PhD Thesis - Coordination of Multiple Robotic Agents for Disaster and Emergency Response

´
INSTITUTO TECNOLOGICO Y DE ESTUDIOS SUPERIORES DE MONTERREY
CAMPUS CAMPUS MONTERREY

SCHOOL OF ENGINEERING AND INFORMATION TECHNOLOGIES
GRADUATE PROGRAMS

DOCTOR OF PHILOSOPHY
IN
INFORMATION TECHNOLOGIES AND COMMUNICATIONS
MAJOR IN INTELLIGENT SYSTEMS

Dissertation
Coordination of Multiple Robotic Agents
For Disaster and Emergency Response

By

´
Jesus Salvador Cepeda Barrera

DECEMBER 2012

A dissertation presented by

´
Jesus Salvador Cepeda Barrera

Submitted to the
Graduate Programs in Engineering and Information Technologies
in partial fulﬁllment of the requirements for the degree of

Doctor of Philosophy
in
Information Technologies and Communications
Major in Intelligent Systems

Thesis Committee:

Dr. Rogelio Soto - Tecnol´ gico de Monterrey
o
Dr. Luiz Chaimowicz - Universidade Federal de Minas Gerais
Dr. Jos´ Luis Gordillo
e - Tecnol´ gico de Monterrey
o
Dr. Leonardo Garrido - Tecnol´ gico de Monterrey
o
Dr. Ernesto Rodr´guez
ı - Tecnol´ gico de Monterrey
o

Instituto Tecnol´ gico y de Estudios Superiores de Monterrey
o
Campus Campus Monterrey
December 2012

Instituto Tecnol´ gico y de Estudios Superiores de Monterrey
o
Campus Campus Monterrey
School of Engineering and Information Technologies
Graduate Program

The committee members hereby certify that have read the dissertation presented by Jes´ s Sal-
u
vador Cepeda Barrera and that it is fully adequate in scope and quality as a partial fulﬁllment
of the requirements for the degree of Doctor of Philosophy in Information Technologies
and Communications, with a major in Intelligent Systems.

Dissertation Committee

Dr. Rogelio Soto
Advisor

Dr. Luiz Chaimowicz
External Co-Advisor
Universidade Federal de Minas Gerais

Dr. Jos´ Luis Gordillo
e
Committee Member

Dr. Leonardo Garrido
Committee Member

Dr. Ernesto Rodr´guez
ı
Committee Member

Dr. C´ sar Vargas
e
Director of the Doctoral Program in
Information Technologies and
Communications

i

Copyright Declaration

I, hereby, declare that I wrote this dissertation entirely by myself and, that, it exclusively
describes my own research.

Jes´ s Salvador Cepeda Barrera
u
Monterrey, N.L., M´ xico
e
December 2012

c 2012 by Jes´ s Salvador Cepeda Barrera
u
All Rights Reserved

ii

Dedicatoria

Dedico este trabajo a todos quienes me dieron la oportunidad y confiaron en que valdrá la
ı
pena este tiempo que no solo requiri´ de trabajo arduo y de nuevas experiencias, sino que
o
demand´ por apoyo constante, paciencia y aliento ante los peródos m´ s difćiles.
o ı a ı

A mi padre por su sacrificio eterno para convencerme de pensar en grande y de hacer que
´
valga la pena el camino y sus dificultades. A el por aguantar hasta estos dás la economá del
ı ı
estudiante y confiar siempre que lo mejor est´ por venir. A ti pap´ por tu amor y guá con
a a ı
sabidurá para permitirme llegar hasta donde me lo proponga.
ı

A mi madre por su abrazo sin igual que siempre abre nuevas brechas cuando pareciera que ya
no hay por donde continuar. A ella por el regazo donde renacen las fuerzas y motivaci´ n para
o
volver a intentar. A ti mam´ por el amor que siempre me da seguridad para seguir adelante
a
sabiendo que hay alguien que por siempre me ha de acompa˜ ar.
n

A mi hermana por saber demostrarme, sin intenciones, que la preparaci´ n nunca estar´ de
o a
m´ s, que la vida puede complicarse tanto como uno quiera y por ende existe la necesidad de
a
ser cada vez m´ s. A ti por ejemplo de lucha y rebeldá.
a ı

A los tós tecn´ logos que nunca han dejado de invertir ni de creer en mi. A ustedes sin quienes
ı o
no hubiera sido posible llegar a este momento. Entre economá, herramientas y confianza
ı
constante, ustedes me dieron siempre motivaci´ n y F´ para ser ejemplo y apostar con el mayor
o e
esfuerzo.

Al abuelo que siempre quiso un ingeniero y ahora se le hizo doctor. Le dedico este trabajo
que sin sus conocimientos y compa˜ á en el taller nunca hubiera tenido la integridad que lo
nı
caracteriza. A usted por ense˜ arme que la ingenierá no es una decisi´ n, sino una convicci´ n.
n ı o o

Finalmente, a la mujer que por su existencia es guá y voz divina. A ti que sabes decir y hacer
ı
lo que hace falta. A ti que complementas como ying y yang, como sol y luna, como piel
morena y cabellos rizados. A ti mi linda esposa por tu amor constante que nunca permiti´ o
tristezas ni en los peores momentos. Lo dedico por tu firme disposici´ n a dejar todo por vivir
o
´
y aprender cosas que nunca te imaginaste, por tu animo vivo por recorrer el mundo a mi lado.
A ti princesa por confiar en mi y acompa˜ arme en cada una de estas p´ ginas.
n a

iii

Acknowledgements

If the observer were intelligent (and extraterrestrial observers are always pre-
sumed to be intelligent) he would conclude that the earth is inhabited by a few
very large organisms whose individual parts are subordinate to a central direct-
ing force. He might not be able to find any central brain or other controlling unit,
but human biologists have the same difficulty when they try to analyse an ant
hill. The individual ants are not impressive objects in fact they are rather stupid,
even for insects but the colony as a whole behaves with striking intelligence. –
Jonathan Norton Leonard

I want to express my deepest feeling of gratitude to all of you who contributed for me to not
be an individual ant. Advisors, peers, friends, and the robotics gurus, which doubtfully will
read this but who surely deserve my gratitude because without them this work won’t even be
possible.

Thanks Prof. Rogelio Soto for your constant confidence in my ideas and for supporting and
guiding all my developments during this dissertation. Thanks for the opportunity you gave
me for working with you and developing that which I like the most and I doesn’t even knew
it existed.

Thanks Prof. Jos´ L. Gordillo for the hard times you gave me and for sharing your knowledge.
e
I really appreciate both things, definitively you make me a more integral professional.

Thanks Prof. Luiz Chaimowicz, for opening the research doors from the very first day. Thanks
for believing in my developments and letting me live a little of the amazing Brazilian experi-
ence. Thanks for your constant guidance even when we are more than 8000km apart. Thanks
for showing me my very first experiences around real robotics and for making me understand
that it is Skynet and not the Terminator which we shall fear.

