Robot Intelligence: An Advanced Knowledge Processing Approach

298 Pages·2010·19.986 MB·English

by  Alexander Skoglund, Boyko Iliev, Rainer Palm (auth.), Honghai Liu, Dongbing Gu, Robert J. Howlett, Yonghuai Liu (eds.)

Advanced Information and Knowledge Processing
SeriesEditors
ProfessorLakhmiJain
[e-mail]
ProfessorXindongWu
[e-mail]
Forothertitlespublishedinthisseries,goto
www.springer.com/series/4738
Honghai Liu (cid:2) Dongbing Gu (cid:2) Robert J. Howlett (cid:2)
Yonghuai Liu
Editors
Robot Intelligence
An Advanced Knowledge Processing Approach
Editors
Dr.HonghaiLiu Dr.RobertJ.Howlett
UniversityofPortsmouth UniversityBrighton
InstituteofIndustrialResearch SchoolofEngineering
PO13QLPortsmouth IntelligentSignalProcessing
UK Laboratories(ISP)
[e-mail] Moulsecoomb
BN24GJBrighton
Prof.Dr.DongbingGu UK
UniversityofEssex [e-mail]
DepartmentofComputerSc
WivenhoePark Prof.Dr.YonghuaiLiu
CO43SQColchester AberystwythUniversity
UK DepartmentofComputerScience
[e-mail] Ceredigion
SY233DBAberystwyth
UK
AI&KPISSN1610-3947
ISBN978-1-84996-328-2 e-ISBN978-1-84996-329-9
DOI10.1007/978-1-84996-329-9
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Preface
Withthegrowingintegrationofmachinelearningtechniquesintoroboticsresearch,
there is a need to address this trend in the context of robot intelligence. The mul-
tidisciplinarynature of robot intelligenceprovides a realistic platform for robotics
researchers to apply machine learning techniques. One of the principal purposes
ofthisbookistopromoteideaexchangesandinteractionsbetweendifferentcom-
munities,whicharebeneficialandbringingfruitfulsolutions.Especiallywhenthe
tasks robots are programmed to achieve become more and more complex, impre-
ciseperceptionoftheenvironmentsrendersadifficultdeliberativecontrolstrategy
appliedforrobotsforsomanyyears.Understandingtheenvironmentwhererobots
operateandthencontrollingrobotsgraduallyrelyonmachinelearningtechniques.
Itismorelikelytobetteroffwithembeddingcontrolproblemsintotheenvironment
perception.
The major challenges for programming autonomous robots stem mainly from
firstlythedynamicenvironmentinwhichitisunabletopredictwheneventswilloc-
curandtherobotshavetoperceivetheirenvironmentrepeatedly,secondlyuncertain
sensory information that is inaccurate, noisy, or faulty, thirdly imperfect actuators
that cannot guarantee perfect execution of actions due to mechanical, electrical,
and servo problems, and finally limited time that constrains time intervals needed
forsensorinformationprocessing,actuatorcontrol,andgoal-orientedplanning.As
such,therobotscannotrelyontheiractionstopredictmotionresults.Heavycom-
putation would make the robots move and respond slowly to changes in the envi-
ronment.
For autonomous mobile robots, early programming approaches followed a se-
quence: sensing the environment, planning trajectories, and controlling motors to
move.Withthiskindofcontrolstrategies,therobotneedsto“think”hard,consum-
ing large amounts of time to model the environmentand reason about what to do.
In addition, modelling and reasoning methods vary with robot tasks and have not
reacheda widelyacceptedlevelof development.Furthermore, this typeof control
strategiesisveryfragile,asitcanfailtodealwithunpredictableeventsindynamic
environmentseveniftherobotcanmodelandreasonprecisely.Meanwhile,itisim-
possibleto predictall the potentialsituationsrobots may encounterand to specify
all the robot behaviors optimally in advance when programming them to achieve
v
vi Preface
complicated tasks in complex environments. Thus, robots have to learn from and
adapttotheiroperatingenvironments.
