Spatial Allocation in Swarm Robotics

123 Pages·2015·9.24 MB·English

Spatial Allocation in Swarm Robotics
Thesis submitted in partial fulfillment of the requirements for the degree of
Master of Science in Engineering of Computing Systems
School of Industrial and Information Engineering (Ing. Ind-Inf)
Jacopo De Stefani - Student ID 782688
Supervisor
Prof. Andrea Bonarini
Co-supervisor
Prof. Mauro Birattari
Laboratory Academic Year
AIR 2013 - 2014
Jacopo De Stefani: Spatial Allocation in Swarm Robotics, Master thesis
in Engineering of Computing Systems, (cid:13)c March 2015
"My family is my strength and my weakness."
— Aishwarya Rai Bachchan
“A friend is one that knows you as you are, understands where you have
been, accepts what you have become, and still, gently allows you to grow.”
— William Shakespeare
Grazie di tutto, Nonno.
Dedicated to the loving memory of Mario Enrico De Stefani.
α 1931– Ω 2013

We have seen that computer programming is an art,
because it applies accumulated knowledge to the world,
because it requires skill and ingenuity, and especially
because it produces objects of beauty.
1974
— Donald E. Knuth [Knuth, ]
ACKNOWLEDGEMENTS
This thesis is the outcome of a long journey, started in Brussels, in
2012
September .
Iwouldliketothankprof. MauroBirattari,forgivingmethepossi-
bility to stay at IRIDIA, and for the continuous and fruitful feedback
he gave me through the preparation of the first version of this work.
However, the completion of this manuscript wouldn’t have been pos-
siblewithoutthesupervisionofprof. AndreaBonarini,whichguided
me to the definition of the final shape of this document.
ThanksalsotoGiovanni,Carlo,ManueleandGiampiero,especially
for their help on technical matters.
Special thanks to all the people that accompanied me during the
Brussels part of my journey.
Tomycolleagues,Nadine,DimitriandRobin,whohavewelcomed
me since the beginning, gave me helpful advices concerning the uni-
versity and helped improving my French skills.
To all the people I met throughout these two years that supported
meandhelpedmegrowasaperson: Michele,Simona,Chiara,Laura,
Thomas & Francesca among all.
A special mention for my "second family" in Brussels, Fabrizio,
Giorgio, Laura, Eleonora, Blanca and Sinan, which is now spread
all across the world.
Thanks to Gianluca, Marco, Davide G., Davide & Anissa, Stefano
and all my friends, and teammates in Castelleone, who always made
me feel at home, no matter how much time I spent away and how
many times I ditched them to complete this work.
Ultimo ma non meno importante, un grazie di cuore ai miei geni-
tori. Grazie per avermi dato la possibilità di partire e per non avermi
mai fatto mancare il vostro supporto e la vostra presenza, indipen-
dentemente dalla distanza.
v

CONTENTS
1 introduction 1
2 background related work 4
&
21 4
. Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . .
211 4
. . Self-organization . . . . . . . . . . . . . . . . . .
212 6
. . Swarm intelligence . . . . . . . . . . . . . . . . .
213 9
. . Swarm robotics . . . . . . . . . . . . . . . . . . .
22 18
. Spatial allocation . . . . . . . . . . . . . . . . . . . . . .
221 19
. . Related work . . . . . . . . . . . . . . . . . . . .
23 33
. Materials . . . . . . . . . . . . . . . . . . . . . . . . . . .
231 33
. . e-puck . . . . . . . . . . . . . . . . . . . . . . . .
232 37
. . IRIDIA Task Abstraction Module . . . . . . . . .
233 39
. . Autonomous Robot Go Swarming . . . . . . . .
3 methodology 43
31 43
. Problem statement . . . . . . . . . . . . . . . . . . . . .
32 46
. Experimental setup . . . . . . . . . . . . . . . . . . . . .
321 47
. . Formal modelling . . . . . . . . . . . . . . . . . .