Thanks eRobots friends and colleagues for not only sharing your knowledge and experiences
with me, but also for validating my own. Thanks for your constant support and company when
nobody else should be working. Thanks for your words when I needed them the most, you
really are a fundamental part of this work.

Thanks Prof. Mario Montenegro and the Verlabians for the most accurate and guided knowl-
edge I’ve ever had about mobile robotics. Thanks for giving me the chance to be part of your
team. Thanks for letting me learn from you and be your mexican friend even though I worked
with Windows.

Thanks God and Life for giving me this opportunity.

iv

by
Jes´ s Salvador Cepeda Barrera
u

Abstract

In recent years, the use of Multi-Robot Systems (MRS) has become popular for several appli-
cation domains. The main reason for using these MRS is that they are a convenient solution
in terms of costs, performance, efficiency, reliability, and reduced human exposure. In that
way, existing robots and implementation domains are of increasing number and complexity,
turning coordination and cooperation fundamental features among robotics research.
Accordingly, developing a team of cooperative autonomous mobile robots has been one
of the most challenging goals in artificial intelligence. Research has witnessed a large body
of significant advances in the control of single mobile robots, dramatically improving the
feasibility and suitability of MRS. These vast scientific contributions have also created the
need for coupling these advances, leading researchers to the challenging task of developing
multi-robot coordination infrastructures.
Moreover, considering all possible environments where robots interact, disaster scenar-
ios come to be among the most challenging ones. These scenarios have no specific structure
and are highly dynamic, uncertain and inherently hostile. They involve devastating effects
on wildlife, biodiversity, agriculture, urban areas, human health, and also economy. So, they
reside among the most serious social issues for the intellectual community.
Following these concerns and challenges, this dissertation addresses the problem of how
can we coordinate and control multiple robots so as to achieve cooperative behavior for assist-
ing in disaster and emergency response. The essential motivation resides in the possibilities
that a MRS can have for disaster response including improved performance in sensing and
action, while speeding up operations by parallelism. Finally, it represents an opportunity for
empowering responders’ abilities and efficiency in the critical 72 golden hours, which are
essential for increasing the survival rate and for preventing a larger damage.
Therefore, herein we achieve urban search and rescue (USAR) modularization leverag-
ing local perceptions and mission decomposition into robotic tasks. Then, we have developed
a behavior-based control architecture for coordinating mobile robots, enhancing most relevant
control characteristics reported in literature. Furthermore, we have implemented a hybrid in-
frastructure in order to ensure robustness for USAR mission accomplishment with current
technology, which is better for simple, fast, reactive control. These single and multi-robot
architectures were designed under the service-oriented paradigm, thus leveraging reusability,
scalability and extendibility.
Finally, we have inherently studied the emergence of rescue robotic team behaviors and
their applicability in real disasters. By implementing distributed autonomous behaviors, we
observed the opportunity for adding adaptivity features so as to autonomously learn additional
behaviors and possibly increase performance towards cognitive systems.

v

List of Figures

1.1 Number of survivors and casualties in the Kobe earthquake in 1995. Image
from [267]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Percentage of survival chances in accordance to when victim is located. Based
on [69]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 70 years for autonomous control levels. Edited from [44]. . . . . . . . . . . . 6
1.4 Mobile robot control scheme. Image from [255]. . . . . . . . . . . . . . . . 9
1.5 Minsky’s interpretation of behaviors. Image from [188]. . . . . . . . . . . . 18
1.6 Classic and new artiﬁcial intelligence approaches. Edited from [255]. . . . . 18
1.7 Behavior in robotics control. Image from [138]. . . . . . . . . . . . . . . . . 19
1.8 Coordination methods for behavior-based control. Edited from [11]. . . . . . 19
1.9 Group architecture overview. . . . . . . . . . . . . . . . . . . . . . . . . . . 23
1.10 Service-oriented group architecture. . . . . . . . . . . . . . . . . . . . . . . 25

2.1 Major challenges for networked robots. Image from [150]. . . . . . . . . . . 30
2.2 Typical USAR Scenario. Image from [267]. . . . . . . . . . . . . . . . . . . 30
2.3 Real pictures from the WTC Tower 2. a) shows a rescue robot within the white
box navigating in the rubble; b) robots-eye view with three sets of victim
remains. Image edited from [194] and [193]. . . . . . . . . . . . . . . . . . 31
2.4 Typical problems with rescue robots. Image from [268]. . . . . . . . . . . . . 35
2.5 Template-based information system for disaster response. Image based on [156,
56]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.6 Examples of templates for disaster response. Image based on [156, 56]. . . . 42
2.7 Task force in rescue infrastructure. Image from [14]. . . . . . . . . . . . . . 43
2.8 Rescue Communicator, R-Comm: a) Long version, b) Short version. Image
from [14]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.9 Handy terminal and RFID tag. Image from [14]. . . . . . . . . . . . . . . . . 44
2.10 Database for Rescue Management System, DaRuMa. Edited from [210]. . . . 44
2.11 RoboCup Rescue Concept. Image from [270]. . . . . . . . . . . . . . . . . . 46
2.12 USARSim Robot Models. Edited from [284, 67]. . . . . . . . . . . . . . . . 47
2.13 USARSim Disaster Snapshot. Edited from [18, 17]. . . . . . . . . . . . . . . 47
2.14 Sensor Readings Comparison. Top: Simulation, Bottom: Reality. Image
from [67]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.15 Control Architecture for Rescue Robot Systems. Image from [3]. . . . . . . . 50
2.16 Coordinated exploration using costs and utilities. Frontier assignment consid-
ering a) only costs; b) costs and utilities; c) three robots paths results. Edited
from [58]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

