Cloud robotics: 5G paves the way for mass-market automation

AUTOMATION EVERYWHERE ✱
JUNE 28, 2016 ✱ ERICSSON TECHNOLOGY REVIEW 1
ERICSSON
TECHNOLOGY
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CLOUDROBOTICS:
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FORMASS-MARKET
AUTOMATION

✱ AUTOMATION EVERYWHERE
2 ERICSSON TECHNOLOGY REVIEW ✱ JUNE 28, 2016
MARZIO PULERI
ROBERTO SABELLA
AFIF OSSEIRAN
Robots, robotics, and system automation have shifted from the floor of the
research lab to becoming a crucial cost, time, and energy-saving element of
modern industry. Smart robots and their control systems have enabled entire
processes, like vehicle assembly, to be carried out automatically. By adding
mobility to the mix, the possibilities to include system automation in almost any
process in almost any industry increase dramatically. But there is a challenge.
How do you build smart robotic systems that are affordable? The answer:
cloud robotics enabled by 5G.
t h e i m pa c t o f r o b o t s a n d
r o b o t i c s has been greatest in the
manufacturing industry, particularly
on assembly lines and in hazardous
environments. Traditionally, robots have been
designed to perform repetitive pick-and-place
tasks, to carry out operations that require
a high degree of precision, as well as doing
hazardous jobs like welding and cutting. But
things are changing… Robots are beginning to
appear in all industries, and are being used to
carry out a wide variety of tasks.
Smarterthanever,modernrobotsarecapable
ofadaptingtochangingconditions.Thecurrent
drawbackofthesmartrobot,however,isthemassive
amountofintelligenceitneedstofunctioncorrectly
—resultingincomplexmachinesandcontrol
systems.Coupledwiththefactthatsmartrobots
tendtobebuiltwithallthehardwareandsoftware
theyneed,theseautomatonstendtobecostly—a
factorthatisslowingdowndevelopmentcyclesand
hamperinguptakeinnewsectors.
Cloudroboticsaimstochangethisbyputting
systemsintelligenceinthecloudandsimplified
roboticsontheground.
Withinthismodel,mobiletechnologyplays
5G PAVES THE WAY FOR
MASS-MARKET AUTOMATION
Cloud robotics

thekeyenablerrole—connectingthecloud-
basedsystemtotherobotsandcontrollersina
system.And,itishigh-performancemobilitythat
willprovidethelatencyandbandwidthneeded
tosupportsystemstabilityandinformation
exchange,whichinturnfacilitatesthebuildingof
sophisticated,yetaffordable,roboticsystems.
Withinmobile,radiotechnologieswillprovide
thewantedlevelofperformance,andsoitisthe
capabilitiesof4Gand5Gradiosystemsthatwill
enable5Gcloudroboticsandfacilitatetheuptakeof
roboticsinnewapplications.
Leadingindustries
Naturally,manufacturingisoneoftheindustries
takingtheleadwhenitcomestocloudrobotics,
butotherssectorslikehealthcare,transportation,
andconsumerservicesareamongtheforerunners
contributingtotheevolutionofrobotics.
Inmanufacturing,cloudroboticswillimprove
theperformanceofaproductionplant,for
example,throughpreventivemaintenance,and
supportadvancedleanmanufacturing.Bystoring
intelligenceinthecloud,itispossibletoincreasethe
levelofautomationofasystem,regulateon-the-fly
processes,andpreventmalfunctionandfaults.
Inthehealthcaresectoremergingapplications
includerobot-assistedremotepatientcare,
automationandoptimizationofhospitallogistics,
andcloud-basedmedicalservicerobots.Asisthe
caseinmostindustries,initialsolutionswilltake
careofsimpletasks,withdevelopmentleadingto
morecomplexanddemandingapplications,suchas
remotesurgery,furtherdowntheline.
Driver-assisted,autonomous,andsemi-
autonomousvehicles,automatictransportation
forthedisabled,andunmanneddeliveryservices
arejustsomeoftheemergingapplicationsin
transportation.Andinconsumerservices,domestic
robots,automatedwheelchairs,personalmobility
assistants,andleisurerobotsexemplifythetypesof
solutionsonthedesigntable.
