Response threshold models, stochastic learning automata and ant colony optimization-based ...

123 Pages·2012·2.04 MB·English

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UNIVERSIDAD POLITECNICA DE MADRID
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FACULTAD DE INFORMATICA
Response Threshold Models, Stochastic Learning
Automata and Ant Colony Optimization-based
Decentralized Self-Coordination Algorithms for
Heterogeneous Multi-Tasks Distribution in
Multi-Robot Systems
Ph.D Thesis
Alma Yadira Quin˜onez Carrillo
M.Sc. in Artificial Intelligence
Madrid, 2012

DEPARTAMENTO DE INTELIGENCIA
ARTIFICIAL
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FACULTAD DE INFORMATICA
Response Threshold Models, Stochastic Learning
Automata and Ant Colony Optimization-based
Decentralized Self-Coordination Algorithms for
Heterogeneous Multi-Tasks Distribution in
Multi-Robot Systems
Alma Yadira Quin˜onez Carrillo
M.Sc. in Artificial Intelligence
Thesis Advisors
Javier de Lope Asia´ın
PhD. in Informatics
Dar´ıo Maravall Go´mez-Allende
PhD. Telecommunications Engineer
Madrid, 2012

Tribunal nombrado por el Magfco. y Excmo. Sr. Rector de la Universidad
Polit´ecnica de Madrid, el d´ıa —– de ———– de 2012.
Presidente: —————————————–
Vocal: —————————————–
Vocal: —————————————–
Vocal: —————————————–
Secretario: —————————————–
Suplente: —————————————–
Suplente: —————————————–
Realizado el acto de defensa y lectura de la Tesis el d´ıa —– de ———– de
2012 en la Facultad de Informa´tica.
VOCAL VOCAL VOCAL
PRESIDENTE SECRETARIO
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I would like to dedicate this thesis to my Mother and my Brothers.
Acknowledgements
After such a great experience, I obviously have many people to thank...
First, I want to thank all my family members. Thanks for being there and
supporting me in every decision. Thank you for believing in me and giving me
the strength to face even the most difficult things. Definitely thanks to you all I
was able achieve this objective.
I would also like to take this opportunity to thank my supervisors, Javier de
Lope y Dar´ıo Maravall, because they have helped me enormously to further my
understanding and expand my horizons in the field of robotics, but above all, I
am very grateful to them for their unfailing interest, guidance and wisdom during
the development this project.
I am sincerely thankful with the Consejo Nacional de Ciencia y Tecnolog´ıa,
the Univesidad Aut´onoma de Sinaloa and the Universidad Polit´ecnica de Madrid
for contributing with the financial support in conducting this PhD thesis.
A heartfelt thanks also to the members of the Lab for making my stay more
comfortable, but in particular, to Antonio Fern´andez and Juan Bekios for their
comments and suggestions.
Finally, but not least important, I would like to express my gratitude to all
my friends that I met here in Madrid, who they not only encouraged me during
the research career, but also, have given me many great moments. Thanks to
Marinela, Iva´n, Lindsay, Miguel, Jez, Boris, Gonzalo, Juan, Tony, Ernesto, Rau´l,
Ghislain, David and Monse for sharing with me so many lunches and speaking
not only about work, I have enjoyed these last few years enormously! Thank you
all for your support, friendship and conviviality.
Yadira Quin˜onez
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Abstract
In recent decades, there has been an increasing interest in systems comprised of
several autonomous mobile robots, and as a result, there has been a substantial
amountofdevelopmentinthefieldofArtificialIntelligence,especiallyinRobotics.
There are several studies in the literature by some researchers from the scientific
community that focus on the creation of intelligent machines and devices capable
to imitate the functions and movements of living beings. Multi-Robot Systems
(MRS) can often deal with tasks that are difficult, if not impossible, to be accom-
plished by a single robot. In the context of MRS, one of the main challenges is
the need to control, coordinate and synchronize the operation of multiple robots
to perform a specific task. This requires the development of new strategies and
methods which allow us to obtain the desired system behavior in a formal and
concise way.
This PhD thesis aims to study the coordination of multi-robot systems, in
particular, addressestheproblemofthedistributionofheterogeneousmulti-tasks.
Themaininterestinthesesystemsistounderstandhowfromsimplerulesinspired
by the division of labor in social insects, a group of robots can perform tasks in
an organized and coordinated way. We are mainly interested on truly distributed
or decentralized solutions in which the robots themselves, autonomously and in
an individual manner, select a particular task so that all tasks are optimally
distributed.
In general, to perform the multi-tasks distribution among a team of robots,
they have to synchronize their actions and exchange information. Under this
approach we can speak of multi-tasks selection instead of multi-tasks assignment,
whichmeans, thattheagentsorrobotsselectthetasksinsteadofbeingassigneda
task by a central controller. The key element in these algorithms is the estimation
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of the stimuli and the adaptive update of the thresholds. This means that each
robot performs this estimate locally depending on the load or the number of
pending tasks to be performed. In addition, it is very interesting the evaluation
of the results in function in each approach, comparing the results obtained by the
introducing noise in the number of pending loads, with the purpose of simulate
the robot’s error in estimating the real number of pending tasks.
The main contribution of this thesis can be found in the approach based on
self-organization and division of labor in social insects. An experimental scenario
for the coordination problem among multiple robots, the robustness of the ap-
proaches and the generation of dynamic tasks have been presented and discussed.
The particular issues studied are:
• Threshold models: It presents the experiments conducted to test the re-
sponse threshold model with the objective to analyze the system perfor-
mance index, for the problem of the distribution of heterogeneous multi-
tasks in multi-robot systems; also has been introduced additive noise in the
number of pending loads and has been generated dynamic tasks over time.
• Learning automata methods: It describes the experiments to test the learn-
ing automata-based probabilistic algorithms. The approach was tested to
evaluate the system performance index with additive noise and with dy-
namic tasks generation for the same problem of the distribution of hetero-
geneous multi-tasks in multi-robot systems.
• Ant colony optimization: The goal of the experiments presented is to test
the ant colony optimization-based deterministic algorithms, to achieve the
distribution of heterogeneous multi-tasks in multi-robot systems. In the
experiments performed, the system performance index is evaluated by in-
troducing additive noise and dynamic tasks generation over time.
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