vi

2.17 Supervisor sketch for MRS patrolling. Image from [168]. . . . . . . . . . . . 53
2.18 Algorithm for determining occupancy grids. Image from [33]. . . . . . . . . 54
2.19 Multi-Robot generated maps in RoboCup Rescue 2007. Image from [225]. . . 55
2.20 Behavioral mapping idea. Image from [164]. . . . . . . . . . . . . . . . . . . 55
2.21 3D mapping using USARSim. Left) Kurt3D and its simulated counterpart.
Right) 3D color-coded map. Edited from [20]. . . . . . . . . . . . . . . . . . 56
2.22 Face recognition in USARSim. Left) Successful recognition. Right) False
positive. Image from [20]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
2.23 Human pedestrian vision-based detection procedure. Image from [90]. . . . . 57
2.24 Human pedestrian vision-based detection procedure. Image from hal.inria.fr/inria-
00496980/en/. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
2.25 Human behavior vision-based recognition. Edited from [207]. . . . . . . . . 58
2.26 Visual path following procedure. Edited from [103]. . . . . . . . . . . . . . . 59
2.27 Visual path following tests in 3D terrain. Edited from [103]. . . . . . . . . . 59
2.28 START Algorithm. Victims are sorted in: Minor, Delayed, Immediate and
Expectant; based on the assessment of: Mobility, Respiration, Perfusion and
Mental Status. Image from [80]. . . . . . . . . . . . . . . . . . . . . . . . . 61
2.29 Safety, security and rescue robotics teleoperation stages. Image from [36]. . . 61
2.30 Interface for multi-robot rescue systems. Image from [209]. . . . . . . . . . . 62
2.31 Desired information for rescue robot interfaces: a)multiple image displays, b)
multiple map displays. Edited from [292]. . . . . . . . . . . . . . . . . . . . 63
2.32 Touch-screen technologies for rescue robotics. Edited from [185]. . . . . . . 64
2.33 MRS for autonomous exploration, mapping and deployment. a) the complete
heterogeneous team; b) sub-team with mapping capabilities. Image from [130]. 65
2.34 MRS result for autonomous exploration, mapping and deployment. a) origi-
nal ﬂoor map; b) robots collected map; c) autonomous planned deployment.
Edited from [130]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
2.35 MRS for search and monitoring: a) Piper J3 UAVs; b) heterogeneous UGVs.
Edited from [131]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
2.36 Demonstration of integrated search operations: a) robots at initial positions, b)
robots searching for human target, c) alert of target found, d) display nearest
UGV view of the target. Edited from [131]. . . . . . . . . . . . . . . . . . . 67
2.37 CRASAR MicroVGTV and Inuktun [91, 194, 158, 201]. . . . . . . . . . . . 70
2.38 TerminatorBot [282, 281, 204]. . . . . . . . . . . . . . . . . . . . . . . . . . 70
2.39 Leg-in-Rotor Jumping Inspector [204, 267]. . . . . . . . . . . . . . . . . . . 71
2.40 Cubic/Planar Transformational Robot [266]. . . . . . . . . . . . . . . . . . . 71
2.41 iRobot ATRV - FONTANA [199, 91, 158]. . . . . . . . . . . . . . . . . . . . 71
2.42 FUMA [181, 245]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
2.43 Darmstadt University - Monstertruck [8]. . . . . . . . . . . . . . . . . . . . 72
2.44 Resko at UniKoblenz - Robbie [151]. . . . . . . . . . . . . . . . . . . . . . 72
2.45 Independent [84]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
2.46 Uppsala University Sweden - Surt [211]. . . . . . . . . . . . . . . . . . . . . 73
2.47 Taylor [199]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
2.48 iRobot Packbot [91, 158]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
2.49 SPAWAR Urbot [91, 158]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

vii

2.50 Foster-Miller Solem [91, 194, 158]. . . . . . . . . . . . . . . . . . . . . . . 74
2.51 Shinobi - Kamui [189]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
2.52 CEO Mission II [277]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
2.53 Aladdin [215, 61]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
2.54 Pelican United - Kenaf [204, 216]. . . . . . . . . . . . . . . . . . . . . . . . 76
2.55 Tehzeeb [265]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
2.56 ResQuake Silver2009 [190, 187]. . . . . . . . . . . . . . . . . . . . . . . . 76
2.57 Jacobs Rugbot [224, 85, 249]. . . . . . . . . . . . . . . . . . . . . . . . . . 77
2.58 PLASMA-Rx [87]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
2.59 MRL rescue robots NAJI VI and NAJI VII [252]. . . . . . . . . . . . . . . . 77
2.60 Helios IX and Carrier Parent and Child [121, 180, 267]. . . . . . . . . . . . . 78
2.61 KOHGA : Kinesthetic Observation-Help-Guidance Agent [142, 181, 189, 276]. 78
2.62 OmniTread OT-4 [40]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
2.63 Hyper Souryu IV [204, 276]. . . . . . . . . . . . . . . . . . . . . . . . . . . 79
2.64 Rescue robots: a) Talon, b) Wolverine V-2, c) RHex, d) iSENSYS IP3, e)
Intelligent Aerobot, f) muFly microcopter, g) Chinese firefighting robot, h)
Teleoperated extinguisher, i) Unmanned surface vehicle, j) Predator, k) T-
HAWK, l) Bluefin HAUV. Images from [181, 158, 204, 267, 287]. . . . . . . 80
2.65 Jacobs University rescue arenas. Image from [249]. . . . . . . . . . . . . . . 81
2.66 Arena in which multiple Kenafs were tested. Image from [205]. . . . . . . . 82
2.67 Exploration strategy and centralized, global 3D map: a) frontiers in current
global map, b) allocation and path planning towards the best frontier, c) a
final 3D global map. Image from [205]. . . . . . . . . . . . . . . . . . . . . 82
2.68 Mapping data: a) raw from individual robots, b) fused and corrected in a new
global map. Image from [205]. . . . . . . . . . . . . . . . . . . . . . . . . . 83
2.69 Building exploration and temperature gradient mapping: a) robots as mobile
sensors navigating and deploying static sensors, b) temperature map. Image
from [144]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
2.70 Building structure exploration and temperature mapping using static sensors,
human mobile sensor, and UAV mobile sensor. Image from [98]. . . . . . . . 84
2.71 Helios IX in a door-opening procedure. Image from [121]. . . . . . . . . . . 85
2.72 Real model and generated maps of the 60 m. hall: a) real 3D model, b)
generated 3D map with snapshots, c) 2D map with CPS, d) 2D map with dead
reckoning. Image from [121]. . . . . . . . . . . . . . . . . . . . . . . . . . . 86
2.73 IRS-U and K-CFD real tests with rescue robots: a) deployment of Kohga
and Souryu robots, b) Kohga finding a victim, c) operator being notified of
victim found, d) Kohga waiting until human rescuer assists the victim, e)
Souryu finding a victim, f) Kohga and Souryu awaiting for assistance, g) hu-
man rescuers aiding the victim, and h) both robots continue exploring. Images
from [276]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
2.74 Types of entries in mine rescue operations: a) Surface Entry (SE), b) Borehole
Entry (BE), c) Void Entry (VE), d) Inuktun being deployed in a BE [201]. . . 89
2.75 Standardized test arenas for rescue robotics: a) Red Arena, b) Orange Arena,
c) Yellow Arena. Image from [67]. . . . . . . . . . . . . . . . . . . . . . . . 91