Thisvolumeaimstoreflectthelatestprogressesmadeoncentralroboticsissues,
includingrobotnavigation,humansecurityandsurveillance,human-robotinterac-
tion, flocking robots, multiple robot cooperation and coordination. The collected
chapters not only represent the state-of-the-art research in robot development and
investigation,butalsodemonstratetheapplicationofawiderangeofmachinelearn-
ing techniques that vary from artificial neural networks, evolutionary algorithms,
fuzzylogic,reinforcementlearning,k-meansclustering,tomulti-agentreinforcing
learning.Thebookcanbeusedasavaluablereferenceforroboticsresearchers,en-
gineers, and practitioners for advanced knowledge, and university undergraduates
and postgraduates who would like to specialize in robotics research and develop-
ment.
Thirteen chapters are carefully selected from the extensive body of recent re-
search work, which tackles the challenging issues of robotics development and
applications with machine learning techniques. The selection is featured with the
breadthofmachinelearningtoolsandemphasizespracticalrobotapplications.
Skoglund et al. present a novel approach to robot skill acquisition from human
demonstration. Usually the morphology of a robot manipulator is very different
fromthatofthehumanarm.Inthiscase,ahumanmotioncannotbesimplycopied.
The proposed approach uses a motion planner that operates in an object-related
world-framecalledhand-statetosimplifyaskillreconstructionandpreservethees-
sentialpartsoftheskill.Inthisway,therobotisabletogeneralizethelearnedskills
toothersimilarskillswithouttriggeringanewlearningprocess.
Palm et al. focus on the robot grasp recognition, which is a major part of the
approachforProgramming-by-Demonstration.Theirworkdescribesthreedifferent
methodsforgrasprecognitionforahumanhand.Thefingerjointangletrajectories
ofhumangraspsaremodeledbyfuzzymodeling.Threemethodsforgrasprecogni-
tionarecomparedwitheachother.
Cheng et al. investigate the multiple manipulators which need to achieve the
samejointconfigurationtofulfillcertaincoordinationtasks.Underthemulti-agent
framework, a robust adaptive control approach is proposed to deal with this con-
sensusproblem.Uncertaintiesandexternaldisturbancesintherobot’sdynamicsare
considered,whichismorepracticalinreal-worldapplications.Duetotheapproxi-
mationabilityofneuralnetworks,theuncertaindynamicsarecompensatedbythe
adaptiveneuralnetworkscheme.
Ji et al. propose an exemplar-based view-invariant human action recognition
frameworktorecognizethehumanactionsfromanyarbitraryviewpointimagese-
quence. The proposed framework is evaluated in a public dataset and the results
showthatitnotonlyreducescomputationalcomplexity,butitisalsoabletoaccu-
ratelyrecognizehumanactionsusingsinglecameras.
Khoury and Liu introduce the concept of fuzzy Gaussian inference as a novel
way to build fuzzy membership functions that map underlying human motions to
hiddenprobabilitydistributions.Thismethodisnowcombinedwithageneticpro-
grammingfuzzyrulebasedsysteminordertoclassifyboxingmovesfromnatural
humanmotioncapturedata.
Preface vii
Zhouetal.considerthedetectionofhazardswithinthegroundplaneimmediately
infrontofamovingpedestrian.Usingepipolarconstraintsbetweentwoviews,de-
tectedfeaturesarematchedtocomputethecameramotionandreconstructthe3-D
geometry. For a less feature based scene a new disparity velocity based obstacle
detectionschemeispresented.
Tian andTang explorethe feasibility of using monocularvision for robot navi-
gation.Thepathdepthislearnedbyusingtheimagescapturedinasinglecamera.
Theirworkconcentratesonfindingpassableregionsfromasinglestillcolorimage
andmakingtherobotvisionlesssensitivetoilluminationchanges.
