Multi-Robot Task Allocation for Inspection Problems with Cooperative Tasks Using Hybrid Genetic ...

149 Pages·2014·5.35 MB·English

by  Chun Liu

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In this dissertation, methods for optimal multi-robot task allocation (MRTA) prob- h Multi-Robot Task Allocation for Inspection
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lems resulting from industrial plant inspection missions that involve both single- and s
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two-robot tasks were developed. A novel hybrid genetic algorithm was developed h
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by combining a subpopulation-based genetic algorithm for global optimization with ori Hybrid Genetic Algorithms
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local search heuristics. It did not only distribute and sequence all tasks, but also Al
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formed temporary robot coalitions that are required for two-robot tasks which in-
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troduce spatial and temporal constraints into the optimization problems. e
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Four alternative coding strategies were developed for the proposed hybrid genetic y
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algorithm: subtask-, task-, combination-, and decomposition-based coding. These –
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inspection problems with different environmental information and computational e
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04 Schriftenreihe Mess- und Regelungstechnik der Universität Kassel
ISBN 978-3-86219-551-0
04
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Univ.-Prof. Dr.-Ing. Andreas Kroll
9 783862 195510
Schriftenreihe Mess‐ und Regelungstechnik der Universität Kassel 
Band 4 / Vol. 4
Herausgegeben von / Edited by
Univ.-Prof. Dr.-Ing. Andreas Kroll

Multi‐Robot Task Allocation for Inspection 
Problems with Cooperative Tasks Using 
Hybrid Genetic Algorithms 

Chun Liu

kassel
university
press
This work has been accepted by the Faculty of Mechanical Engineering of the University of Kassel as a
thesis for acquiring the academic degree of Doktor der Ingenieurwissenschaften (Dr.-Ing.).

Supervisor: Univ.-Prof. Dr.-Ing. Andreas Kroll, Universität Kassel
Co-Supervisor: Dr.-Ing. Wilfried Jakob, Karlsruher Institut für Technologie (KIT)

Defense Day: 9th September 2014

The electronic version of this publication is available on Kasseler Online Bibliothek, Repository und Archiv
(KOBRA) at https://kobra.bibliothek.uni-kassel.de/

Bibliographic information published by the Deutsche Nationalbibliothek
The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed
bibliographic data are available in the Internet at http://dnb.dnb.de.