viii

3.1 MaSE Methodology. Image from [289]. . . . . . . . . . . . . . . . . . . . . 94
3.2 USAR Requirements (most relevant references to build this diagram include:
[261, 19, 80, 87, 254, 269, 204, 267, 268]). . . . . . . . . . . . . . . . . . . 96
3.3 Sequence Diagram I: Exploration and Mapping (most relevant references to
build this diagram include: [173, 174, 175, 176, 21, 221, 86, 232, 10, 58, 271,
101, 33, 240, 92, 126, 194, 204]). . . . . . . . . . . . . . . . . . . . . . . . . 99
3.4 Sequence Diagram IIa: Recognize and Identify - Local (most relevant refer-
ences to build this diagram include: [170, 175, 221, 23, 242, 163, 90, 207, 89,
226]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
3.5 Sequence Diagram IIb: Recognize and Identify - Remote (most relevant ref-
erences to build this diagram include: [170, 175, 221, 23, 242, 163, 90, 207,
89, 226]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.6 Sequence Diagram III: Support and Relief (most relevant references to build
this diagram include: [58, 33, 80, 19, 226, 150, 267, 204, 87, 254]). . . . . . . 102
3.7 Robots used in this dissertation: to the left a simulated version of an Adept
Pioneer 3DX, in the middle the real version of an Adept Pioneer 3AT, and to
the right a Dr. Robot Jaguar V2. . . . . . . . . . . . . . . . . . . . . . . . . 103
3.8 Roles, behaviors and actions mappings. . . . . . . . . . . . . . . . . . . . . 106
3.9 Roles, behaviors and actions mappings. . . . . . . . . . . . . . . . . . . . . 107
3.10 Behavior-based control architecture for individual robots. Edited image from [178].108
3.11 The Hybrid Paradigm. Image from [192]. . . . . . . . . . . . . . . . . . . . 109
3.12 Group architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
3.13 Architecture topology: at the top the system element communicating wireless
with the subsystems. Subsystems include their nodes, which can be differ-
ent types of computers. Finally, components represent the running software
services depending on the existing hardware and node’s capabilities. . . . . . 112
3.14 Microsoft Robotics Developer Studio principal components. . . . . . . . . . 114
3.15 CCR Architecture: when a message is posted into a given Port or PortSet,
triggered Receivers call for Arbiters subscribed to the messaged port in order
for a task to be queued and dispatched to the threading pool. Ports deﬁned as
persistent are concurrently being listened, while non-persistent are one-time
listened. Image from [137]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
3.16 DSS Architecture. The DSS is responsible for loading services and manag-
ing the communications between applications through the Service Forwarder.
Services could be distributed in a same host and/or through the network. Im-
age from [137]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
3.17 MSRDS Operational Schema. Even though DSS is on top of CCR, many
services access CCR directly, which at the same time is working on low level
as the mechanism for orchestration to happen, so it is placed sidewards to the
DSS. Image from [137]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

ix

3.18 Behavior examples designed as services. Top represents the handle collision
behavior, which according to a goal/current heading and the laser scanner sen-
sor, it evaluates the possible collisions and outputs the corresponding steering
and driving velocities. Middle represents the detection (victim/threat) behav-
ior, which according to the attributes to recognize and the camera sensor, it
implements the SURF algorithm and outputs a flag indicating if the object
has been found and the attributes that correspond. Bottom represents the seek
behavior, which according to a goal position, its current position and the laser
scanner sensor, it evaluates the best heading using the VFH algorithm and
then outputs the corresponding steering and driving velocities. . . . . . . . . 119

4.1 Process to Quick Simulation. Starting from a simple script in SPL we can
decide which is more useful for our robotic control needs and programming
skills, either going through C# or VPL. . . . . . . . . . . . . . . . . . . . . . 122
4.2 Created service for fast simulations with maze-like scenarios. Available at
http://erobots.codeplex.com/. . . . . . . . . . . . . . . . . . . . . . . . . . . 123
4.3 Fast simulation to real implementation process. It can be seen that going from
a simulated C# service to real hardware implementations is a matter of chang-
ing a line of code: the service reference. Concerning VPL, simulated and real
services are clearly identified providing easy interchange for the desired test. . 124
4.4 Local and remote approaches used for the experiments. . . . . . . . . . . . . 124
4.5 Speech recognition service experiment for voice-commanded robot naviga-
tion. Available at http://erobots.codeplex.com/. . . . . . . . . . . . . . . . . 125
4.6 Vision-based recognition service experiment for visual-joystick robot naviga-
tion. Available at http://erobots.codeplex.com/. . . . . . . . . . . . . . . . . 126
4.7 Wall-follow behavior service. View is from top, the red path is made of a robot
following the left (white) wall in the maze, while the blue one corresponds to
another robot following the right wall. . . . . . . . . . . . . . . . . . . . . . 127
4.8 Seek behavior service. Three robots in a maze viewed from the top, one static
and the other two going to specified goal positions. The red and blue paths
are generated by each one of the navigating robots. To the left of the picture a
simple console for appreciating the VFH [41] algorithm operations. . . . . . 127
4.9 Flocking behavior service. Three formations (left to right): line, column and
wedge/diamond. In the specific case of 3 robots a wedge looks just like a
diamond. Red, green and blue represent the traversed paths of the robots. . . 128
4.10 Field-cover behavior service. At the top, two different global emergent behav-
iors for a same algorithm and same environment, both showing appropriate
field-coverage or exploration. At the bottom, in two different environments,
just one robot doing the same field-cover behavior showing its traversed path
in red. Appendix D contains complete detail on this behavior. . . . . . . . . . 128
4.11 Victim and Threat behavior services. Being limited to vision-based detection,
different figures were used to simulate threats and victims according to recent
literature [116, 20, 275, 207]. To recognize them, already coded algorithms
were implemented including SURF [26], HoG [90] and face-detection [279]
from the popular OpenCV [45] and EmguCV [96] libraries. . . . . . . . . . . 129