TheIoTandthefourthindustrialrevolution
Whilesomeindustriesmightbeleading
development,allsectorshaveaparttoplayinthe
evolutionofcloudrobotics,astheydeployrobotsto
carryoutawidevarietyoftasks[1].Herearejusta
fewexamples:
〉〉 farming:spraying,fertilizing,harvesting,andmilking
〉〉 construction:assembly,dispensing,laying,
andwelding
〉〉 utilities:remotecontrol,inspection,andrepair
AsakeyelementoftheIoTandanenablerofthe
fourthindustrialrevolution,roboticsandrobots
willvarygreatlyintermsofapplication,agility,
complexity,cost,andtechnicalcapability.But
most—ifnotall—robotswillrequirewireless
connectivitytothecloudandwideranalytics
ecosystem[2].
Ofalltheindustriesthatwillshaperobotics,
industrialIoTisexpectedtohavethegreatest
impactintheshortterm.Andso,tobestexemplify
4G AND 5G RADIO
SYSTEMS WILL ENABLE CLOUD
ROBOTICS AND FACILITATE THE
UPTAKE OF ROBOTICS IN NEW
APPLICATIONS
Terms and abbreviations
AMCL — Adaptive Monte Carlo Localization | CAN — controller area network | COTS — commercial off-the-shelf |
DWA — dynamic window approach | NAT — network address translation | ROS — robot operating system |
UGV — unmanned ground vehicle | VM — virtual machine

Infrastructure and
upgrading costs
Minimal cabling and
infrastructure adaptations
5G
Real-time automation Ultra-low latency communications
Safety-critical systems Ultra-high availability and reliability
Mobilecapabilities
Criticalfactors
Figure 1:
Mobile connectivity
offers flexibility

thebusinessmodelforcloud-connectedrobots,this
articlefocusesonindustrialIoT.
Stakeholderroles
Withinthecloud-roboticsdomaintherearea
numberofdifferentrolesorplayers:
〉〉therawrobotproviderwhobuildsCOTSrobotsand
customizedmachinesbasedonrequirementsset
byamanufacturer
〉〉thelocalaccessproviderorICTplayerwhoconnects
theon-siterobots(inafactory,onaconstructionsite,
oronafarm,forexample)toalocalgatewayorhub
〉〉thelocalsystemsintegratororICTplayerwho
integrates(wiredandwireless)access,data
processing,cloudsystems,securityprocedures,
andmanufacturingexecutionsystems(MESs)
infrastructure
〉〉theglobalnetworkaccessandcloudproviderorICT
playerthatextendsconnectivitytoaglobalmacro
networkoutsidethesite
〉〉theindustrialIoTICTsystemproviderorICTplayer
thatautomates,digitalizes,andconnectsthefull
industrialcycle,fromrawcomponentstoproduct
servicing,andrecycling
Ecosystem
Giventhepotentiallymassiverangeofusecases,
thebusinessprospectsforcloud-basedrobotics
lookpromising.However,asisoftenthecase
withopportunity,cloudroboticspresentscertain
challenges.Sincetheone-size-fits-allbusiness
modeldoesn’twork,asharedplatformforcommon
functionsisneeded;whichcanthenbetailoredto
particularapplicationsbybuildinguse-case-specific
modulesontop.
Spectrum
Thebusinessmodelsthatapplytoagivenusecase
willultimatelydependonthespectrumsharing
regime—licensedorunlicensed—whichwillinturn
settheboundariesfortheperformanceofwireless
cloud-robotictechnologysolutions.