Zugl.: Kassel, Univ., Diss. 2014
ISBN 978-3-86219-551-0

© 2014, kassel university press GmbH, Kassel
www.uni-kassel.de/upress

Printed in Germany
Foreword
This dissertation summarizes my research on the multi-robot task allocation for inspection
problems that has been carried out at the Department of Measurement and Control, Me-
chanical Engineering, University of Kassel. This research would not have been possible
without the inspiration and help of many people to whom I would like to express my grati-
tude.
First and foremost, I would like to express my deepest gratefulness to my supervisor Prof.
Dr.-Ing. Andreas Kroll, the head of our department, for his invaluable insights and advices
that gave me needful guidance to complete the academic research and this dissertation.
IwouldliketothankDr.-Ing. WilfriedJakobforhispreciousideasandcommentsthathelped
me improve this thesis. I would also like to thank Prof. Dr. Bernhard Sick and Prof. Dr. rer.
nat. Arno Linnemann for their willingness and dedication to serve on my committee.
IamalsoindebtedtoallmycolleaguesintheDepartmentofMeasurementandControl. This
research would not have been achieved without their great support and important sugges-
tions. In particular, I would like to thank Dr. Hanns-Jakob Sommer for his precious scientific
advices, Christina Kuchta for her great help in solving difficulties in life and in correcting the
spelling and grammar of papers.
I am very grateful for the scholarship awarded by the China Scholarship Council (CSC)
that has financially supported my study and research at Kassel. I am also thankful for the
Completion Scholarship awarded by the University of Kassel (Abschlussstipendien für Pro-
movierendederUniversitätKassel)thathassupportedmeincompletingthisdissertation.
Finally, I must express my appreciation to my family. I am deeply indebted to my parents
who have always been there to support me. Without their understanding and unconditional
love, this work would not have been completed. My special thanks to my husband, Gang
Mei, for his love, patience, and encouragement, particularly in critical phases.
Parts of the work have been published in an international conference and a journal.
Kassel, September 2014
Chun Liu
I
Abstract
Inthisdissertation,methodsforoptimalmulti-robottaskallocation(MRTA)forindustrialplant
inspection are investigated. MRTA involves distributing and scheduling a set of tasks for a
group of robots to minimize the total cost taking into account operational constraints. With
technicalprogressanddecliningcostofroboticmobility,interestinindustrialmobilerobotics
has grown significantly in recent years. Many efforts have been devoted to mobility-related
problems such as self-localization and mapping, though only few studies deal with the op-
timal task allocation in multi-robot systems. Since a good task allocation provides more
efficient scheduling(e.g. less cost, shorter time), the objectiveof this researchis to develop
search/optimizationmethodsforinspectionproblemsthatinvolvebothsingle-andtwo-robot
tasks.
A novel hybrid genetic algorithm is presented by combining a subpopulation-based genetic
algorithm for global optimization with local search heuristics. To speed up the proposed
algorithm, local search is applied to the superior individuals selected from each generation.
Thishybridgeneticalgorithmdoesnotonlydistributeandsequencealltasks,butalsoforms
robot coalitions that are required for two-robot tasks which introduce spatial and temporal
constraints into the optimization problems.
Four alternative coding strategies are designed for the hybrid genetic algorithm:
• Subtask-based coding: Algorithms with this coding strategy cover all possible solu-
tions, though result in a large search space.
• Task-based coding: Two ways of assigning two-robot tasks are implemented to im-
prove the efficiency of the algorithms.
• Combination-based coding: A temporal constraint is introduced to group tasks such
that good solutions can be obtained within a small number of generations. Two alter-
natives with different task groups are presented.
• Decomposition-based coding: Three decompositions that exploit available knowledge
on the geometry of the inspection areas (assuming rectangular pathways and inad-
missible zones) are designed to improve the efficiency of the proposed algorithm.
The performance of the proposed hybrid genetic algorithm with different coding strategies
is evaluated in case studies on inspecting a large storage tank area of a petroleum refinery.
Experimentalresultsshowthatthedecomposition-basedcodingstrategycanachievebetter
solutions than the other coding strategies within a small number of generations.
This work considers single- and two-robot tasks derived from the planned deployment of
a group of homogeneous mobile robots for inspections that can be carried out by a single
robotorrequirethecooperationoftworobots. Thedevelopedalgorithmcanbeextendedto
solvetasksrequiringmorerobots,buttheproblemcomplexitywouldincreasesignificantly.
II
Zusammenfassung
In dieser Dissertation werden Methoden zur optimalen Aufgabenverteilung in Multiroboter-
systemen (engl. Multi-Robot Task Allocation – MRTA) zur Inspektion von Industrieanlagen