x

4.12 Simultaneous localization and mapping features for the MSRDS VSE. Robot
1 is the red path, robot 3 the green and robot 3 the blue. They are not only
mapping the environment by themselves, but also contributing towards a team
map. Nevertheless localization is a simulation cheat and laser scanners have
no uncertainty as they will have in real hardware. . . . . . . . . . . . . . . . 130
4.13 Subscription Process: MSRDS partnership is achieved in two steps: running
the subsystems and then running the high-level controller asking for subscrip-
tions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
4.14 Single robot exploration simulation results: a) 15% wandering rate and flat
zones indicating high redundancy; b) Better average results with less redun-
dancy using 10% wandering rate; c) 5% wandering rate shows little improve-
ments and higher redundancy; d) Avoiding the past with 10% wandering rate,
resulting in over 96% completion of a 200 sq. m area exploration for every
run using one robot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
4.15 Typical navigation for qualitative appreciation: a) The environment based
upon Burgard’s work in [58]; b) A second more cluttered environment. Snap-
shots are taken from the top view and the traversed paths are drawn in red.
For both scenarios the robot efficiently traverses the complete area using the
same algorithm. Black circle with D indicates deployment point. . . . . . . . 136
4.16 Autonomous exploration showing representative results in a single run for 3
robots avoiding their own past. Full exploration is completed at almost 3 times
faster than using a single robot, and the exploration quality shows a balanced
result meaning an efficient resources (robots) management. . . . . . . . . . . 137
4.17 Autonomous exploration showing representative results in a single run for 3
robots avoiding their own and teammates’ past. Results show more interfer-
ence and imbalance at exploration quality when compared to avoiding their
own past only. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
4.18 Qualitative appreciation: a) Navigation results from Burgard’s work [58]; b)
Our gathered results. Path is drawn in red, green and blue for each robot.
High similarity with a much simpler algorithm can be appreciated. Black
circle with D indicates deployment point. . . . . . . . . . . . . . . . . . . . 138
4.19 The emergent in-zone coverage behavior for long time running the exploration
algorithm. Each color (red, green and blue) shows an area explored by a
different robot. Black circle with D indicates deployment point. . . . . . . . 139
4.20 Multi-robot exploration simulation results, appropriate autonomous explo-
ration within different environments including: a) Open Areas; b) Cluttered
Environments; c) Dead-end Corridors; d) Minimum Exits. Black circle with
D indicates deployment point. . . . . . . . . . . . . . . . . . . . . . . . . . 140
4.21 Jaguar V2 operator control unit. This is the interface for the application where
autonomous operations occur including local perceptions and behaviors coor-
dination. Thus, it is the reactive part of our proposed solution. . . . . . . . . 142
4.22 System operator control unit. This is the interface for the application where
manual operations occur including state change and human supervision. Thus,
it is the deliberative part of our proposed solution. . . . . . . . . . . . . . . . 142
4.23 Template structure for creating and managing reports. Based on [156, 56]. . . 143

xi

4.24 Deployment of a Jaguar V2 for single robot autonomous exploration experi-
ments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
4.25 Autonomous exploration showing representative results implementing the ex-
ploration algorithm in one Jaguar V2. An average of 36 seconds for full ex-
ploration demonstrates coherent operations considering simulation results. . . 145
4.26 Deployment of two Jaguar V2 robots for multi-robot autonomous exploration
experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
4.27 Autonomous exploration showing representative results for a single run using
2 robots avoiding their own past. An almost half of the time for full explo-
ration when compared to single robot runs demonstrates efﬁcient resource
management. The resultant exploration quality shows the trend towards per-
fect balancing between the two robots. . . . . . . . . . . . . . . . . . . . . . 146
4.28 Comparison between: a) typical literature exploration process and b) our pro-
posed exploration. Clear steps and complexity reduction can be appreciated
between sensing and acting. . . . . . . . . . . . . . . . . . . . . . . . . . . 147

A.1 Generic single robot architecture. Image from [2]. . . . . . . . . . . . . . . . 154
A.2 Autonomous Robot Architecture - AuRa. Image from [12]. . . . . . . . . . . 155

D.1 8 possible 45◦ heading cases with 3 neighbor waypoints to evaluate so as to
deﬁne a CCW, CW or ZERO angular acceleration command. For example,
if heading in the -45◦ case, the neighbors to evaluate are B, C and D, as left,
center and right, respectively. . . . . . . . . . . . . . . . . . . . . . . . . . . 181
D.2 Implemented 2-state Finite State Automata for autonomous exploration. . . . 184

xii

List of Tables

1.1 Comparison of event magnitude. Edited from [182]. . . . . . . . . . . . . . . 7
1.2 Important concepts and characteristics on the control of multi-robot systems.
Based on [53, 11, 2, 24]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.3 FSA, FSM and BBC relationships. Edited from [192]. . . . . . . . . . . . . . 20
1.4 Components of a hybrid-intelligence architecture. Based on [192]. . . . . . . 21
1.5 Nomenclature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.6 Relevant metrics in multi-robot systems . . . . . . . . . . . . . . . . . . . . 23

2.1 Factors inﬂuencing the scope of the disaster relief effort from [83]. . . . . . . 40
2.2 A classiﬁcation of robotic behaviors. Based on [178, 223]. . . . . . . . . . . 51
2.3 Recommendations for designing a rescue robot [37, 184, 194, 33, 158, 201, 267]. 69

3.1 Main advantages and disadvantages for using wheeled and tracked robots [255,
192]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

4.1 Experiments’ results: average delays . . . . . . . . . . . . . . . . . . . . . . 133
4.2 Metrics used in the experiments. . . . . . . . . . . . . . . . . . . . . . . . . 134
4.3 Average and Standard Deviation for full exploration time in 10 runs using
Avoid Past + 10% wandering rate with 1 robot. . . . . . . . . . . . . . . . . 136
Avoid Past + 10% wandering rate with 3 robots. . . . . . . . . . . . . . . . . 137
Avoid Kins Past + 10% wandering rate with 3 robots. . . . . . . . . . . . . . 138

B.1 Comparison among different software systems engineering techniques [219,
46, 82, 293, 4]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

C.1 Wake up behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
C.2 Resume behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
C.3 Wait behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
C.4 Handle Collision behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
C.5 Avoid Past behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
C.6 Locate behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
C.7 Drive Towards behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
C.8 Safe Wander behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
C.9 Seek behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
C.10 Path Planning behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

xiii

C.11 Aggregate behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
C.12 Unit Center Line behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
C.13 Unit Center Column behavior. . . . . . . . . . . . . . . . . . . . . . . . . . 168
C.14 Unit Center Diamond behavior. . . . . . . . . . . . . . . . . . . . . . . . . . 168
C.15 Unit Center Wedge behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . 169
C.16 Hold Formation behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
C.17 Lost behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
C.18 Flocking behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
C.19 Disperse behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
C.20 Field Cover behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
C.21 Wall Follow behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
C.22 Escape behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
C.23 Report behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
C.24 Track behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
C.25 Inspect behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
C.26 Victim behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
C.27 Threat behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
C.28 Kin behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
C.29 Give Aid behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
C.30 Aid- behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
C.31 Impatient behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
C.32 Acquiescent behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
C.33 Unknown behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177

xiv

Contents

Abstract v

List of Figures xii

List of Tables xiv

1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Problem Statement and Context . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.1 Disaster Response . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.2 Mobile Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2.3 Search and Rescue Robotics . . . . . . . . . . . . . . . . . . . . . . 12
1.2.4 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.3 Research Questions and Objectives . . . . . . . . . . . . . . . . . . . . . . . 16
1.4 Solution Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.4.1 Dynamic Roles + Behavior-based Robotics . . . . . . . . . . . . . . 17
1.4.2 Architecture + Service-Oriented Design . . . . . . . . . . . . . . . . 20
1.4.3 Testbeds Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.5 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1.6 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2 Literature Review – State of the Art 28
2.1 Fundamental Problems and Open Issues . . . . . . . . . . . . . . . . . . . . 29
2.2 Rescue Robotics Relevant Software Contributions . . . . . . . . . . . . . . . 38
2.2.1 Disaster Engineering and Information Systems . . . . . . . . . . . . 38
2.2.2 Environments for Software Research and Development . . . . . . . . 45
2.2.3 Frameworks, Algorithms and Interfaces . . . . . . . . . . . . . . . . 49
2.3 Rescue Robotics Relevant Hardware Contributions . . . . . . . . . . . . . . 68
2.4 Testbed and Real-World USAR Implementations . . . . . . . . . . . . . . . 79
2.4.1 Testbed Implementations . . . . . . . . . . . . . . . . . . . . . . . . 81
2.4.2 Real-World Implementations . . . . . . . . . . . . . . . . . . . . . . 87
2.5 International Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