Security
Informationsecurityneedsdifferfromoneindustry
tothenext.Forexample,thedemandsposedby
industrialIoTapplicationsaremorestringentthan
theyareforconsumerproducts.Forindustrial
IoT,themultipleactorsinthesupplychainneed
tobeabletosharedatainacontrolledandsecure
environment.Tobeabletoaddressthesecurity
requirementsformostindustrialIoTusecases,
informationsharingneedstoensure:
〉〉theabilitytocontrolandlimitdataaccess
〉〉dataisavailabletoallpartiesinthevaluechain
atalltimes
〉〉thatallpartiescantrustthedatabeingshared
Thebenefitsofwirelessconnectivity
Whendeployingacloud-basedroboticsystem,
constantandreliableconnectivityforeachrobot
isessential,irrespectiveofthetypeofrobot,the
tasksitcarriesout,ortheenvironmentinwhichit
operates.Sincewithwireless,thereisnoneedfor
complexandinflexiblewirelineconnections,itis
theobvioussolutiontoprovidecommunicationto
systemsthatrelyonmobilerobotsmovingalong
setpathsorfollowingon-the-flydirections.But
evenforsystemslikeassemblylineswherefixed
robotsaretheprimaryautomatons,shiftingto
wirelessconnectivitysignificantlysimplifiesthe
overallsolution—limitingcablingneedstojust
powersupply.AsFigure1shows,whatwireless
connectivityachievesisflexibility.Andso,when
changesneedtobemade,forexample,tothe
layoutofamanufacturingplanttoadapttoanew
productionprocess,ortoaddnewrobots,the
flexibilityofferedbywirelessconnectivityensures
thatmodificationcostscanbekepttoaminimum,
whichinturnsoffersgreaterflexibilityindecision-
makingatthebusinesslevel.
THE FLEXIBILITY OFFERED
BY WIRELESS CONNECTIVITY
ENSURES THAT MODIFICATION
COSTS CAN BE KEPT TO A
MINIMUM

Selectingthebestwirelessconnectivity
technologynaturallydependsontheoperating
environmentofthegivenscenario.Withoutdiving
intospecificusecases,NB-IoT,Wi-Fi,LTE,and
soon5G technologieslooselyapplytodifferent
categoriesofapplications,asfollows:
NB-IoT technologycanbeappliedtoindustrial
applicationslikeprocessmonitoringinchemical
plants.Thistechnologyisattractiveintermsof
batterylife,cost,andcoverage,butitisnotsuitable
forapplicationslikeremotecontrolofmachinery
thatdemandhighbitrateandlowlatency.
Wi-Fi isagoodoptionforscenarioswhererobots
aremostlyfixedandplacedinawell-definedcontext.
DeploymenttimeforWi-Fi isshort,itprovidesan
easy-to-manageaffordablenetworkthatisprivate,
withlowlatencyandconsiderablebandwidth—
withouttheneedforanexternalnetworkoperator.
Unfortunately,thesuitabilityofWi-Fi islimited.
Firstofall,Wi-Fi offersafewnon-overlapping
channels,butmostofitschannelsoverlapandcreate
interference.Thepresenceofradionoise,which
iscommoninproductionenvironments,further
degradestheperformanceofaWi-Fi network,as
itoperatesinunlicensedspectrum.Unmanned
groundvehicles(UGVs)andothertypesofmobile
robotsthatmoveinandoutofthecoverageofa
Wi-Fi hotspotcouldsuffersevereimpairments,
likeapplicationrestart,duetothetimeneededto
reestablishtheconnection—asWi-Fi doesnot
supportfasthandover.Thehandoverissuecanbe
overcomebyenablingrobotstosimultaneously
receivemoreWi-Fi channels,butwithaconsequent
increaseincostandcomplexity.
LTE guaranteeshighbandwidthandreasonable
latency,andoffersfullcontrolofinterferenceand
handover.Assuch,LTE immediatelyresolvesallof
theissuespresentedbyWi-Fi.However,astandard
LTE deploymentrequiresoperatorinvolvement
andaSIM foreachconnectedterminal.Each
deploymentrequiresanagreementwithanoperator,
incurringoperationalcosts,whichcanbesignificant
especiallywhenitcomestointernationalenterprises
thathaveoperationsinmultiplecountries,
necessitatingseveralagreements.
