Time-Critical Cooperative Control of Autonomous Air Vehicles

251 Pages·2017·17.451 MB·English

by  Isaac Kaminer, António M. Pascoal, Enric Xargay, Naira Hovakimyan, Venanzio Cichella, Vladimir Dobrokhodov

Time-Critical Cooperative Control
of Autonomous Air Vehicles

Time-Critical Cooperative
Control of Autonomous
Air Vehicles
Isaac Kaminer, António Pascoal,
Enric Xargay, Naira Hovakimyan,
Venanzio Cichella, and
Vladimir Dobrokhodov
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TothepreciousmemoryofmymotherOlgaandgrandmotherLeah,
tomyfatherIsaac,tomysonsEmanuelandConor,
toJenny,Cristof,andShoshiandtoEkaterina,mylove.
IsaacKaminer
TothebelovedmemoryofmyparentsManuelandIdalina,
tomywifeStephanieandsonRicardowithboundlessloveandgratitude.
AntónioPascoal
Tomyparents,ElviraandEnric,withadmiration,gratitude,
andunconditionallove.
EnricXargay
Tothechildren:Arik,Arsen,andMariawithallmylove,withallmyheart.
NairaHovakimyan
TomygrandfatherFerruccio,
tothelovingmemoryofmygrandparentsGina,Giovanna,andVenanzio,
alwayspresentinmythoughts,inmyheart,inmylife.
VenanzioCichella
Tomyparents,ValentinaandNicholai,withgratitudeandadmiration,
tomygirlsElenaandAnnawhoarethepurposeofmylife,
andtomybrotherOlegwhoisalwaysthereforme.
VladimirDobrokhodov
List of Figures
Fig.1.1 Cooperativeroadsearchusingmultiplefixed-wingUAVs. 6
Fig.1.2 CooperativeroadsearchusingmultiplemultirotorUAVs. 7
Fig.1.3 Sequentialauto-landing. 9
Fig.2.1 Conceptualcontrolarchitectureforfixed-wingUAVs. 25
Fig.2.2 ConceptualcontrolarchitectureformultirotorUAVs. 25
Fig.2.3 Trajectorygenerationfortwovehicles;spatialdeconfliction. 29
Fig.2.4 Trajectorygenerationfortwovehicles;temporaldeconfliction. 29
Fig.2.5 Trajectorygeneration;arrivalmargin. 30
Fig.2.6 Adaptiveaugmentationloopforafixed-wingUAV. 37
Fig.3.1 Followingavirtualtargetvehicle;problemgeometry. 42
Fig.3.2 Shapingtheapproachtothepath. 45
Fig.3.3 Path-followingkinematicclosed-loopsystem. 47
Fig.3.4 Effectofthecharacteristicdistancedontheconvergenceofthevehicletothe
path. 49
Fig.3.5 Path-followingclosed-loopsystem. 50
Fig.3.6 Framed3Dspatialpathandmaingeometricproperties. 53
Fig.3.7 PathfollowingofasingleUAV. 54
Fig.3.8 Path-followingperformancerelatedtoFig.3.7. 55
Fig.3.9 PathfollowingofasingleUAV;fastconvergencetothepath(d =50m). 56
Fig.3.10 Path-followingperformancerelatedtoFig.3.9. 57
Fig.3.11 PathfollowingofasingleUAV;slowconvergencetothepath(d =250m). 58
Fig.3.12 Path-followingperformancerelatedtoFig.3.11. 59
Fig.3.13 Projectionofvectorwˆ1ontothetangentvectortothepathˆt. 59
Fig.4.1 Time-criticalcooperativepath-followingclosed-loopsystem. 70
Fig.4.2 Networktopologies. 75
Fig.4.3 Simultaneousarrival;3Dtime-trajectories. 76
Fig.4.4 Simultaneousarrival;pathdeconfliction. 77
Fig.4.5 Simultaneousarrival;missionevolution. 78
Fig.4.6 Simultaneousarrival;path-followingperformance. 79
Fig.4.7 Simultaneousarrival;timecoordination. 80
Fig.4.8 Simultaneousarrival;informationflow. 80
Fig.4.9 Sequentialauto-landing;3Dtime-trajectories. 81
Fig.4.10 Sequentialauto-landing;trajectorydeconfliction. 82
Fig.4.11 Sequentialauto-landing;missionevolution. 83
Fig.4.12 Sequentialauto-landing;path-followingperformance. 84
Fig.4.13 Sequentialauto-landing;timecoordination. 85
Fig.4.14 Sequentialauto-landing;informationflow. 85
Fig.5.1 NASA’sIceBridgeprogram;coordinationofaDC-8aircraftwiththe
CryoSat-2satelliteforradaraltimetercalibrationandvalidation. 89
xii ListofFigures
Fig.5.2 Sequentialauto-landing;3Dtime-trajectories. 94
Fig.5.3 Sequentialauto-landing;trajectorydeconfliction. 96