3 Solution Detail 93
3.1 Towards Modular Rescue: USAR Mission Decomposition . . . . . . . . . . 95
3.2 Multi-Agent Robotic System for USAR: Task Allocation and Role Assignment 98

xv

3.3 Roles, Behaviors and Actions: Organization, Autonomy and Reliability . . . 104
3.4 Hybrid Intelligence for Multidisciplinary Needs: Control Architecture . . . . 106
3.5 Service-Oriented Design: Deployment, Extendibility and Scalability . . . . . 113
3.5.1 MSRDS Functionality . . . . . . . . . . . . . . . . . . . . . . . . . 113

4 Experiments and Results 121
4.1 Setting up the path from simulation to real implementation . . . . . . . . . . 122
4.2 Testing behavior services . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
4.3 Testing the service-oriented infrastructure . . . . . . . . . . . . . . . . . . . 130
4.4 Testing more complete operations . . . . . . . . . . . . . . . . . . . . . . . 133
4.4.1 Simulation tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
4.4.2 Real implementation tests . . . . . . . . . . . . . . . . . . . . . . . 139

5 Conclusions and Future Work 148
5.1 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

A Getting Deeper in MRS Architectures 153

B Frameworks for Robotic Software 158

C Set of Actions Organized as Robotic Behaviors 162

D Field Cover Behavior Composition 178
D.1 Behavior 1: Avoid Obstacles . . . . . . . . . . . . . . . . . . . . . . . . . . 178
D.2 Behavior 2: Avoid Past . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
D.3 Behavior 3: Locate Open Area . . . . . . . . . . . . . . . . . . . . . . . . . 180
D.4 Behavior 4: Disperse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
D.5 Emergent Behavior: Field Cover . . . . . . . . . . . . . . . . . . . . . . . . 182

Bibliography 210

xvi

Chapter 1

Introduction

“One can expect the human race to continue attempting systems just within or
just beyond our reach; and software systems are perhaps the most intricate
and complex of man’s handiworks. The management of this complex craft
will demand our best use of new languages and systems, our best adaptation
of proven engineering management methods, liberal doses of common sense,
and a God-given humility to recognize our fallibility and limitations.”

– Frederick P. Brooks, Jr. (Computer Scientist)

C HAPTER O BJECTIVES
— Why this dissertation.
— What we are dealing with.
— What we are solving.
— How we are solving it.
— Where we are contributing.
— How the document is organized.

In recent years, the use of Multi-Robot Systems (MRS) has become popular for several
application domains such as military, exploration, surveillance, search and rescue, and even
home and industry automation. The main reason for using these MRS is that they are a
convenient solution in terms of costs, performance, efficiency, reliability, and reduced human
exposure to harmful environments. In that way, existing robots and implementation domains
are of increasing number and complexity, turning coordination and cooperation fundamental
features among robotics research [99].
Accordingly, developing a team of cooperative autonomous mobile robots with efficient
performance has been one of the most challenging goals in artificial intelligence. The co-
ordination and cooperation of MRS has involved state of the art problems such as efficient
navigation, multi-robot path planning, exploration, traffic control, localization and mapping,
formation and docking control, coverage and flocking algorithms, target tracking, individual
and team cognition, tasks’ analysis, efficient resource management, suitable communications,
among others. As a result, research has witnessed a large body of significant advances in
the control of single mobile robots, dramatically improving the feasibility and suitability of
cooperative robotics. These vast scientific contributions created the need for coupling these

1

CHAPTER 1. INTRODUCTION 2

advances, leading researchers to develop inter-robot communication frameworks. Finding a
framework for cooperative coordination of multiple mobile robots that ensures the autonomy
and the individual requirements of the involved robots has always been a challenge too.
Moreover, considering all possible environments where robots interact, disaster scenar-
ios come to be among the most challenging ones. These scenarios, either man-made or natu-
ral, have no specific structure and are highly dynamic, uncertain and inherently hostile. These
disastrous events like: earthquakes, floods, fires, terrorist attacks, hurricanes, trapped popu-
lations, or even chemical, biological, radiological or nuclear explosions(CBRN or CBRNE);
involve devastating effects on wildlife, biodiversity, agriculture, urban areas, human health,
and also economy. So, the rapidly acting to save lives, avoid further environmental damage
and restore basic infrastructure has been among the most serious social issues for the intellec-
tual community.
For that reason, technology-based solutions for disaster and emergency situations are
main topics for relevant international associations, which had created specific divisions for
research on this area such as IEEE Safety, Security and Rescue Robotics (IEEE SSRR)
and the RoboCup Rescue, both active since 2002. Therefore, this dissertation focuses on
an improvement for disaster response and recovery, encouraging the relationship between
multiple robots as an important tool for mitigating disasters by cooperation, coordination and
communication among them and human operators.

1.1 Motivation
Historically, rescue robotics began in 1995 with one of the most devastating urban disasters
in the 20th century: the Hanshin-Awajii earthquake in January 17th in Kobe, Japan. Accord-
ing to [267], this disaster claimed more than 6,000 human lives, affected more than 2 million
people, damaged more than 785,000 houses, direct damage costs were estimated above 100
billion USD, and death rates reached 12.5% in some regions. The same year robotics re-
searchers in the US pushed the idea of the new research field while serving as rescue workers
at the bombing of the Murrah federal building in Oklahoma City [91]. Then, the 9/11 events
consolidated the area by being the first known place in the world to have real implementations
of rescue robots searching for victims and paths through the rubble, inspecting structures, and
looking for hazardous materials [194]. Additionally, the 2005 World Disasters report [283]
indicates that between 1995 and 2004 more than 900,000 human lives were lost and direct
damage costs surpassed the 738 billion USD, just in urban disasters. Merely indicating that
something needs and can be done.
Furthermore, these incidents as well as other mentioned disasters can also put the res-
cuers at risk of injury or death. In Mexico City the 1985 earthquake killed 135 rescuers during
disaster response operations [69]. In the World Trade Center in 2001, 402 rescuers lost their
lives [184]. More recently in March 2011, in the nuclear disaster in Fukushima, Japan [227]
rescuers were not even allowed to enter the ravaged area because it implied critical radiation
exposure. So, the rescue task is dangerous and time consuming, with the risk of further prob-
lems arising on the site [37]. To reduce these additional risks to the rescuers and victims,
the search is carried out slowly and delicately provoking a direct impact on the time to locate