untersucht. MRTA umfasst die Verteilung und Ablaufplanung von Aufgaben für eine Gruppe
von Robotern unter Berücksichtigung von operativen Randbedingungen mit dem Ziel, die
Gesamteinsatzkostenzuminimieren. DankzunehmendemtechnischenFortschrittundsink-
enden Technologiekosten ist das Interesse an mobilen Robotern für den Industrieeinsatz in
den letzten Jahren stark gestiegen. Viele Arbeiten konzentrieren sich auf Probleme der
Mobilität wie Selbstlokalisierung und Kartierung, aber nur wenige Arbeiten untersuchen die
optimale Aufgabenverteilung. Da sich mit einer guten Aufgabenverteilung eine effizientere
Planung erreichen lässt (z. B. niedrigere Kosten, kürzere Ausführungszeit), ist das Ziel
dieser Arbeit die Entwicklung von Lösungsmethoden für das aus Inspektionsaufgaben mit
Einzel- und Zweiroboteraufgaben folgende Such-/Optimierungsproblem.
EinneuartigerhybriderGenetischerAlgorithmuswirdvorgestellt,dereinenteilbevölkerung-
basierten Genetischen Algorithmus zur globalen Optimierung mit lokalen Suchheuristiken
kombiniert. Zur Beschleunigung dieses Algorithmus werden auf die fittesten Individuen
einer Generation lokale Suchoperatoren angewendet. Der vorgestellte Algorithmus verteilt
die Aufgaben nicht nur einfach und legt den Ablauf fest, sondern er bildet auch temporäre
Roboterverbünde für Zweiroboteraufgaben, wodurch räumliche und zeitliche Randbedin-
gungen entstehen.
VieralternativeKodierungsstrategienwerdenfürdenvorgestelltenAlgorithmusentworfen:
• TeilaufgabenbasierteKodierung: HierdurchwerdenallemöglichenLösungenabgedeckt,
allerdings ist der Suchraum sehr groß.
• Aufgabenbasierte Kodierung: Zwei Möglichkeiten zur Zuweisung von Zweiroboterauf-
gaben wurden implementiert, um die Effizienz des Algorithmus zu steigern.
• Gruppierungsbasierte Kodierung: Zeitliche Randbedingungen zur Gruppierung von
Aufgaben werden vorgestellt, um gute Lösungen innerhalb einer kleinen Anzahl von
Generationen zu erhalten. Zwei Umsetzungsvarianten werden vorgestellt.
• Dekompositionsbasierte Kodierung: Drei geometrische Zerlegungen wurden entwor-
fen, die Informationen über die räumliche Anordnung ausnutzen, um Probleme zu
lösen, die Inspektionsgebiete mit rechteckigen Geometrien aufweisen.
In Simulationsstudien wird die Leistungsfähigkeit der verschiedenen hybriden Genetischen
Algorithmenuntersucht. DazuwurdedieInspektionvonTanklagerneinerErdölraffineriemit
einer Gruppe homogener Inspektionsroboter als Anwendungsfall gewählt. Die Simulatio-
nen zeigen, dass Kodierungsstrategien, die auf der geometrischen Zerlegung basieren, bei
einer kleinen Anzahl an Generationen eine bessere Lösung finden können als die anderen
untersuchten Strategien.
DieseArbeitbeschäftigtsichmitEinzel-undZweiroboteraufgaben,dieentwedervoneinem
einzelnen mobilen Roboter erledigt werden können oder die Zusammenarbeit von zwei
Robotern erfordern. Eine Erweiterung des entwickelten Algorithmus zur Behandlung von
Aufgaben, die mehr als zwei Roboter erfordern, ist möglich, würde aber die Komplexität der
Optimierungsaufgabe deutlich vergrößern.
III
Abbreviations
Abbreviation Term
AAP Accessible area with inspection positions
AAS Accessible area divided by intersections
CB Combination-based coding
DB Decomposition-based coding
EC Executability constraints
GA Genetic algorithm
HGA Hybrid genetic algorithm
HSC Three-tasks combination
IA Instantaneous assignments/Inaccessible areas
LIA Along the same inaccessible areas
LS Local search
LW Least-waiting-time decoding
MR Multi-robot tasks
MRTA Multi-robot task allocation
MRTA-S Multi-robot task allocation with only single-robot tasks
MRTA-T Multi-robot task allocation with two-robot tasks
MT Multi-task robots
NT Nearest-task decoding
SB Subtask-based coding
SC Scheduling constraints
SR Single-robot tasks
ST Single-task robots
TA Time-extended assignments
TB Task-based coding
WSC Two-tasks combination
IV
Symbols
Symbol Explanation
A MRTA solution candidate by permutation coding
A Task assignment and task schedule of robot R
k k
ak i-th subtask of A
i k
B Set of individuals
B Set of best individuals in j-th generation
j
C Set of time costs
C (A ) Time for robot R to complete its tasks A
k k k k
ct Traveling time of robot R from inspection position of subtask P to that
ijk k i
of P
j
cs Time robot R needs to carry out inspection task of subtask P
jk k j
cw Waiting time of robot R to execute subtask P after arriving at the in-
jk k j
spection position of P
j
dP Pathway width
DF Degrees of freedom
E Grid map
e Cell of grid map
xy
fT Function that maps the set T to the set P
F Ratio of the mean squares – F statistic
G Current generation number
crt
G Maximum number of generations
max
a
G Gene-apportion
opt
g (i) i-thelementofgene-apportionoftemporaryoptimalindividualobtained
j
in j-th generation
H Thresholdvalueforgroupingsubtasksusingcombination-basedcoding
J Cost function (completion time)
K Number of subpopulations
l Number of subtasks of A
k k
lz Number of genes of Z
k k
M Submatrices of E
MS Mean squares
NG Number of genes of a chromosome
NP Number of subtasks of P
NQ Number of subtask groups of Q
NR Number of robots
NT Number of tasks of T
Nb Number of elements in B in j-th generations
j
Nx Size of grid map in x direction
Ny Size of grid map in y direction
V