Fig.5.4 Sequentialauto-landingwithloosetemporalconstraints;missionevolution. 98
Fig.5.5 Sequentialauto-landingwithloosetemporalconstraints;path-following
performance. 99
Fig.5.6 Sequentialauto-landingwithloosetemporalconstraints;timecoordination. 100
Fig.5.7 Sequentialauto-landingwithloosetemporalconstraints;inter-vehicle
separation. 100
Fig.5.8 Sequentialauto-landingwithloosetemporalconstraints;informationflow. 101
Fig.5.9 Sequentialauto-landingwithstricttemporalconstraints;timecoordination. 101
Fig.5.10 Sequentialauto-landingwithrelativetemporalconstraints;timecoordination. 102
Fig.6.1 Sequential auto-landing; partially quantized control law with fine
quantization. 111
Fig.6.2 Sequentialauto-landing;fullyquantizedcontrollawwithfinequantization. 112
Fig.6.3 Sequential auto-landing; partially quantized controllaw with coarse
quantization. 113
Fig.6.4 Sequentialauto-landing;fullyquantizedcontrollawwithcoarsequantization. 114
Fig.7.1 Extendednetworkwithlocalestimators. 117
Fig.7.2 Edgesnapping;two-wellfunctionandforcingfunctionforlink-weight
dynamics. 120
Fig.7.3 Convergencetimeofcoordinationerrordynamicswiththreedifferent
algorithms. 121
Fig.7.4 Histogramsanddistributionfitsofthenormalizedconvergencetime. 122
Fig.7.5 Sequentialauto-landing;modifiedcoordinationcontrollaw(hybrid). 123
Fig.7.6 Sequentialauto-landing:informationflow(hybrid). 123
Fig.7.7 Sequentialauto-landing;linkweightsandlocalconnectivity(hybrid). 124
Fig.7.8 Sequentialauto-landing;modifiedcoordinationcontrollaw(edgesnapping). 125
Fig.7.9 Sequentialauto-landing:informationflow(edgesnapping). 125
Fig.7.10 Sequentialauto-landing;linkweightsandlocalconnectivity(edgesnapping). 126
Fig.8.1 SIGRascalUAVwithtwodifferentonboardcameras. 130
Fig.8.2 Network-centricarchitectureoftheairborneplatform. 130
Fig.8.3 Cooperativeroad-search;trajectorygeneration. 132
Fig.8.4 Cooperativeroad-search;timecoordinationduringthetransitionphase. 133
Fig.8.5 Cooperativeroad-search;cooperativepath-followingcontrolduringthe
road-searchphase. 134
Fig.8.6 Cooperativeroad-search;missionperformance. 135
Fig.8.7 Cooperativeroad-search;coordinatedvision-basedtargettracking. 136
Fig.8.8 Time-criticalcooperationinaroad-searchmission. 137
Fig.8.9 High-resolutionimageexploitation. 138
Fig.9.1 Geometryofthepath-followingproblem. 147
Fig.9.2 Shapingtheapproachtothepath. 147
Fig.9.3 Followingavirtualtarget;simulationscenario. 150
Fig.9.4 Followingavirtualtarget;missionexecution. 151
Fig.9.5 Followingavirtualtarget;path-followingerrorsandcommands. 152
Fig.9.6 Followingavirtualtarget;simulationscenariowithinner-loopautopilot. 153
Fig.9.7 Followingavirtualtarget;missionexecution. 154
ListofFigures xiii
Fig.9.8 Followingavirtualtarget;path-followingerrorsandcommands. 155
Fig.9.9 DronedrawingaBéziercurve. 156
Fig.10.1 Examplesofcoordinatedmaneuvers. 160
Fig.10.2 Coordinatedexchangeofpositions;simulationresults. 164
Fig.10.3 Coordinatedexchangeofpositionswithidealcommunications; time
coordination. 165
Fig.10.4 Coordinatedexchangeofpositionswithrange-basedcommunications;
estimateofthequalityofservice(QoS). 166
Fig.10.5 Coordinatedexchangeofpositionswithrange-basedcommunications;time
coordination. 167
Fig.10.6 Coordinatedexchangeofpositionswithrange-basedcommunicationsand
inner-loopautopilots;timecoordination. 168
Fig.10.7 Effectofnetworkconnectivityontime-coordination;normofthecoordination
errorvector. 168
Fig.10.8 Effectofpath-followingcorrectiontermsα¯i(·)oninter-vehicleseparation. 169
Fig.11.1 IndoorfacilityattheNavalPostgraduateSchool. 172