Toovercometheselimitations,LTE unlicensed,
operatinginafree5GHzbandwidth,willbe
introduced.Withouttheneedforanoperator,this
technologysupportsthedeploymentofaprivate
LTE network,similartoWi-Fi,butwithmostofthe
benefitsofLTE.Theuseofunlicensedspectrum,
however,maygiverisetointerferencewithother
nearbyradios,orfromradiosusingthesameor
adjacentspectrum.
5G willbethetechnologyofchoicefor
applicationsthatrequireveryhighcapacityaswell
asverylowlatency.5G networks,incorporating
LTE accessalongwithnewairinterfaces,willoffer
thecoverage,bandwidth(1Gbpsperuser),andsub-
1mslatencyneededtosupportthetime-critical
applicationsofindustrieslikehealthcareand
agriculture.AndsimilartoLTE,solutionswillbe
deployedineitherlicensedorunlicensedspectrum
withthesamebenefitsanddrawbacks.
Benefitstoindustry
Inmanufacturing,mobilerobotscanbeusedto
advantagetotransportgoodsbetweenvarious
stationsinaprocessortoandfromdepots.
Deployingmobilerobotsinlogisticsimproves
productivityandsupportstheimplementationof
effectiveleanmanufacturing.Aslongasthereareno
constraintsimposedintheirmovementcapabilities
causedbyunexpectedobstaclesordirt,robots
cancarryoutanysequenceofeventstoensurethat
materialsarriveattherightplacejustintime.
Inthehealthcaresector,robotscanbeusedto
transportspecimens,drugs,andbedlinentowards,
labs,pharmacies,anddepositories—offloading
repetitivelow-leveltasksfromskilledhospitalstaff.
Asinthemanufacturingcase,theflexibilityoffered
bymobilerobots,whichcanbereinstructedonthe
flytocarryouttasksdependingoncontingentneeds,
facilitatesoperationoptimizationandcostreductions.
Inagriculture,roboticscanbeappliedtothe
movementofgoodsandequipmentbetweenfields,
stalls,andbarns.Mobilerobotscanbedeployed
directlyinpasturesandonarablelandtocarry
outtaskslikemonitoring,spraying,pruning,and
harvesting.Overall,theuseofroboticswillincrease
productivityandreduceopex.
Atthelowestlevel,itisthelevelofperformance

offeredby4G,5G,andWi-Fi solutionsinterms
ofbandwidthandlatencythatwillenablerobot
controlfunctionalitiestobetransferredtothecloud.
4G and5G solutionsaremoreadvantageousthan
Wi-Fi,astheyarelesssensitivetointerference
causedbyinfrastructureandothermachines,and
provideseamlesshandover,ensuringcontinuous
connectivityforrobotsastheymovebetweencells.
Theprimarylinkcharacteristicthatdetermines
thestabilityofrobotcontrolislatency,andsofor
mostusecases,4G isagoodstartingpointtoprovide
connectivity.Butas5G supportsawiderrangeof
requirementsincludingsub-millisecondlatency,it
willfulfillboththebandwidthandlatencyneedsthat
applicationslikeremote-controlroboticsdemand.
Sub-millisecondlatencyisneededwhentouchor
force(hapticcontrol)sensorsareusedtomaintain
controloverrobotmovement.Scenariosthatrequire
visualfeedbackneedaround-triplatencyofupto
10ms—dependingonthetaskbeingperformed—
whereasround-triptimesofupto100msaregood
enoughtomaintaincontrolofarobotasitmoves
fromoneplacetoanother.Suchresponsetimes
implyhighbandwidthavailability,eventhough
manyremoteapplications—withtheexception
ofthosethatrelyoncontrolwithHDcamerasand
stereoscopics—oftenneedtotransferonlysmall
amountsofdatainordertofunction.Transferring
sensorandcommanddatausuallyrequiresseveral
tenstoa100kbps.