322 50
. . Multi-robot task abstraction . . . . . . . . . . . .
323 51
. . Single-robot task abstraction . . . . . . . . . . .
324 52
. . Robot placement . . . . . . . . . . . . . . . . . .
33 52
. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . .
331 53
. . Overview . . . . . . . . . . . . . . . . . . . . . .
332 55
. . Naive . . . . . . . . . . . . . . . . . . . . . . . . .
333 57
. . Probabilistic . . . . . . . . . . . . . . . . . . . . .
334 59
. . Informed . . . . . . . . . . . . . . . . . . . . . . .
335 61
. . Summary . . . . . . . . . . . . . . . . . . . . . .
4 results 63
41 63
. Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . .
411 63
. . Uniform . . . . . . . . . . . . . . . . . . . . . . .
412 63
. . Biased . . . . . . . . . . . . . . . . . . . . . . . .
413 65
. . Corridor . . . . . . . . . . . . . . . . . . . . . . .
42 66
. Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . .
421 66
. . Allocated robots . . . . . . . . . . . . . . . . . .
422 67
. . Maximum error . . . . . . . . . . . . . . . . . . .
423 67
. . Allocation levels . . . . . . . . . . . . . . . . . .
424 68
. . Cluster views . . . . . . . . . . . . . . . . . . . .
43 69
. Allocation uniformity. . . . . . . . . . . . . . . . . . . .
431 70
. . Maximum error . . . . . . . . . . . . . . . . . . .
432 71
. . Allocated robots . . . . . . . . . . . . . . . . . .
433 72
. . Allocation levels . . . . . . . . . . . . . . . . . .
vii
viii contents
44 73
. Allocation speed . . . . . . . . . . . . . . . . . . . . . .
441 74
. . Scenario Uniform . . . . . . . . . . . . . . . . . .
442 74
. . Scenario Biased . . . . . . . . . . . . . . . . . . .
443 75
. . Scenario Corridor . . . . . . . . . . . . . . . . . .
45 82
. Scenario difficulty . . . . . . . . . . . . . . . . . . . . . .
451 82
. . Scenario Uniform . . . . . . . . . . . . . . . . . .
452 82
. . Scenario Biased . . . . . . . . . . . . . . . . . . .
453 83
. . Scenario Corridor . . . . . . . . . . . . . . . . . .
5 conclusion future work 87
&
51 87
. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . .
52 89
. Future work . . . . . . . . . . . . . . . . . . . . . . . . .
a additional plots 90
a1 91
. Maximum error . . . . . . . . . . . . . . . . . . . . . . .
a2 94
. Allocated robots . . . . . . . . . . . . . . . . . . . . . . .
bibliography 97
LIST OF FIGURES
21
Figure . Examplesofself-organizingpatternsemerging
5
from morphogenesis . . . . . . . . . . . . . . .
22 9
Figure . Examples of swarm intelligence . . . . . . . . .
23
Figure . Overview of the typical development process
12
in swarm engineering . . . . . . . . . . . . . . .
24
Figure . Overview of the main challenges in the devel-
17
opment of a swarm robotics system . . . . . .
25
Figure . Overview of the approaches used in the sci-
entific literature to tackle problems related to
19
spatial allocation. . . . . . . . . . . . . . . . . .
26
Figure . Finite state machine representing a high level
20
decomposition of the basic foraging task . . .
27
Figure . Overview of the approaches used in the scien-
tific literature to tackle multi-robot task alloca-
25
tion in a centralized fashion . . . . . . . . . . .
28
Figure . Overview of the approaches used in the scien-
tific literature to tackle multi-robot task alloca-
27
tion in a distributed fashion . . . . . . . . . . .
Figure 2.9 Annotated picture of the e-puck robot . . . . . 33
Figure 2.10 Sensor placement on the e-puck . . . . . . . . . 34
Figure 2.11 PictureoftheTAMwithane-puckwiththerange
andbearingboardandtheomni-directionalcam-
37
era extension . . . . . . . . . . . . . . . . . . . .