Fig.11.2 Parrot’sAR.Drone[2];quadrotoremployedintheflight-testexperiments. 172
Fig.11.3 Phaseonorbitcoordination;desiredandactualvehiclepathsin3D. 174
Fig.11.4 Phaseonorbitcoordination;missionexecution. 174
Fig.11.5 Phaseonorbitcoordination;phaseshift. 175
Fig.11.6 Phaseonorbitcoordination;coordinationerrorandcoordination-staterates. 175
Fig.11.7 Spatialcoordinationalongoneaxis;desiredandactualvehiclepathsin3D. 175
Fig.11.8 Spatialcoordinationalongoneaxis;missionexecution. 176
Fig.11.9 Spatialcoordinationalongoneaxis;speedprofiles. 176
Fig.11.10Spatialcoordinationalongoneaxis;coordinationerrorandcoordination-state
rates. 176
Foreword
We can see on the horizon a new age of autonomous systems in which the opportu-
nitiesforapplicationsareendless:self-drivingcarsandtrucks,autonomouspackage
and cargo delivery systems, police and military operations, earth exploration, criti-
calinfrastructureinspectionandmaintenance,andmedicaldiagnosticsandoperating
roomrobotics,tonameafew.Autonomoussystemswithmachinereasoningandintel-
ligencewillforeverchangehowmachinesservehumans.Weareatthethresholdofan
era when humans and machines understanding mission context, sharing understand-
ingandsituationawareness,andadaptingtotheneedsandcapabilitiesofeachother
withadvancedcommunicationsystems,controlalgorithms,andsensornetworkswill
execute challenging scientific and commercial missions with unprecedented safety,
reliability,andperformance.
Control theory brings together advanced mathematics, modeling and simulation,
andsoftwaredevelopment,andformsthekernelfortheoperatingsystemsembedded
in autonomous systems. Control of multiple autonomous air vehicles is one of the
mostchallengingcontrolproblemsduetothenatureofflight.Eachairvehiclecontrol
andmissionmanagementsystemconsistsofnestedcontrolloopswhichmustprovide
inner-loopstability,commandfollowing,andouter-looptrajectorycontroltoaccom-
plishdemandingtasks,aswellashavetheabilitytoreject/compensateforsignificant
environmental disturbances. The resulting architecture must manage tasks, generate
trajectories, enable vehicles to follow the corresponding paths, perform under fail-
ures,damage,orwhenthingsgowrong,andcommunicatecriticalinformationtoall
systemsthatneedit.
Thisbookrepresentsawelcomecontributiontotheliteratureonthedevelopment
of autonomous systems. It addresses key topics that are critical to the control of au-
tonomous air vehicles and provides a rigorous mathematical formulation for control
systems design and analysis. Clearly, having multiple air vehicles cooperate to ex-
ecute complex missions requires the ability to follow desired paths and complete
tasks at prescribed times, and to be able to do this under limited/constrained com-
munications networks. The authors Isaac Kaminer, António Pascoal, Enric Xargay,
Naira Hovakimyan, Venanzio Cichella, and Vladimir Dobrokhodov bring together a
wealth of experience and theoretical rigor applied to the time-critical control of au-
tonomousfixed-wingandmultirotorairvehicles.Thefirstsectionofthisbookgivesan
overviewofageneralframeworkleadingtoanarchitecturefortrajectorygeneration,
path following, multi-vehicle coordination and communications, and inner-loop au-
topilotcontrol.Thekeyideaexploitedisthedecouplingofspaceandtimethatallows
usingthevelocitiesofthevehiclesforcoordination,whilecircumventingtheperfor-
mancelimitationsoftrajectorytrackingcontrollers.Thisdecouplingmakesitpossible
toaccountforheterogeneousvehicledynamics.Thesecondandthirdsectionsusethe
framework proposed to address time-critical control of autonomous fixed-wing and