Cloudintelligence
Ideally,smartsrobotsneedhigh-performance,
low-costcomputingcapabilities.Tosatisfythis
need,cloudroboticsdesignisbasedontheuseof
automatonswithaminimalsetofcontrols,sensors,
andactuators,withsystemintelligenceplacedin
thecloud,wherecomputingcapacityisunlimited.
Andwhileproperradioconnectivitycansatisfy
latencyandbandwidthdemands,careneedstobe
takenwithdistributionandmanagementofservices.
AsshowninFigure2,remote-controlusecases
shouldbedistributedamongafewremotecloud
datacenters,eachdedicatedtoaspecifictask—all
Smart facility
managing service
Cloud management
service
Cloud
network
host
host
Robot navigation
service
Robot
Robot data
processing service
Remote
controlData center
Figure 2:
An example cloud
architecture

USB
CAN bus CAN bus
Serial port
USB
USB
4G module
Control board
Miniature
PC
Brushless driver
Left motor
Brushless driver
Right motor
Wi-Fi
module
Laser
rangefinder
Figure 4:
UGV architecture
Smart facility
managing service
Cloud management
service
Work cell 2
Work cell 1 Warehouse
Cloud
network
host
host
Robot 1
Robot 2Robot navigation
service
Robot
Robot data
processing service
Data center
Two possible routes:
clockwise and
counter-clockwise
Collision avoidance
in real time
Remote
control
Figure 3:
Logistics in a warehouse

underthecontrolofacloudmanagementservice
thathandlesnetworkconfiguration,aswellasthe
allocationofprocessesandVMs.
Controlservicescanbecategorizedeitherastime
critical(suchasnavigationandsensorinformation
processing)ornot.Tominimizetheeffectonlatency,
thecloudmanagementserviceshouldplacetime-
criticalservicesclosetothebasestationservingthe
workingarea,asdoingsoguaranteesstabilityand
reactiontimes,enablingtaskstobecompletedwithin
theallocatedinterval.Servicesthatarenottime-
critical—suchasthefacilitymanagementfunction
controllingtheplant—canbeplacedremotely,as
theiractionsdonotaffectreal-timebehavior.
Themainservicesrequiredbycloudroboticsare:
〉〉smartfacilitymanagement—includingdataanalytics
androbotcoordinationmanagement
〉〉imageprocessing—forpatternrecognition
〉〉navigation
Thesmartfacilitymanagementservicecoordinates
userrequestsforservicesandrobotactivitiesby
matchingrobotstorequestedtasksaccordingto
anoptimizationcriterionsuchasminimumpath
length.Theimage-processingserviceinvolves
theextractionofrelevantcameradatatoaid
navigationandothertasks.Thenavigationservice
calculatesthepaththerobotshouldtaketoreachits
destinationonthebasisofitscurrentpositionand
sensordata.Itisalsoresponsibleforthecollision
avoidancemechanism.
Toensurerobotstability,navigationtasks
typicallyrequirearound-triplatencyunder100ms
[3],andmostofthistimeistakenupbyprocessing
innetworkrouters.Thecontrolloopsforscenarios
thatusevisual,force,ortouchfeedbackarehighly
timecritical,requiringround-triptimesoflessthan
1msanduptoabout10ms,forsomeapplications.To
maintainrobotstabilityinsuchscenarios,sensors,
processingandcontrolservicesshouldbelocated
closetothebasestationprovidingconnectivity.
Inanumberofcases,robotcontrolservicesneed
tobedistributedamongdatacenters.Thisisthe
case,forexample,whenarobotwithstricttiming
constraintsoperatesinalargespacelikeafieldor
adepot;orwhenrobotsmovebetweenradiocells;
orforload-balancingreasons,whenseveralmobile
robotsconvergeonthesamearea.Insuchcases,
thecloudmanagementservicedistributescontrol
servicesinrealtime,transferringcomputational
processeswithoutdisruptiontoservicesinplace.
Thiscapabilityistypicallysupportedbydatacenter
hypervisors.Thecloudmanagementservicemight
inadditionmanagetheconfigurationofthephysical
networktooptimizenetworkconnectivity.
