Table Of ContentHaibin Duan • Pei Li
Bio-inspired Computation in
Unmanned Aerial Vehicles
123
HaibinDuan PeiLi
BeihangUniversity(formerlyBeijing BeihangUniversity(BUAA)
UniversityofAeronautics Beijing
andAstronautics,BUAA) China,People’sRepublic
Beijing
China,People’sRepublic
ISBN978-3-642-41195-3 ISBN978-3-642-41196-0(eBook)
DOI10.1007/978-3-642-41196-0
SpringerHeidelbergNewYorkDordrechtLondon
©Springer-VerlagBerlinHeidelberg2014,correctedpublication2018
Preface
Bio-inspired computation, short for biologically inspired computation, is the use
of computers to model the living phenomena, and simultaneously the study of
life to improve the usage of computers, which has attracted a lot of researchers’
attention. A variety of bio-inspired computation models have been proposed and
applied to solve many real-world problems successfully, such as ant colony opti-
mization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC)
and differential evolution (DE). Although rigorous theoretical analysis for most
of the bio-inspired computation methods has not been conducted systematically,
and the current study in this field is still in the experimental and preliminary
application stage, the bio-inspired computation methods have already found their
applications in many typical fields. Some of the phenomena are also known as
swarmintelligence,inspiredbythesocialbehaviourofgregariousinsectsandother
animals. The emergent behaviour of multiple unsophisticated agents interacting
among themselves and with their environment leads to a functional strategy that
is useful to achieve complicated goals in an efficient manner. There exist a
number of desirable properties in this kind of model, which include feedback,
self-organization,adaptationtochangingenvironments,andmultipledecentralized
interactions among agents to work collaboratively as a group in completing
complextasks.
Unmanned aerial vehicle (UAV), colloquially known as a drone, is an aircraft
without a human pilot on board. Its flight is controlled either autonomously by
computers in the vehicle or under the remote control of a pilot on the ground or
in another vehicle. UAV offers advantages for many applications compared with
their manned counterparts. They preserve human pilots of flying in dangerous
conditions that can be encountered not only in military applications but also
in other scenarios involving operation in bad weather conditions, or near to
buildings,trees,civilinfrastructuresandotherobstacles.Whilerecenttechnological
advanceshaveenabledthedevelopmentofunmannedvehicularsystemsandrecent
implementations have proven the UAV’s benefits in both military and civilian
applications, the full benefit of unmanned systems will be utilized when they
can operate autonomously. Typical application domains of UAVs include recon-
naissance and surveillance missions in an urban environment, target tracking and
evasivemanoeuvres,searchandrescueoperations,homelandsecurity,etc.Inrecent
years a significant shift of focus has occurred in the field of autonomous UAVs
as researchers have begun to investigate problems involving multiple rather than
single UAV. Systems consisting of multiple UAVs performing complex missions
presentnewchallengestothecontrolcommunity.UAVsmustpossessattributesof
autonomy in order to function effectively in a ‘system of systems’ configuration.
Coordinated and collaborative control of UAV swarms demands new and novel
technologies that integrate modelling, control, communications and computing
concernsintoasinglearchitecture.
From the computational point of view, bio-inspired computation models are
largely stochastic search algorithms. They are useful for undertaking distributed
and multimodal optimization problems. The search process is robust and efficient
in maintaining diversity. A mechanism to impose a form of forgetting is also
adopted in some swarm intelligence algorithms such that the solution space can
be explored in a comprehensive manner. Thus, the algorithms are able to avoid
convergence to a locally optimal solution and, at the same time, arrive at a global
optimizedsolutionwithahighprobability.Wecanlearnmorethantheoptimization
algorithmsfromthebio-inspiredalgorithms.Theinteractionamongtheagentsand
feedbackmechanismarethebasicelementsthatresultintheemergenceofdynamic
patternsatthecolonylevel.Themostinterestingpropertiesoftheseself-organized
patterns are robustness (the ability for a system to perform without failure under
a wide range of conditions) and flexibility (the ability for a system to readily
adapt to new, different or changing requirements). Adaptation must happen fast
enough for UAVs to provide benefits in case of environmental change, and the
autonomy should be constructed so that these lessons can be shared with other
autonomous systems that have not yet encountered that situation. Yet, even in a
hostile, dynamic, unstructured and uncertain environment, this learning must not
adversely affect safety, reliability or the ability to collaborate with the operator or
other autonomous systems. Although such capabilities are not currently available,
the emergence mechanism of robustness and flexibility in biological colony may
provideusmanyentirelynewthreads.