Liuetal.proposeanewmodeltocharacterizecameradistortionintheprocessof
thecameracalibration.Thismodelattemptstoblindlycharacterizetheoverallcam-
eradistortionwithouttakingthespecificradial,decentering,orthinprismdistortion
into account. To estimate the parameters of interest, the well-known Levernburg-
Marquardtalgorithmisapplied.ToinitializetheLevernburg-Marquardtalgorithm,
the results from the classical Tsai algorithm are estimated. After both the camera
intrinsicanddistortionparametershavebeenestimated,thedistortedimagepoints
arecorrectedusingagaintheLevernburg-Marquardtalgorithm.
WangandGupresentanapproachtodesignaflockingalgorithmbyusingfuzzy
logic.Thedesignofthreebasicbehaviorsinaflockingalgorithmisdiscussed.They
are alignment behavior, separation behavior, and cohesion behavior. Navigation
control component is used in the design of cohesion behavior. To avoid becoming
crowdingorcollision,anadaptivenavigationgainisused.Thisgainchangeswith
thenumberofneighbors.Theflockingstabilityisanalyzedandstabilityconditions
areacquiredfromthestabilityanalysis.
Oyekanetal.developabehaviorbasedcontrolarchitectureforUAVsurveillance
mission. This architecture contains two layers: atomic action layer and behavior
layer.Theyhavealsodevelopedsixatomicactionsandtenbehaviorsfortheselay-
ers.Varioustechniqueshavebeenusedinthedevelopment,includingadaptivePID
controller,fuzzylogiccontroller,SURFalgorithm,andKalmanfilter.
Guo et al. present a novel anti-disturbance control strategy named hierarchical
composite anti-disturbance control for a class of non-linear robotic systems with
multipledisturbances.Thestrategyisestablishedwhichincludesadisturbanceob-
serverbasedcontrollerandanH∞ controller,stabilityanalysisfortwocasestudies
areprovided.
Ballantyneetal.presentsomeofthekeyconsiderationsforhumanguidednav-
igationinthecontextofdynamicandcomplexindoorenvironments.Solutionsand
issuesrelatedtogesturerecognition,multi-cueintegration,tracking,targetpursuing,
sceneassociationandnavigationplanningarediscussed.
Kubota and Nishida discuss the adaptation of perceptual modules of a partner
robot based on classification and prediction through actual interactions with a hu-
man. They proposed a prediction-based perceptual system consisting of the input
layer,clusteringlayer,predictionlayer,andperceptualmoduleselectionlayer.They
applytheproposedmethodtotheactualinteractionbetweenahumanandahuman-
likepartnerrobot.
We would like to express our sincere thanks to all the authors who have con-
tributedtothebookandsupportduringthebookpreparation.Withouttheirsupport,
viii Preface
itisimpossibletoseetheadventofthisbook.ThanksalsogotoNatashaHarding
fromSpringerUKwhokindlyandeffectivelycommunicatedbetweenthepublisher
andoureditorsofthisbook.Wefeelespeciallygratefultoourpublisher,Springer,
who kindly supports the research direction of robot intelligence and the publica-
tionofthebook.Finally,itwouldbeourpleasurethatthisbookwouldbevaluable,
forin-depthunderstandingofrobotintelligencefromtheadvancedknowledgepro-
cessingpointofview,toawiderangeofaudiencefrommulti-disciplinaryresearch
communitiesandindustrialpractitioners.
