Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles using Multi-Objective ...

181 Pages·2012·0.82 MB·English

Abstract
Barlow,GregoryJohn. DesignofAutonomous Navigation Controllers forUnmannedAerial
VehiclesUsingMulti-objective GeneticProgramming. (underthedirectionofEdwardGrant.)
Unmanned aerial vehicles (UAVs) have become increasingly popular for many applications,
includingsearchandrescue,surveillance,andelectronicwarfare,butalmostallUAVsarecon-
trolled remotely by humans. Methods of control must be developed before UAVs can become
trulyautonomous. Whilethe(cid:2)eldofevolutionaryrobotics(ER)hasmadestridesinusingevo-
lutionary computation (EC) to develop controllers for wheeled mobile robots, little attention
has been paid to applying EC to UAV control. EC is an attractive method for developing UAV
controllers because it allows the human designer to specify the set of high level goals that are
to be solved by arti(cid:2)cial evolution. In this research, autonomous navigation controllers were
developed using multi-objective genetic programming (GP) for (cid:2)xed wing UAV applications.
Four behavioral (cid:2)tness functions were derived from (cid:3)ight simulations. Multi-objective GP
used these (cid:2)tness functions to evolve controllers that were able to locate an electromagnetic
energy source, to navigate the UAV to that source ef(cid:2)ciently using on-board sensor measure-
ments, and to circle around the emitter. Controllers were evolved in simulation. To narrow the
gap between simulated and real controllers, the simulation environment employed noisy radar
signals and a sensor model with realistic inaccuracies. All computations were performed on a
92-processor Beowulf cluster parallel computer. To gauge the success of evolution, baseline
(cid:2)tness values for a successful controller were established by selecting values for a minimally
successful controller. Two sets of experiments were performed, the (cid:2)rst evolving controllers
directly from random initial populations, the second using incremental evolution. In each set
of experiments, autonomous navigation controllers were evolved for a variety of radar types.
Boththedirectevolutionandincrementalevolutionexperimentswereabletoevolvecontrollers
thatperformedacceptably. However,incrementalevolutionvastlyincreasedthesuccessrateof
incrementalevolutionoverdirectevolution. The(cid:2)nalincrementalevolutionexperimentonthe
mostcomplexradarinvestigatedinthisresearchevolvedcontrollersthatwereabletohandleall
oftheradartypes. EvolvedUAVcontrollersweresuccessfullytransferredtoawheeledmobile
robot. An acoustic array on-board the mobile robot replaced the radar sensor, and a speaker
emitting a tone was used as the target. Using the evolved navigation controllers, the mobile
robot moved to the speaker and circled around it. Future research will include testing the best
evolvedcontrollersbyusingthemto(cid:3)yrealUAVs.
DESIGN OF AUTONOMOUS NAVIGATION CONTROLLERS
FOR UNMANNED AERIAL VEHICLES USING
MULTI-OBJECTIVE GENETIC PROGRAMMING
BY
GREGORY J. BARLOW
A THESIS SUBMITTED TO THE GRADUATE FACULTY OF
NORTH CAROLINA STATE UNIVERSITY
IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
ELECTRICALANDCOMPUTERENGINEERING
RALEIGH
MARCH 2004
APPROVED BY:
Dedicated to
myparents,JohnBlackBarlow,Jr. andCherylStevensBarlow.
ii
Biography
Gregory John Barlow was born June 14, 1980 in Greensboro, North Carolina to John Black
Barlow, Jr. and Cheryl Stevens Barlow. He graduated from the North Carolina School of Sci-
enceandMathematics,Durham,NorthCarolinainMay1999. HereceivedBachelorofScience
degrees in Electrical Engineering and Computer Engineering from North Carolina State Uni-
versity, Raleigh, North Carolina in May 2003. He received the Master of Science Degree in
Electrical Engineering from North Carolina State University, Raleigh, North Carolina in May
2004.
Asanundergraduate,GregorywasaBarryM.GoldwaterScholar,aJohnT.CaldwellScholar,
a Caldwell Fellow, and a University Scholar. He received a North Carolina State University
UndergraduateResearchAwardforthespringof2001,aNationalScienceFoundationSummer
Undergraduate Fellowship in Sensor Technologies at the University of Pennsylvania for the
summerof2001,andwasawinnerofthe2001NorthCarolinaStateUniversityUndergraduate
ResearchSymposium andthe2001SigmaXiStudentResearchSymposium.
Gregory is a member of Eta Kappa Nu Electrical and Computer Engineering Honor Society,
Tau Beta Pi Engineering Honor Society, the Honor Society of Phi Kappa Phi, the Institute of
Electrical and Electronics Engineers, and the International Society for Genetic and Evolution-
aryComputation.
iii
Acknowledgments
I would like to thank the members of my committee, Dr. Edward Grant, Dr. Choong K. Oh,
Dr. Mark W. White, and Dr. H. Troy Nagle. I would like to thank Dr. Edward Grant for all of
his support and encouragement. In all the years I’ve been privileged to work with him, he has
givenmesomanyopportunitiestolearnandgrow. Dr. Granthashelpedmetobecomeabetter
researcher and a better person. I would also like to especially thank Dr. Choong Oh for giving
metheopportunity tobecomeinvolved inthisresearch. Dr. Ohhasbeenawonderful mentor.
Iwouldliketoacknowledgethe(cid:2)nancialsupportofthisworkprovidedbytheOf(cid:2)ceofNaval
Research(ONR)throughtheUnitedStatesNavalResearchLaboratory(NRL)underDr. Mari-
bel Soto (ONR) and Dr. Choong Oh (NRL). Computer time on the 92 processor Beowulf
clusterwasfurnishedbyNRL(Code5732).