This monograph, divided into eight chapters, mainly includes our recent work
relevant to UAV control issues in which we have taken advantage of bio-inspired
computation, such as path planning and replanning for single UAV and multi-
ple UAVs, formation flight control and formation configuration, heterogeneous
cooperative control for multiple UAVs/unmanned ground vehicles (UGVs) and
vision-based surveillance and navigation problems. Chapter 1 discusses the devel-
opmentofUAVandemphasizestherolebio-inspiredintelligenceplaysinachieving
higherautonomouscapability.Chapter2introducesfourrepresentativebio-inspired
algorithms, which are ACO, PSO, ABC and DE. We explain the biological
inspiration, principle and implementation procedures of the algorithms in detail.
Then in Chapter 3 we deal with the modelling problem of UAVs and give a brief
introductionofthecontrollerdesignmethod.Chapter4dealswiththepathplanning
problem using bio-inspired algorithms, both for single UAV and multiple UAVs,
bothinthetwo-dimensionalscenarioandthree-dimensionalscenarios.Thischapter
mainlycontainsthreesections.First,achaoticABCapproachisproposedfortwo-
dimensionalpathplanning.Thenpathplanningisextendedtoathree-dimensional
scenario through an improved ACO added a path smoothing strategy. Section 4.5
deals with coordinated path re-planning for multiple UAVs using the method of
Max-MinadaptiveACO.InChap.5,wemainlydealwiththreesignificantaspects
of formation control: formation control, close formation and formation configu-
ration. Chapter 6 discusses multiple UAVs/UGVs heterogeneous cooperation and
cooperative search of multiple UAVs based on differential evolution. Chapter 7
describes bio-inspired computation algorithms involving vision-based surveillance
andnavigation.Chapter8discussestheopportunitiesforthedevelopmentofUAVs
andpointsoutthepotentialchallengesforachievinghigherautonomouscapability.
Byincorporatingthebio-inspiredintelligenceintoUAVs,itispossibletoenhance
theirabilitytounderstandandadapttotheenvironmentandtheabilitytocooperate
withotherautonomoussystems.
Specialthanksareduetoseveralmembersofourresearchteam-ANTResearch
Group: Guanjun MA, Senqi LIU, Hao LI, Yaxiang YU, Xiangyin ZHANG,
ChunfangXU,ChanghaoSUN,YunpengZHANG,FangLIU,YiminDENG,Qinan
LUO, Yingcai BI, Shuangtian LI, Qifu ZHANG, Olukunle Kolawole SOYINKA,
JiaqianYU,XiaohuaWANG,JunnanLI,WeirenZHU,LuGAN,ZenghuZHANG,
YanXU,HuaxinQIU,CongZHANG,FeiYE,CongLIandZiweiZHOUfortheir
diligentworkandcontributionsintherelatedfields.Theauthorswouldalsoliketo
extend their thanks and appreciations to Ms. Li SHEN, the physical sciences and
engineeringeditorofSpringer,andtheeditorialassistant,Ms.JessieGUO,fortheir
kindhelpandassistanceandtoSpringer’scopyeditorsfortheirreadingofthisentire
manuscriptandtheirinstructivecomments.