212 40
Figure . Architecture of ARGoS . . . . . . . . . . . . . . .
213
Figure . Simplifiedpseudo-codeofthemainsimulation
41
loop of ARGoS . . . . . . . . . . . . . . . . . . .
214
Figure . Screenshot of the ARGoS simulator for the sce-
nario Biased environment configuration . . . . 42
31
Figure . UMLActivityDiagramrepresentingtheNDSR-
ST-IA approach to the multi-robot task alloca-
48
tion problem . . . . . . . . . . . . . . . . . . . .
32
Figure . UMLActivityDiagramrepresentingtheXDSR-
MT-IAapproachtothemulti-robottaskalloca-
49
tion problem . . . . . . . . . . . . . . . . . . . .
33
Figure . Schematic representation of the TAM disposi-
50
tion in a cluster . . . . . . . . . . . . . . . . . .
34 51
Figure . Finite state machine representing the TAM states
35
Figure . Finitestatemachinerepresentingthehighlevel
53
behavior of a single agent . . . . . . . . . . . .
ix
x ListofFigures
36
Figure . Deterministic finite state machine correspond-
ing to the implemented individual robot con-
troller for the Naive method . . . . . . . . . . . 55
37
Figure . Probabilistic finite state machine correspond-
ing to the implemented individual robot con-
troller for the Probabilistic method . . . . . . . . 57
38
Figure . Probabilistic finite state machine correspond-
ing to the implemented individual robot con-
troller for the Informed method . . . . . . . . . 59
Figure 3.9 SchematicrepresentationoftheInformedodometry-
61
based cluster avoidance procedure . . . . . . .
41
Figure . Representationoftheclusterdispositioninsce-
nario Uniform . . . . . . . . . . . . . . . . . . . 64
42
Figure . Representationoftheclusterdispositioninsce-
nario Biased . . . . . . . . . . . . . . . . . . . . . 64
43
Figure . Representationoftheclusterdispositioninsce-
nario Corridor . . . . . . . . . . . . . . . . . . . 65
44
Figure . Median values for the maximum error across
clusters e (t) and the number of allocated
max
robots R(t) on the scenario Uniform . . . . . . . 76
45
Figure . Empirical cumulative density functions for the
allocation levels t (l), (l ∈ 0.25,0.50) distribu-
o
tionsofthethreemethodsonthescenarioUni-
form . . . . . . . . . . . . . . . . . . . . . . . . . 77
46
Figure . Median values for the maximum error across
clusters e (t) and the number of allocated
max
robots R(t) on the scenario Biased . . . . . . . . 78
47
Figure . Empirical cumulative density functions for the
allocation levels t (l), (l ∈ 0.25,0.50) distribu-
o
tions of the three methods on the scenario Biased 79
48
Figure . Median values for the maximum error across
clusters e (t) and the number of allocated
max
robots R(t) on the scenario Corridor . . . . . . . 80
49
Figure . Empirical cumulative density functions for the
allocation levels t (l), (l ∈ 0.25,0.50) distribu-
o
tionsofthethreemethodsonthescenarioCor-
ridor . . . . . . . . . . . . . . . . . . . . . . . . . 81
Figure 4.10 Median value of v (t) for i ∈ {1,··· ,4} on sce-
i
nario Uniform . . . . . . . . . . . . . . . . . . . 84
Figure 4.11 Median value of v (t) for i ∈ {1,··· ,4} on sce-
i
nario Biased . . . . . . . . . . . . . . . . . . . . . 85
Figure 4.12 Median value of v (t) for i ∈ {1,··· ,4} on sce-
i
nario Corridor . . . . . . . . . . . . . . . . . . . 86
1
Figure A. Comparison of the maximum allocation error
e of the three methods on the scenario Uni-
max
form . . . . . . . . . . . . . . . . . . . . . . . . . 91