Logisticsineverything
Logisticsisasignificantpartofmostindustrial
processes,andassuchisagoodusecasetotestthe
applicationofcloudrobotics.Itofteninvolvesthe
useoffixedandmobilerobots,aswellastheirmutual
cooperation.Withthisinmind,Ericssonsetupa
testbed—showninthephotographinFigure 6—
toassesstheimplementationofthejust-in-timelean
manufacturingconcept,monitoringrobotsasthey
shuttledgoodsamongwarehousesandworkcells,as
showninFigure3.
Duringtrials,twoUGVsshuttledgoodsbetween
threeserviceareas.TheUGV architecture,
illustratedinFigure4,includestheelementsneeded
todrivetherobot’smotor,collectsensorinformation
forlow-levellocalcontrol,andhandledatatransferto
thecloud-basedremotecontroller.
Therobotswereequippedwithalaser
rangefinderfornavigationandawebcamfor
patternrecognition.Trialsstartedwithalearning
phase,duringwhichthelaserrangefinderbuilt
upamapoftheworkingarea—whichtherobot
subsequentlyusedforlocalizationpurposes.Wheel
odometrycontrolledtherobot’smovements.During
operation,thelaserrangefinderdetectedobstacles
intherobot’spath,activatingcollisionavoidance
mechanismswhennecessary.
Allcontrolservicesforthefacilitymanagement,
navigation,anddataprocessingwerelocatedina
cloud,runningindatacentersorondedicatedhosts.
Thenavigationsystemusedaspreadmiddleware
ROS [4].Robotlocalizationwasimplementedusing
odometryand2DlaserdatawiththeAMCL [5]
algorithm,whilenavigationwasbasedonDWA [6]
forcollisionavoidanceandDijkstra’salgorithmfor
findingtheshortestpath.

Thetestareacompriseda6mx7mrectangle,
withthewarehouseandworkcellsplacedatthreeof
thecorners.Twoapplicationscenariosweretested:
transportationofgoods,andtransportationofgoods
withobstacles.
Usecase—transportationofgoods
Thefirstusecasecase—illustratedinFigure3—
involvedacontrollerappandon-siteworkers.The
robotsweretaskedwithpickingupgoodsatthe
warehouseandtransportingthemtooneofthework
cells.Pickuprequestswereprocessedbyasmart
facilitymanagingservice,accordingtothefollowing
sequenceofevents:
〉〉thefacilitymanagementserviceselectstheclosest
availablerobottothewarehouse,instructingittopick
upthegoods
〉〉thecontrollerappsendsamessagetoaworkeratthe
warehousetoloadtherobotwiththegoodsrequested
〉〉whenloadingiscompleted,theworkerusesthe
controllerapptoinformthefacilitymanagement
servicethattherobotisreadytomove
〉〉therobotproceedstothedestinationalongtheshortest
pathandinformsthesystemofitsarrival
〉〉thecontrollerappsendsamessagetoaworkeratthe
destinationtounloadthegoodsfromtherobot
〉〉theworkerusesthecontrollerapptoinformthefacility
managementservicethattheoperationiscomplete
〉〉thefacilitymanagementservicenavigatestherobot
awayfromtheloadingbay,settingitsstatustoavailable
fornewtasks
Usecase—managingobstacles
Thesecondusecase—illustratedinFigure5—
aimedtoreproduceacompletelyautomatedlogistics
process.Forthesetrials,non-industrialrobots
wereused.Threeroboticarmswereputinplaceto
loadandunloadpalletsfromthemobilerobots.An
automatedwarehousewassimulatedbyarotating
platform,andtwoautomateddoorswereplaced
alongthenavigationtracks.Theobjectiveofthis
proof-of-concepttrialwastoverifytheinteraction
andcoordinationofmultiplerobotscontrolledby
multipleremotedistributedservices.Eachrobot
wasassignedadedicatedcontrolservicefordirect
control,withthefacilitymanagementservice
coordinatingactionsamongtherobotsinrealtime.