Portsmouth,UK HonghaiLiu
Colchester,UK DongbingGu
Brighton,UK RobertJ.Howlett
Aberystwyth,UK YonghuaiLiu
Contents
1 Programming-by-DemonstrationofRobotMotions . . . . . . . . . . 1
AlexanderSkoglund,BoykoIliev,andRainerPalm
2 GraspRecognitionbyFuzzyModelingandHiddenMarkovModels 25
RainerPalm,BoykoIliev,andBourhaneKadmiry
3 DistributedAdaptiveCoordinatedControlofMulti-Manipulator
SystemsUsingNeuralNetworks . . . . . . . . . . . . . . . . . . . . . 49
Zeng-GuangHou,LongCheng,MinTan,andXuWang
4 ANewFrameworkforView-InvariantHumanActionRecognition . 71
XiaofeiJi,HonghaiLiu,andYiboLi
5 Using Fuzzy Gaussian Inference and Genetic Programming
toClassify3DHumanMotions . . . . . . . . . . . . . . . . . . . . . 95
MehdiKhouryandHonghaiLiu
6 ObstacleDetectionUsingCross-RatioandDisparityVelocity . . . . 117
HuiyuZhou,AndrewM.Wallace,andPatrickR.Green
7 LearningandVision-BasedObstacleAvoidanceandNavigation . . . 143
JiandongTianandYandongTang
8 AFractionDistortionModelforAccurateCameraCalibrationand
Correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
YonghuaiLiu,AlaAl-Obaidi,AnthonyJakas,andJunjieLiu
9 ALeader-FollowerFlockingSystemBasedonEstimatedFlocking
Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
ZongyaoWangandDongbingGu
10 ABehaviorBasedControlSystemforSurveillanceUAVs . . . . . . . 209
JohnOyekan,BowenLu,BoLi,DongbingGu,andHuoshengHu
ix
x Contents
11 HierarchicalCompositeAnti-DisturbanceControlfor Robotic
SystemsUsingRobustDisturbanceObserver . . . . . . . . . . . . . 229
LeiGuo,Xin-YuWen,andXinXin
12 AutonomousNavigationforMobileRobotswithHuman-Robot
Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
JamesBallantyne,EdwardJohns,SalmanValibeik,CharenceWong,
andGuang-ZhongYang
13 Prediction-BasedPerceptualSystemofaPartnerRobotforNatural
Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
NaoyukiKubotaandKenichiroNishida
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
Contributors
AlaAl-Obaidi SmartLightDevices,Ltd.,AberdeenAB242YN,UK,
[e-mail]
JamesBallantyne InstituteofBiomedicalEngineering,ImperialCollegeof
London,London,UK,[e-mail]
LongCheng KeyLaboratoryofComplexSciences,InstituteofAutomation,
ChineseAcademyofSciences,Beijing100190,China,
[e-mail]
PatrickR.Green Heriot-WattUniversity,Edinburgh,UK,[e-mail]
DongbingGu SchoolofComputerScienceandElectronicEngineering,
UniversityofEssex,WivenhoePark,ColchesterCO34SQ,UK,[e-mail]
LeiGuo SchoolofInstrumentScienceandOpto-ElectronicsEngineering,
BeihangUniversity,Beijing100191,China;ResearchInstituteofAutomation,
SoutheastUniversity,Nanjing210096,China,[e-mail]
Zeng-GuangHou KeyLaboratoryofComplexSciences,InstituteofAutomation,
ChineseAcademyofSciences,Beijing100190,China,[e-mail]
HuoshengHu SchoolofComputerScienceandElectronicEngineering,
UniversityofEssex,WivenhoePark,ColchesterCO34SQ,UK,[e-mail]
BoykoIliev DepartmentofTechnology,OrebroUniversity,70182Orebro,
Sweden,[e-mail]
AnthonyJakas DepartmentofComputerScience,AberystwythUniversity,
CeredigionSY233DB,UK,[e-mail]
XiaofeiJi IntelligentSystemsandBiomedicalRoboticsGroup,SchoolofCreative
Technologies,TheUniversityofPortsmouth,EldonBuilding,PortsmouthPO1
2DJ,UK;SchoolofAutomation,ShenyangInstituteofAeronauticalEngineering,
No.37DaoyiSouthAvenue,Shenyang110136,China,[e-mail]
xi