We have had the benefit of the collaboration of coworkers and discussions
with international partners, from whom we have learned a great deal. Among
them are Prof. Zongji CHEN, Prof. Bo Hu LI, and Prof. Shiyin QIN of Beihang
University(BUAA); Prof. Ben M. CHEN and Prof. Kay Chen TAN of National
University of Singapore; Prof. Qinping ZHAO of State Key Laboratory of Virtual
RealityTechnologyandSystemsofChina;Prof.XinguiHEofPekingUniversity;
Prof. Ming LI and Prof. Yanming FAN of Shenyang Aircraft Design Research
Institute; Prof. Derong LIU of Institute of Automation of CAS; Prof. Marco
DORIGO of Universite’ Libre de Bruxelles; Prof. Yuhui SHI of Xi’an Jiaotong-
Liverpool University; Prof. Yaochu JIN and Prof. Yang GAO of University of
Surrey; Prof. Ling WANG of Tsinghua University; Prof. Licheng JIAO and Prof.
Maoguo GONG of Xidian University; Prof. Bin XIAN of Tianjin University;
Prof.Sung-KwunOHofTheUniversityofSuwon;Prof.DaoboWANGandProf.
HuajunGONGofNanjingUniversityofAeronauticsandAstronautics;Prof.Wen-
Hua CHEN of Loughborough University; Prof. Youmin ZHANG of Concordia
University; Prof. Wei REN of University of California, Riverside; and Prof. Delin
LUOofXiamenUniversity.Weareindebtedtothemfortheirkindhelpandvaluable
comments.
The related work reported in this monograph was partially supported by the
NaturalScienceFoundationofChina(NSFC)undergrants#61273054,#60975072
and#60604009;NationalNaturalScienceFoundationofChina(NSFC)StateKey
Program under grant #61333004; National Key Basic Research Program of China
(973Program)undergrants#2014CB046401and#2013CB035503;NationalHigh
Technology Research and Development Program of China (863 Program) under
grant#2011AA040902;NationalMagneticConfinementFusionResearchProgram
ofChinaundergrant#2012GB102006,ProgramforNewCenturyExcellentTalents
in University of China under grant #NCET-10-0021; Top-Notch Young Talents
ProgramofChina,BeijingNOVAProgram,undergrant#2007A017;Fundamental
Research Funds for the Central Universities of China, Aeronautical Foundation
of China, under grants #20115151019, 2008ZC01006 and #2006ZC51039; Open
FundoftheStateKeyLaboratoryofVirtualRealityTechnologyandSystemsunder
grants #VR-2013-ZZ-02 and #VR-2011-ZZ-01; Open Fund of the Provincial Key
Laboratory for Information Processing Technology of Suzhou University under
grants#KJS1020and#KJS0821.
The main objectives pursued have been on addressing the question of how
to achieve higher autonomous capability by taking advantages of bio-inspired
computation. This monograph is intended for researchers, college students and
industrial practitioners who may wish to get an insight into the complex nature of
andpracticalsolutionstobio-inspiredcomputationinUAVissues.Wehopethatit
helps to promote further research and practice in this promising field. Finally, we
also hope that readers enjoy reading this monograph, and, most importantly, that
theylearnsomethingnewbylookingatthingsfromanewperspective.
