacquisitionmethod.Forexample,themetricvalueof‘equipmentweight’
forcollectingimagerydatadependsontheweightofthecameramountedontheUAScomparedwith
theweightofothercameras.
n
∑
T(x) = M w ×A (3)
T i i
i=0
Oncethetasktobeperformedontheinfrastructurehasbeenidentifiedandallthemetricsand
weights assigned to the attributes are finalized, a mathematical function is formulated as seen in
Equation(3),whichquantifiesthetaskcomplexitybasedonthedataacquisitionmethods. Theformula
canbeappliedfortheothertasksandthegreatesttaskcomplexityvalueamongstthemultipletasks
maybeusedasthefinaltaskcomplexityvaluetoconductanyfurtheranalysis. Inthisway,theformula
developedinEquation(3)quantifiesthetaskcomplexityvaluefromaminimumof1toamaximum
valueof100,therebyallowingtaskcomplexitytobescaledtotheenvironmentalcomplexity.
3.2. ValueModelforUASRatingandEvaluation
Autonomousmobilerobotictechnologyhasrapidlyevolvedoverthelastdecadeandhasbegun
to impact multiple sectors across various industries [16]. In order to characterize, compare and
evaluatetheadvancementsinautonomyofavehicle,commontaxonomiesandterminologieswill
needtobeputinplace. Currently,multiplegovernmentagenciessuchastheDepartmentofDefense
Joint Program Office (JPO), the U.S. Army Maneuver Support Center, and the National Institute
of Standards and Technology (NIST) have separate but related on-going efforts to describe levels
of robotic behaviors [4]. Although most of these are military related projects for combat systems,
similarautonomousbehavioralmodelscanbemodifiedforcommercialoperations. Althoughthereare
multiplereferencesandmetricsthathavebeencreatedforUASautonomyevaluation,here,aversion
oftheautonomyandtechnologicalreadinessassessment(ATRA)isusedandcanberetrofittedintothe
largermodelfordecisionanalysisofUASoperationsexplainedearlier.
Thetwoparametersthatareusedtoevaluatetheautonomyandrobustnessofasystembeing
implementedare,autonomylevel(AL)andtechnologyreadinesslevel(TRL).Theseparametersare
unifiedtoformtheATRA,whichcanbeusedtoprovideaholisticviewofthesoftwarecomponent
ofUAS[4]. TheformationofthenetworkbetweentheALandTRLtoformtheATRAisshownin
Figure7. TheuseofthisframeworktoevaluateautonomousUAScanbeusedtomakebothqualitative
andquantitativecomparisonsthatmaybeaccomplishedwiththehelpofaformulatedvaluefunction.
DDrroonneess 22001177,, 11,, 55 1100 ooff 1177
qualitative and quantitative comparisons that may be accomplished with the help of a formulated
Tvhaleucer efuanticotnioonf. sTuhceh carefaratimone wofo srukcehn aa bfrleasmeevwalourakt ieonnabthleast ceavnalbueatsiyosnt ethmaat tcicaanl lbyeu ssyesdtetmoacotimcapllayr eusthede
atou tcoonmopmayreo tfhoen aeuUtoAnVomtoya onfo othneer .UAV to another.

Figure7.Modelrelatingautonomylevelandtechnologyreadinesslevel.
Figure 7. Model relating autonomy level and technology readiness level.
TThhee AATTRRAAf frraammeewwoorkrkc acnanb ebeu suesdedto tmo meaesausruerteh ethme amtuartiutyriatyn danrodb ruosbtnuesstnseosfst hoef UthAe SU[A17S] .[1In7]t.h Iins
sthtuisd ys,tuthdeyf, rtahme efwraomrkewisourske dist ouisnevde stoti giantveehstoigwatteh ehyocwan thbeeyu sceadn ibneS HusMeda pinp lSicHatMio nasptpolidcaetteiornmsi ntoe
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w(AitLhFnUeSc)e [s1s7a,r1y8]m woidthifi nceactieosnssarfyro mmoodtihfiecratsioounrsc fersotmo fiotthoeurr sSoHurMcesa ptop fliict aotuiorn SsHaMge anpdpal.iTcahteioenxsa amgpenledoaf.