survivors. Typically, the mortality rate increases and peaks the second day, meaning that sur-
vivors who are not located in the first 48 hours after the event are unlikely to survive beyond
a few weeks in the hospital [204]. Figure 1.1 shows the survivors rescued in the Kobe earth-
quake. As can be seen, beyond the third day there are almost no more victims rescued. Then,
Figure 1.2 shows the average survival chances in a urban disaster according to the days after
the incident. It can be appreciated that after the first day the chances of surviving are dramati-
cally decreased by more than 40%, and also after the third day another critical decrease shows
no more than 30% chances of surviving. So, there is a clear urgency for rescuers in the first
3 days where chances are good for raising survival rate, thus giving definition to the popular
term among rescue teams of “72 golden hours”.

Figure 1.1: Number of survivors and casualties in the Kobe earthquake in 1995. Image
from [267].

Figure 1.2: Percentage of survival chances in accordance to when victim is located. Based
on [69].

Consequently, real catastrophes and international contributions within the IEEE SSRR
and the RoboCup Rescue lead researchers to define the main usage of robotics in the so called


Urban Search and Rescue (USAR) missions. The essence of USAR is to save lives but,
Robin Murphy and Satoshi Tadokoro, two of the major contributors in the area, refer the
following possibilities for robots operating in urban disasters [204, 267]:

Search. Aimed to gather information on the disaster, locate victims, dangerous ma-
terials or any potential hazards in a faster way without increasing risks for secondary
damages.

Reconnaissance and mapping. For providing situational awareness. It is broader than
search in the way that it creates a reference of the ravaged zone in order to aid in the
coordination of the rescue effort, thus increasing the speed of the search, decreasing the
risk to rescue workers, and providing a quantitative investigation of damage at hand.

Rubble removal. Using robotics can be faster than manually and with a smaller foot-
print (e.g., exoskeletons) than traditional construction cranes.

Structural inspection. Providing better viewing angles at closer distances without ex-
posing the rescuers nor the survivors.

In-situ medical assessment and intervention. Since medical doctors may not be per-
mitted inside the critical ravaged area, called hot zone, robotic medical aid ranges from
verbal interactions, visual inspections and transporting medications; to complete sur-
vivors’ diagnosis and telemedicine. This is perhaps the most challenging task for robots.

Acting as a mobile beacon or repeater. Serve as landmark for localization and ren-
dezvous purposes or simply extending the wireless communication ranges.

Serving as a surrogate. Decreasing the risk to the rescue workers, robots may be used
as sensor extensions for enhancing rescuers’ perceptions enabling them to remotely
gather information of the zone and monitor other rescuers progress and needs.

Adaptively shoring unstable rubble. In order to prevent secondary collapse and avoid-
ing higher risks for rescuers and survivors.

Providing logistics support. Provide recovery actions and assistance by autonomously
transporting equipment, supplies and goods from storage areas to distribution points and
evacuation and assistance centres.

Instant deployment. Avoiding the initial overall evaluations for letting human rescuers
to go on site, robots can go instantly, thus improving speed of operations in order to raise
survival rate.

Other. General uses may suggest robots doing particular operations that are impossible
or difﬁcult to perform by humans, as they can enter smaller areas and operate without
breaks. Also, robots can operate for long periods in harsher conditions in a more ef-
ﬁcient way than humans do (e.g., they don’t need water or food, no need to rest, no
distractions, and the only fatigue is power running low).


In the same line, multi-agent robotic systems (MARS, or simply MRS) have inherent
characteristics that come to be of huge benefit for USAR implementations. According to [159]
some remarkable properties of these systems are:

Diversity. They apply to a large range of tasks and domains. Thus, they are a versatile
tool for disaster and emergency support where tasks are plenty.

Greater efficiency. In general, MRS exchanging information and cooperating tend to
be more efficient than a single robot.

Improved system performance. It has been demonstrated that multiple robots finish
tasks faster and more accurately than a single robot.

Fault tolerance. Using redundant units makes a system more tolerable to failures by
enabling possible replacements.

Robustness. By introducing redundancy and fault tolerance, a task is lesser compro-
mised and thus the system is more robust.

Lower economic cost. Multiple simpler robots are usually a better and more affordable
option than one powerful and expensive robot, essentially for research projects.

Ease of development. Having multiple agents allow developers to focus more pre-
cisely than when trying to have one almighty agent. This is helpful when the task is
as complex as disaster response.

Distributed sensing and action. This feature allows for better and faster reconnais-
sance while being more flexible and adaptable to the current situation.

Inherent parallelism. The use of multiple robots at the same time will inherently search
and cover faster than a single unit.

So, the essential motivation for developing this dissertation resides in the possibilities
and capabilities that a MRS can have for disaster response and recovery. As referred, there are
plenty of applications for rescue robotics and the complexity of USAR demands for multiple
robots. This multiplicity promises an improved performance in sensing and action that are
crucial in a disaster race against time. Also, it provides a way for speeding up operations
by addressing diverse tasks at the same time. Finally, it represents an opportunity for instant
deployment and for increasing the number of first responders in the critical 72 golden hours,
which are essential for increasing the survival rate and for preventing a larger damage.
Additionally, before getting into the specific problem statement, it is worth to refer that
choosing the option for multiple robots keeps developments herein aligned with international
state of the art trends as shown in Figure 1.3. Finally, this topic provides us with an insight
into social, life and cognitive sciences, which, in the end, are all about us.


Figure 1.3: 70 years for autonomous control levels. Edited from [44].

1.2 Problem Statement and Context
The purpose of this section is to narrow the research field into the specific problematic we
are dealing with. In order to do that, it is important to give a precise context on disasters and
hazards and about mobile robotics. Then we will be able to present an overview of search and
rescue robotics (SAR or simply rescue robotics) for finally stating the problem we address
herein.