I would like to thank all the members of the Center of Robotics and Intelligent Machines
(CRIM),pastandpresent,fortheircollaborationandsupport. Ihavespentsixwonderfulyears
intheCRIMandhavehadthechancetoworkwithmanygreatpeople. Iwouldespeciallylike
tothankAndrewNelson,JohnGaleotti,LeonardoMattos,andMarcEdwards.
Most of all, I would like to thank my parents, John and Cheryl Barlow. Their persistent and
wholehearted commitment to my education and growth as a person have made me what I am
today. I am forever grateful. I would also like to thank my three sisters, Logan, Lindsey, and
Gwendolyn,LiddyGerchman,andallofmyfamilyandfriends.
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Contents
ListofFigures ix
ListofTables xiii
ListofAbbreviations xv
1 Introduction 1
1.1 MotivationandResearchGoals . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 OverviewofThesisChapters . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 LiteratureReview 4
2.1 Evolutionary Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 History ofEvolutionary RoboticsResearch . . . . . . . . . . . . . . . 6
2.1.2 Evolutionary RoboticsController Architectures . . . . . . . . . . . . . 8
2.1.3 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.4 RobotTypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 GeneticProgramming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.1 Evolutionary Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 ConcernsandStrategies . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 PerformanceEvaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
v
2.3.1 FunctionalFitnessFunctions . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.2 Aggregate FitnessFunctions . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.3 Competitive FitnessEvaluation . . . . . . . . . . . . . . . . . . . . . 18
2.3.4 Multi-objective Optimization . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.5 Incremental Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3 UnmannedAerialVehicleControl 25
3.1 SimulationEnvironment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2 UnmannedAerialVehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2.1 Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.3 Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2.4 Controller Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2.5 Simulation FlightModel . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3 Radar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.1 RadarTypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.2 RadarModeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4 Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.5 ProblemDif(cid:2)culty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.6 TransferencetorealUAVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4 EvolutionandFitnessEvaluation 40
4.1 Multi-objective GeneticProgramming . . . . . . . . . . . . . . . . . . . . . . 42
4.1.1 GeneticProgramming Parameters . . . . . . . . . . . . . . . . . . . . 42
4.1.2 FunctionsandTerminals . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.1.3 ParallelEvaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
vi
4.2 FitnessFunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2.1 Normalized distance . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2.2 Circling distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.2.3 Leveltime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2.4 Turncost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2.5 Combining theFitnessMeasures . . . . . . . . . . . . . . . . . . . . . 55
4.3 IncrementalEvolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.3.1 FunctionalIncrementalEvolution . . . . . . . . . . . . . . . . . . . . 57
4.3.2 Environmental IncrementalEvolution . . . . . . . . . . . . . . . . . . 58
5 ExperimentsandResults 60
5.1 EffectivenessofFitnessFunctions . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 MetricsforPost-evolution Controller Evaluation . . . . . . . . . . . . . . . . . 63
5.3 DirectEvolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.3.1 Continuously Emitting, StationaryRadar . . . . . . . . . . . . . . . . 65
5.3.2 Intermittently Emitting, StationaryRadarwithRegularPeriod . . . . . 70
5.3.3 Intermittently Emitting, StationaryRadarwithIrregular Period . . . . . 78
5.3.4 Continuously Emitting, MobileRadar . . . . . . . . . . . . . . . . . . 86
5.3.5 Intermittently Emitting, MobileRadarwithRegularPeriod . . . . . . . 94
5.4 IncrementalEvolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.4.1 SeedPopulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.4.2 Intermittently Emitting, StationaryRadar . . . . . . . . . . . . . . . . 103
5.4.3 Continuously Emitting, MobileRadar . . . . . . . . . . . . . . . . . . 106
5.4.4 Intermittently Emitting, StationaryRadarwithMultipleIncrements . . 108
5.4.5 Intermittently Emitting, MobileRadarwithMultipleIncrements . . . . 111
5.4.6 AnalysisofIncrementally EvolvedControllers . . . . . . . . . . . . . 112
5.5 TransferencetoaWheeledMobileRobot . . . . . . . . . . . . . . . . . . . . 116
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6 ConclusionandFutureResearch 125
6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
6.2 FutureResearch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
7 References 129
Appendices 135
A ExperimentalResults 136
A.1 DirectEvolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
A.1.1 Continuously Emitting, StationaryRadar . . . . . . . . . . . . . . . . 136
A.1.2 Intermittently Emitting, StationaryRadarwithRegularPeriod . . . . . 137
A.1.3 Intermittently Emitting, StationaryRadarwithIrregular Period . . . . . 138
A.1.4 Continuously Emitting, MobileRadar . . . . . . . . . . . . . . . . . . 139
A.1.5 Intermittently Emitting, MobileRadarwithRegularPeriod . . . . . . . 140
A.2 IncrementalEvolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
A.2.1 SeedPopulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
A.2.2 Intermittently Emitting, StationaryRadar . . . . . . . . . . . . . . . . 142
A.2.3 Continuously Emitting, MobileRadar . . . . . . . . . . . . . . . . . . 143
A.2.4 Intermittently Emitting, StationaryRadarwithMultipleIncrements . . 144
A.2.5 Intermittently Emitting, MobileRadarwithMultipleIncrements . . . . 145
B SampleResultsfromEvolutionaryRuns 147
B.1 Continuously Emitting,StationaryRadar . . . . . . . . . . . . . . . . . . . . . 147
B.2 Intermittently Emitting,MobileRadar . . . . . . . . . . . . . . . . . . . . . . 156
viii