Multi Mobile Robot Task Allocation Using Multi-Objective Optimization Techniques

142 Pages·2017·17.39 MB·English

Multi Mobile Robot Task Allocation
Using Multi-Objective Optimization
Techniques
Avraam Tolmidis
School of Electrical & Computer Engineering
Aristotle University of Thessaloniki
This dissertation is submitted for the degree of
Doctor of Philosophy
March 2017

Declaration
I hereby declare that except where specific reference is made to the work of others, the
contents of this dissertation are original and have not been submitted in whole or in part
forconsiderationforanyotherdegreeorqualificationinthis,oranyotherUniversity. This
dissertation is the result of my own work and includes nothing which is the outcome of
workdoneincollaboration,exceptwherespecificallyindicatedinthetext. Thisdissertation
contains less than 65,000 words including appendices, bibliography, footnotes, tables and
equationsandhaslessthan150figures.
AvraamTolmidis
March2017

Acknowledgements
Firstly,IwouldliketoexpressmysinceregratitudetomyadvisorAssociateProf. Petroufor
thecontinuoussupportofmyPh.Dstudyandrelatedresearch,forhisendlesspatienceand
motivation. Itisamazinghowmucheffortheput(andstilldoes)inmakingsuretheresultof
thisworkiswhatitshouldbe. AskinghimtosupervisemyPh.Dstudywasoneofthebest
decisionsIhavemadethroughoutmyacademiclife.
Iwould alsolike tothankmy friendAndreas forhis supportand counselling throughout
theprocess,andmyfriendLydiaforprovidinghersupportwheneveritwasrequired.
Furthermore, I would like to thank everyone at the School of Electrical & Computer
Engineering of the Aristotle University of Thessaloniki, from the teaching stuff to the
secretariat(whosesupportwasinvaluable),forprovidingmethemeansandtheknowledge
tomakethecompletionofthisthesispossible.
Last but not the least, I would like to thank my parents for supporting me throughout
workingonthisthesisandmymylifeingeneral. Therewasnotatimethattheydidn’tcare
moreaboutmyhappinessandwell-being,thantheirs. Ireallycannotthankthemenough.

Abstract
Thisthesisexploresmulti-objectiveoptimisationformulti-robottaskallocation. Thegoal
wastodevelopageneralisedsystemfortaskallocation,thatwouldbeabletosupporttasks
belongingtodifferentdomains,optimisingtheallocationwithregardstovariousobjectives.
Improvements/additionstotheinitialsystemwerealsodeveloped: first,amethodtocombine
knowledgeofdifferentrobotsinordertobetterestimatetherequiredresourcesforeachtask;
then,anadaptivemulti-objectiveroutingsystem,thatenablestheroboticagentstoselecta
route using different profiles, that allocate different levels of importance to the objectives
theypursue. Atthesametime,theyadapttochangesintheenvironment.
The scope of this work is the task allocation problem itself; not task detection, neither
taskdecomposition. Thecollaborativeknowledgeusageofknowledge,aswellastherouting
method proposed, enhance the taskallocation system, however they aredeveloped insuch a
way,thattheycanbeusedindependently.
In order to solve the individual problems, software was developed, using the Java pro-
gramminglanguage. SimulatorswereusedlikePlayer/Stage,inordertotestthequalityof
thesolution,orNetLogo,toacquiretrafficdata. Insomeparts,likethecollaborativelearning
method,thesolutionwastestedonadditionaldomains,inordertoexamineitsbroadnessof
application. Otherparts(likethetaskallocationsystemitself)weredevelopedinsuchaway,
thattheirapplicationdomainisthebroadestpossible.
The results have revealed that the solutions proposed have a significant effect on the
efficiency. In the task allocation system, up to 15% of the time, as well as up to 35% of
the energy wassaved, compared tothe othermethods examined. Thecollaborative learning
methodhasshowntoreducetheresourcepredictionerrorbymorethan40%inthepresence
of limited available data, The routing methods compared to the oneproposed, requiredfrom
40to105%moretimetoreachtheirtarget. Trafficcongestiondoubledthetheirtimerequired,
whiletheincreaseforthemethodproposedwasintherangeof10%. Differentrouteprofiles,
produceddifferentresults.
Therefore, the methods proposed in this work can bring a big improvement in the
allocation of tasks in multi-robot systems, something important during our times with the
constant increase of robots in our everyday life. The collaborative learning method - even
viii
though it uses already existing approaches - can bring a significant improvement in the
absenceofsufficientdata,andtheroutingmethodproposed,hasthepotentialtoevolveintoa
completeurbantrafficmanagementsystem.
Table of contents
Listoffigures xiii
Listoftables xv
Symbols xx
1 Introduction 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 ProblemDescription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3.1 TypesofTaskAllocationProblemsinMulti-RobotSystems . . . . 3
1.3.2 StepsFollowedDuringPresentResearch . . . . . . . . . . . . . . 7
1.4 ThesisContribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.5 ThesisStructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 StateoftheArt 15
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 Multi-objectiveMulti-RobotTaskAllocation . . . . . . . . . . . . . . . . 15
2.3 CollaborativeRobotDecisionMakingFrameworks . . . . . . . . . . . . . 19
2.4 LearningUsingEnsembleMethods . . . . . . . . . . . . . . . . . . . . . . 19
2.5 CurrentRoutePlanningApproaches . . . . . . . . . . . . . . . . . . . . . 23
3 Multi-objective optimization for dynamic task allocation in a multi-robot sys-
tem 29
3.1 FrameworkOverview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 TaskAssignmentModel . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.1 ProblemFormulation . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.2 BidFormationProcess . . . . . . . . . . . . . . . . . . . . . . . . 38
3.2.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
x Tableofcontents
3.2.4 ComparisonoftheProposedMethodtoState-of-the-Art . . . . . . 45
3.3 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.4 AssessmentoftheProposedSolution . . . . . . . . . . . . . . . . . . . . . 50
3.5 ChapterConclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4 EnsembleMethodsforCooperativeRoboticLearning 59
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.2 MotivationalScenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3 PredictionModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.3.1 ProblemFormulation . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.3.2 PredictionModelFormationProcess . . . . . . . . . . . . . . . . . 64
4.3.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.4 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.4.1 RobotTaskAllocationSystem . . . . . . . . . . . . . . . . . . . . 69
4.4.2 AdditionalApplications . . . . . . . . . . . . . . . . . . . . . . . 71
4.5 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.5.1 Multi-RobotTaskAllocationSystem . . . . . . . . . . . . . . . . 72
4.5.2 AdditionalApplications . . . . . . . . . . . . . . . . . . . . . . . 76
4.6 ChapterConclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5 AdaptiveMulti-objectiveOptimisedRouteDiscovery 83
5.1 RoutePlanningforTaskAllocationandBeyond . . . . . . . . . . . . . . . 83
5.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.3 Multi-ObjectiveAdaptivePathPlanning . . . . . . . . . . . . . . . . . . . 85
5.3.1 BidFormationProcess . . . . . . . . . . . . . . . . . . . . . . . . 87
5.4 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.5 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.5.1 AMORComparedtoOtherMethods . . . . . . . . . . . . . . . . . 94
5.5.2 UseofDifferentAMORProfiles . . . . . . . . . . . . . . . . . . . 98
5.6 ChapterConclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
6 ConclusionsandFutureWork 107
6.1 Multi-ObjectiveOptimizationforRobotTaskAllocation . . . . . . . . . . 107
6.1.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.1.2 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
6.2 EnsembleMethodsforCooperativeLearning . . . . . . . . . . . . . . . . 109
6.2.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109