aThUeA eVxatmhaptlwe oafs aa sUsAemVb tlheadt wwiatsh aosfsfe-tmheb-lsehde wlficthom ofpf-otnheen-sthsealnf dcocmonptornoelnsytss taenmd cisountsreodl asynsdteemva ilsu uasteedd
banasde devoanlutahteeAd TbRasAedfr aomn ethweo ArkTdReAv eflroapmeedwtoordke mdeovnesltorpateedi ttso udseamgeonanstdraetfef eictsti vuesnageses ainndt heeffeevcatilvueantieosns
oinf athuet oenvoamluoautisonU AofS a,uwtohniloemalosuosk UeeApSin, gwthrialcek aolsfoi tkseoeppeinragt itorancakl eonf vitisr oonpmereanttio.nal environment.
TThhee ffiirrsstt ppaarraammeetteerr eexxpplloorreedd iinn ddeettaaiill isis ththee aauutotonnoommyy lelvevele l(A(ALL). )T.hTihsi sisi as aseste otfo mfmetreitcrsi ctshatht aist
iusseudse tdo tsoysstyemsteamticaatlilcya lelyvaeluvaatlue aatneda mndeamsueraes uthree tahuetoanuotmonooums ocaupsacbaiplitaibeisl iatineds lainmditalitmiointas toiof nas UoAf aS
U[1A8]S. [T1h8e].seT hmeseetrmic ertarincgreasn gfreosmfr o1m to1 1to0,1 r0e,prerepsreensetinntgin tghteh eacaccecsessisoino noof faauutotonnoommoouuss bbeehhaavviioorr ffrroomm
rreemmoottee--ccoonnttrroolllleedd UUAASSw iwthitrhu drimudeinmtaernytafirrys tofirrdste roflridgehrt cfolingthrot lucopnttoroful lluypa uttoo nfoumllyo usaucatopnaobmiliotiuess
wcaipthabnioli-thieusm wainth-p niolo-thudmecaisni-opni-lmot adkeicnigsiionnv-omlvakedinagt ianlvl.oTlvheedA aLt aolfl. aTUheA ASLis omf aa iUnlAySb aiss emdaoinnlyit bGaNseCd
founn ictt iGoNnsC, wfuhnicchtioanres,p wrihmicahry araett prirbimutaertyo aetntraibbluinteg taou etnonabolminygi anutthoenhoamrdyw ina rtehec ohmarpdownaernet coofmthpeoUnAenSt.
Tofh tehGe NUCASc.a Tphabe iGlitNieCs ocafpthaebiUliAtieSs aorfe tdheir UecAtlyS aprreo pdoirretciotlnya plrtoopthoertaiountoaln toom thoeu saucatopnaobmiliotiuess coafpthabeiUlitAieSs.
Tofh ethleis UtoAfSt.h Tehdei flfiesrte onft tlheev edlisffoefreanutt olenvoemlsy offo aruotuornoexmaym fpolre oisurc oemxapmilpedle iins Tcoabmlepi5l.ed in Table 5.
TTaabbllee 55.. LLiisstt ooff aauuttoonnoommyy lleevveellss ((AALL)) wwiitthh ddeessccrriippttiioonnss..
Autonomy Levels
AutonomyLevels
Level Description
Level Description
1 Remote Control
1 RemoteControl
2 Automatic Flight Control
2 AutomaticFlightControl
3 System Fault Adaptive
3 SystemFaultAdaptive
4 4 GPS GAPsSsiAstsesids tNedaNviagvaigtiaotnio n
5 5 PathP PaltahnPnlainngn i&ng E&xeEcxuectiuotnio n
6 RealTimePathPlanning
6 Real Time Path Planning
7 DynamicMissionPlanning
7 Dynamic Mission Planning
8 RealTimeCollaborativeMissingPlanning
8 9 Real TimeS CwoalrlmabGorroautipveD eMciissisoinnMg Paklainngning
9 10 Swarm GrouFupl lDAetcoinsoiomno Musaking
10 Full Atonomous