BeihangUniversity(BUAA) HaibinDuan
Beijing,People’sRepublicofChina PeiLi
Contents
1 Introduction .................................................................. 1
1.1 UnmannedAerialVehicle(UAV) ...................................... 1
1.1.1 HistoryofUAVs ................................................ 2
1.1.2 UnmannedAircraftSystem .................................... 5
1.1.3 Autonomy:AKeyEnabler..................................... 6
1.2 Bio-inspiredComputation.............................................. 11
1.2.1 DefinitionofSwarm............................................ 11
1.2.2 GeneralFeaturesofBio-inspiredComputation ............... 13
1.2.3 Bio-inspiredComputationAlgorithms ........................ 16
1.3 Bio-inspiredIntelligenceinUAVs ..................................... 23
1.3.1 AchieveHigherAutonomousCapability...................... 23
1.3.2 EnhanceRobustnessandFlexibility........................... 24
1.3.3 CooperativeControlofMultipleUAVs........................ 26
1.3.4 CooperativeControlofHeterogeneousVehicleGroups...... 28
1.4 OutlineoftheMonograph.............................................. 30
References..................................................................... 32
2 Bio-inspiredComputationAlgorithms .................................... 35
2.1 Introduction ............................................................. 35
2.2 AntColonyOptimization............................................... 38
2.2.1 BiologicalInspiration........................................... 39
2.2.2 PrincipleofAntColonyOptimization......................... 41
2.2.3 AntSystemandItsExtensions................................. 43
2.3 ParticleSwarmOptimization........................................... 45
2.3.1 BiologicalInspiration........................................... 45
2.3.2 PrincipleofParticleSwarmOptimization..................... 47
2.3.3 ParametersandPopulationTopology.......................... 50
2.4 ArtificialBeeColony ................................................... 53
2.4.1 BiologicalInspiration........................................... 53
2.4.2 PrincipleofArtificialBeeColony ............................. 54
2.4.3 AlgorithmicStructureofArtificialBeeColony............... 57
2.5 DifferentialEvolution................................................... 60
2.5.1 BiologicalInspiration........................................... 60
2.5.2 PrincipleofDifferentialEvolution............................. 61
2.5.3 ControlParametersofDifferentialEvolution ................. 64
2.6 OtherAlgorithms........................................................ 65
2.6.1 GlowwormSwarmOptimization .............................. 65
2.6.2 BacteriaForagingOptimization................................ 65
2.6.3 Bat-InspiredAlgorithm......................................... 66
2.7 Conclusions ............................................................. 67
References..................................................................... 67
3 UAVModelingandControllerDesign..................................... 71
3.1 Introduction ............................................................. 71
3.2 Parameter Identification for UAVs Based
onPredator–PreyPSO.................................................. 72
3.2.1 MathematicalModelofUAVs ................................. 72
3.2.2 Predator–PreyPSOforParameterIdentification.............. 76
3.2.3 Experiments..................................................... 80
3.3 PSOOptimizedControllerforUnmannedRotorcraftPendulum..... 85
3.3.1 MathematicalModelofPendulumOscillationforMAVs.... 85
3.3.2 OscillationControllerDesignBasedonLQRandPSO ...... 90
3.3.3 Experiments..................................................... 94
3.4 Conclusions ............................................................. 96
References..................................................................... 96
4 UAVPathPlanning .......................................................... 99
4.1 Introduction ............................................................. 99
4.1.1 CharacteristicofPathPlanningforUAVs..................... 100
4.1.2 MainFeaturesofPathReplanningforMultipleUAVs ....... 101
4.2 ModelingforPathPlanning ............................................ 102
4.2.1 EnvironmentRepresentation................................... 102
4.2.2 EvaluationFunction ............................................ 104
4.3 ChaoticABCApproachtoUAVPathPlanning ....................... 105
4.3.1 BriefIntroductiontoChaosTheory............................ 105
4.3.2 ProceduresofPathPlanningUsingChaotic
ABCApproach.................................................. 106
4.3.3 Experiments..................................................... 108
4.4 HybridACO-DEApproachtoThree-DimensionalPath
PlanningforUAVs...................................................... 110
4.4.1 HybridMeta-heuristicACO-DEAlgorithm................... 110
4.4.2 ProceduresofThree-DimensionalPathPlanning
UsingHybridACO-DE......................................... 114
4.4.3 Path-SmoothingStrategies ..................................... 116
4.4.4 Experiments..................................................... 118
4.5 CoordinatedPathReplanningforMultipleUAVsUsing
Max–MinAdaptiveACO............................................... 120
4.5.1 ModelofMultipleUAVCoordinatedPathReplanning....... 120
4.5.2 CoordinationMechanismofMultipleUAV