Fixedandmobileobstaclesandatrafficlightwere
placedintheworkingareatotestpatternmatching
andforceon-the-flyreroutingonred.Thelaser
rangefinderwasusedtodetectobstacles,andimages
fromthewebcamwereprocessedbycloud-based
analyticstodeterminethestatusofthetrafficlight.
Radioconnectivitywasprovidedbasedonthe
performancecharacteristicsofatypicalpublic
operator4G mobilenetwork.Duringtrials,round-
triplatencyofabout40mswasmeasured—sufficient
forstablenavigationandcontrolofroboticarms.
Thebandwidthavailableforeachrobotwasalso
goodenoughtotransferthedatacollectedfromthe
cameraandsensors—withapeakbitrateof25Mbps
whenthereceptionwasgood,and7Mbpswhenit
waspoor.
Latencywasnotadeterminingfactorinthe
robots’abilitytoavoidobstaclesorreacttopattern
recognitioninstructions.However,thepresence
ofasymmetricNAT functionalitytypicallyused
inLTE operatornetworkstoavoidpeer-to-peer
communicationprovestobeaproblem.This
functioncanberemovedbutwhenitisinplace,the
robotsmustbedesignatedasclientsandthecontrol
servicesasservers,whichinturnrequirestherobots
tocontinuouslypollthecontrolserviceforupdates.
Differentiationofcloud-basedcontrolprocesses
forservicesthataretimecriticalandthosethatare
notisachievedbyallocatingappropriatemachines
toensurecorrect(non-erratic)robotbehavior
atalltimes.Duringtrials,testswerecarriedout
ontherobot’sopticalsensorstodetermineany
weaknesses.Thelaserrangefinderwassensitive
tosunlightreflectionsonthefloor,whichcreated
faketargetsthatneedtoberemovedtoensure
correctnavigation.Theprocessingofimages
fromthewebcamalsoprovedtobesensitiveto
lightconditions,necessitatingtheintroductionof
compensationfunctionstoproperlyidentifythe
trafficlightcolors.
Thecontrolprocessesfortheroboticarmswere
dividedintothreemodules,twoofwhichwerecloud
based.Thefirstcloud-basedprocessprovided
thecorrectmovementsequenceforthearms,in

Work
cell 2
Work
cell 2
Work
cell 1
Work
cell 1
Ware-
house
Ware-
house
Robot 1
Robot 1
Loading
Unloading
UnloadingRobot moves awayRobot moves away
Loading
Obstacle
Obstacle
Rerouting
Traffic
light
Traffic
light
Figure 5:
Tests for warehouse logistics
Figure 6:
Test bed in Peccioli, Italy

Acknowledgments
The authors wish to acknowledge: Sandor Albrecht, Åsa Degermark, Marika Stålnacke, Rowan Högman, Harald
Kallin, and other colleagues for helping us create the demo at MWC 2016.
References:
1. Ericsson, 2015, Ericsson Business Review, Manufacturing reengineered: robots, 5G and the Industrial
IoT, available at: https://www.ericsson.com/res/thecompany/docs/publications/business-review/2015/
ebr-issue4-2015-industrial-iot.pdf
2. Ericsson, 2015, Ericsson Technology Review, Industrial remote operation: 5G rises to the challenge,
available at:
http://www.ericsson.com/res/thecompany/docs/publications/ericsson_review/2015/etr-5g-remote-control.pdf
3. BAE Systems, 2008, Remote Operation of the Black Knight Unmanned Ground Combat Vehicle,
available at: http://www.nrec.ri.cmu.edu/projects/black_knight/tech/spie_bk_final.pdf
4. ICRA, 2009, ROS: an open-source Robot Operating System, available at: https://www.willowgarage.com/
sites/default/files/icraoss09-ROS.pdf
5. AAAI/IAAI, 1999, Monte Carlo Localization: Efficient Position Estimation for Mobile Robots, available at:
http://robots.stanford.edu/papers/fox.aaai99.pdf
6. IEEE, 1997, Robotics & Automation Magazine, The dynamic window approach to collision avoidance,
abstract available at: http://dx.doi.org/10.1109/100.580977
accordancewithrequestssentbythemanagement
service.Thesecondcloud-basedprocessperformed
theinversekinematictransformationstotranslate
Cartesiancoordinatesintojointcoordinates.The
thirdprocess,placedonaPCclosetotherobot,
locallycontrolledthemovementsoftherobot’sjoints
atalowlevel.Withround-triplatencyof40ms,the
actionsoftherobotswerestable—shorterlatency
wasnotneededastheactionsinvolvedinthetrial
werepick-and-place.Thecoordinationofrobotsby
themanagementservicewasnotaffectedbylatency
intheseoperatingconditionseither.Theonlyvisible
effectwasamoderateslowingdownofoperations.