PathReplanning ................................................ 125
4.5.3 ProceduresofMultipleUAVCoordinatedPathReplanning.. 127
4.5.4 Experiments..................................................... 133
4.6 Conclusions ............................................................. 136
References..................................................................... 141
5 MultipleUAVFormationControl.......................................... 143
5.1 Introduction ............................................................. 143
5.1.1 FormationControl.............................................. 144
5.1.2 CloseFormation ................................................ 144
5.1.3 FormationConfiguration ....................................... 145
5.2 Dual-ModeRHCforMultipleUAVFormationFlight
BasedonChaoticPSO.................................................. 146
5.2.1 Leader-FollowingFormationModel........................... 146
5.2.2 PrincipleofRHC ............................................... 147
5.2.3 ChaoticPSO-BasedDual-ModeRHCFormation
ControllerDesign............................................... 149
5.2.4 Experiments..................................................... 154
5.3 DE-BasedRHCControllerforMultipleUAVCloseFormation...... 161
5.3.1 ModelofMultipleUAVsforCloseFormation................ 161
5.3.2 DescriptionofRHC-BasedMultipleUAV
CloseFormation ................................................ 162
5.3.3 DE-BasedRHCControllerDesignforCloseFormation ..... 163
5.3.4 Experiments..................................................... 167
5.4 DE-BasedRHCControllerforMultipleUAVFormation
Reconfiguration ......................................................... 167
5.4.1 ModelofMultipleUAVsforFormationConfiguration....... 167
5.4.2 Description of RHC-Based Multiple UAV
FormationReconfiguration..................................... 171
5.4.3 DE-BasedRHCControllerDesignforFormation
Reconfiguration................................................. 172
5.4.4 Experiments..................................................... 174
5.5 Conclusions ............................................................. 176
References..................................................................... 181
6 MultipleUAV/UGVHeterogeneousControl.............................. 183
6.1 Introduction ............................................................. 183
6.2 MultipleUAV/UGVHeterogeneousCoordinatedControl............ 185
6.2.1 MathematicalModelforUAVsandUGVs.................... 185
6.2.2 MultipleUGVCoordinatedControlBasedonRHC.......... 186
6.2.3 Multiple UAV Coordinated Control Based
onVelocityVectorControl..................................... 190
6.2.4 MultipleUAV/UGVHeterogeneousCooperation............. 193
6.2.5 Time-DelayCompensationofHeterogeneous
NetworkControl................................................ 195
6.3 DE-BasedRHCforMultipleUAVCooperativeSearch............... 202
6.3.1 ModelDescriptionforCooperativeSearch.................... 204
6.3.2 DE-BasedRHCforCooperativeAreaSearch................. 208
6.3.3 Experiments..................................................... 210
6.4 Conclusions ............................................................. 211
References..................................................................... 213
7 BiologicalVision-BasedSurveillanceandNavigation.................... 215
7.1 Introduction ............................................................. 215
7.2 ABC Optimized Edge Potential Function Approach
toTargetIdentification.................................................. 217
7.2.1 ThePrincipleofEdgePotentialFunction ..................... 217
7.2.2 ABCOptimizedEPFApproachtoTargetIdentification...... 218
7.2.3 Experiments..................................................... 221
7.3 AChaoticQuantum-BehavedPSOBasedonLateral
InhibitionforImageMatching ......................................... 221
7.3.1 TheQuantum-BehavedPSOAlgorithm....................... 224
7.3.2 LateralInhibitionMechanism.................................. 229
7.3.3 ChaoticQuantum-BehavedPSOBased
onLateralInhibition............................................ 231
7.3.4 Experiments..................................................... 232
7.4 Implementation of Autonomous Visual Tracking
andLandingforLow-CostQuadrotor ................................. 236
7.4.1 TheQuadrotorandCarrierTestBed........................... 236
7.4.2 ComputerVisionAlgorithm.................................... 238
7.4.3 ControlArchitectureforTrackingandLanding............... 239
7.4.4 Experiments..................................................... 241
7.5 Conclusions ............................................................. 244
References..................................................................... 245
8 ConclusionsandOutlook ................................................... 247
8.1 Conclusions ............................................................. 247
8.2 NewTrendsinUAVDevelopment..................................... 248
8.2.1 SmallAirVehicles.............................................. 248
8.2.2 Air-BreathingHypersonicVehicles............................ 250
8.2.3 DesignfromthePerspectiveofSystemIntegration........... 251
8.3 FurtherExtensionsofBio-inspiredIntelligenceinUAVs............. 253
8.3.1 AchieveHigherAutonomousCapability...................... 253