Conclusions
Theinterestincloudroboticsisontherise.Its
initialimpactisalreadybecomingapparentin
manufacturing,agriculture,andtransportation,
andinscenarioswherelogisticscanbeoptimized—
suchasharborsandhospitals—anditislikelythat
domesticapplicationswillsoonexperienceashiftin
theevolutionofhome-helprobots.
Therisinglevelofintelligenceinrobotsallows
themtoadapttochangingconditions,whichis
positivefordevelopment,butsignificantlyincreases
theircomplexity.Byinsteadconnectingrobotsand
placingthiscomplexity(intelligence)inacloud,
affordableminimal-infrastructuresmartrobot
systemswithunlimitedcomputingcapacitywill
evolve.Attheoutset,4G systemsandWi-Fi willbe
usedtoprovidecloudroboticswiththenecessary
connectivity,but5G isthetargettechnologytruly
capableofdeliveringtheperformanceneededto
supporttheapplicationsofthefuture.
Whilecloudroboticsmaystillbeinitsearlystages
ofdevelopment,thisarticlehasoutlinedaproposed
architectureforcloudroboticsthatreliesonmobile
connectivity.Thisarchitecturehasbeentrialed,
provingthattheconceptisviable,settingthedirection
foradvancedcloud-basedroboticsystems.

Marzio Puleri
◆ is a senior researcher
at Ericsson Research in
Italy. He joined Ericsson
in 1993, working on
microelectronics and a
number of communication
technologies. He is currently
working on cloud robotics
and radio beamforming.
Before joining Research,
he was system manager
and technical coordinator
of microwave and packet
switching systems. He
holds an M.Sc. in electronic
engineering from the
Sapienza University of
Rome, Italy.
https://www.linkedin.com/
in/marzio-puleri-92b6568
Roberto Sabella
◆ is the manager of the
Italian branch of Ericsson
Research. He is the Ericsson
reference person for the
5G for Italy initiative and
holds the Presidency of
the Tuscany Technology
District on ICT. His expertise
covers several areas of
telecom networks, including
packet-optical transport,
optical solutions for mobile
backhaul and fronthaul,
and photonics technologies
for radio and data centers.
He has authored over 100
papers for international
journals, magazines,
and conferences, as
well as books on optical
communications, and holds
more than 30 patents.
He has served as adjunct
professor of telecom
systems at the Sapienza
University of Rome, Italy.
He is a senior member of
IEEE. He holds a D.Eng. in
electronic engineering from
the Sapienza University of
Rome, Italy.
in/roberto-sabella-2054732
Afif Osseiran
◆ is the director of
radio communications
at the Ericsson CTO
office. He holds a Ph.D.
in radio communcation
systems from KTH Royal
Institute of Technology,
Stockholm, Sweden, an
M.Sc. in electrical and
communication engineering
from Polytechnique
Montréal, Canada, and
a Masters in electrical
engineering from INSA
Rennes, France. Since
joining Ericsson in 1999, he
has held several positions,
including management of
METIS, the EU 5G flagship
project. He has published
over 50 technical papers
for conferences and
international journals , has
co-authored a book on
5G and two books on IMT-
Advanced. Osseiran is a
senior member of IEEE.
in/afifosseiran
theauthors

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