1.2.1 Disaster Response
Everyday people around the world confront experiences that cause death, injuries, destroy per-
sonal belongings and interrupt daily activities. These incidents are known as accidents, crises,
emergencies, disasters, or catastrophes. Particularly, disasters are defined as deadly, destruc-
tive, and disruptive events that occur when hazards interact with human vulnerability [182].
The hazard comes to be the threat such as an earthquake, CBRNE, terrorist attack, among
others previously referred (a complete list of hazards is presented in [182]). This dissertation
focuses on aiding in emergencies and disasters such as Table 1.1 classifies.
Once a disaster has occurred, it changes with time through 4 phases that characterize the
emergency management according to [182, 267] and [204]. In spite of the description pre-
sented below, it is worth to refer that Mitigation and Preparedness are pre-incident activities,
whereas Response and Recover are post-incident. Particularly, disaster and emergency re-
sponse requires the capabilities of being as fast as possible for rescuing survivors and avoiding
any further damage, while being cautious and delicate enough to prevent any additional risk.
This dissertation is settled precisely in this phase, where the first responders’ post-incident
actions reside. The description of the 4 phases is now presented.

Ph. 1: Mitigation. Refers to disaster prevention and loss reduction.


Ph. 2: Preparedness. Efforts to increase readiness for a disaster.
Ph. 3: Response (Rescue). Actions immediately after the disaster for protecting lives and
property.
Ph. 4: Recovery. Actions to restore the basic infrastructure of the community or, preferably,
improved communities.

Table 1.1: Comparison of event magnitude. Edited from [182].
Accidents Crises Emergencies/ Calamities/ Catas-
Disasters trophes
Injuries few many scores hundreds/thousands
Deaths few many scores hundreds/thousands
Damage minor moderate major severe
Disruption minor moderate major severe
Geographic localized disperse disperse/diffuse disperse/diffuse
Impact
Availability abundant sufficient limited scarce
of Resources
Number of few many hundreds hundreds/thousands
Responders
Recovery minutes/ days/weeks months/years years/decades
Time hours/days

During the response phase search and rescue operations take place. In general, these
operations consist on activities such as looking for lost individuals, locating and diagnosing
victims, freeing extricated persons, providing first aids and basic medical care, and transport-
ing the victims away from the dangers. The human operational procedure that persists among
different disasters is described by D. McEntire in [182] as the following steps:
1) Gather the facts. Noticing just what happened, the estimated number of victims and
rescuers, type and age of constructions, potential environmental influence, presence of
other hazards or any detail for improving situational awareness.
2) Asses damage. Determine the structural damage in order to define the best actions basi-
cally including: entering with medical operation teams, evacuating and freeing victims,
or securing the perimeter.
3) Identify and acquire resources. Includes the need for goods, personnel, tools, equip-
ment and technology.
4) Establish rescue priorities. Determining the urgency of the situations for defining which
rescues must be done before others.
5) Develop a rescue plan. Who will enter the zone, how they will enter, which tools are
going to be needed, how they will leave, how to ensure safety for rescuers and victims;
all the necessary for following an strategy.


6) Conduct disaster and emergency response operations. Search and rescue, cover, fol-
low walls, analyse debris, listen for noises indicating survivors, develop everything that
is considered as useful for saving lives. According to [267], this step is the one that
takes the longest time.
7) Evaluate progress. Prevention of further damage demands for continuously monitor-
ing the situation including to see if the plan is working or there must be a better strategy.

In the described procedure, research has witnessed characteristic human behavior [182].
For example, typically the first volunteers to engage are untrained people. This provokes a
lack of skills that shows people willing to help but unable to handle equipments, coordinate
efforts, or develop any data entry or efficient resources administration and/or distribution. An-
other example is that there are emergent and spontaneous rescuers so that the number can be
overwhelming to manage, therefore causing division of labor and encountered priorities so
that some of them are willing to save relatives, friends and neighbors, without noticing other
possible survivors. Additionally, professional rescuers are not always willing to use volun-
teers in their own operations, thus from time to time, there are huge crowds with just a few
working hands. This situation leads into frustrations that compromise safeness of volunteers,
professional rescue teams, and victims, thus decreasing survival rates while increasing possi-
bilities for larger damages. The only good behavior that persists is that victims do cooperate
with each other and with rescuers during the search and rescue.
Consequently, we can think of volunteering rescue robotic teams for conducting the
search and rescue operations at step 6, which constitutes the most time-consuming disaster
response activities. Robots do not feel emotions such as preferences for relatives, they are
typically built for an specific task, and they will surely not become frustrated. Moreover,
robots have demonstrated to be highly capable for search and coverage, wall following, and
sensing under harsh environments. So, as R. Murphy et al. referred in [204]: there is a
particular need to start using robots in tactical search and rescue, which covers how the field
teams actually find, support, and extract survivors.

1.2.2 Mobile Robotics
Given the very broad definition of robot, it is important to state that we refer to the machine
that has sensors, a processing ability for emulating cognition and interpreting sensors’ signals
(perceive), and actuators in order to enable it to exert forces upon the environment to reach
some kind of locomotion, thus referring a mobile robot. When considering one single mobile
robot, designers must take into account at least an architecture upon which the robotic re-
sources are settled in order to interact with the real world. Then robotic control takes place as
a natural coupling of the hardware and software resources conforming the robotic system that
must develop an specified task. This robotic control has received huge amounts of contribu-
tions from the robotics community most them focusing in at least one of the topics presented
in Figure 1.4: perception and robot sensing (interpretation of the environment), localization
and mapping (representation of the environment), intelligence and planning, and mobility
control.
Furthermore, a good coupling of the blocks in Figure 1.4 shall result in mobile robots ca-
pable to develop tasks with certain autonomy. Bekey defines autonomy in [29] as: a systems’


Figure 1.4: Mobile robot control scheme. Image from [255].

capability of operating in the real-world environment without any form of external control
for extended periods of time; they must be able to survive dynamic environments, maintain
their internal structures and processes, use the environment to locate and obtain materials for
sustenance, and exhibit a variety of behaviors. This means that autonomous systems must
perform some task while, within limits, being able to adapt to environment’s dynamics. In
this dissertation special efforts towards autonomy including every block represented in Figure
1.4 are required.
Moreover, when considering multiple mobile robots there are additional factors that in-
tervene for having a successful autonomous system. First of all, the main intention of using
multiple entities is to have some kind of cooperation, thus it is important to deﬁne cooperative
behavior. Cao et al. in [63] refer that: “given some task speciﬁed by a designer a multiple-
robot system displays cooperative behavior if due to some underlying mechanism, there is an
increase in the total utility of the system”. So, pursuing this increase in utility (better perfor-
mance) cooperative robotics addresses major research axes [63] and coordination aspects [99]
presented below.

Group Architecture. This is the basic element of a multi-robot system, it is the persis-
tent structure allowing for variations at team composition such as the number of robots,
the level of autonomy, the levels of heterogeneity and homogeneity between them, and
the physical constraints. Similar to individual robot architectures, it refers to the set
of principles organizing the control system (collective behaviors) and determining its
capabilities, limitations and interactions (sensing, reasoning, communication and act-
ing constraints). Key features of a group architecture for mobile robots are: multi-level
control, centralization / decentralization, entities differentiation, communications, and
the ability to model other agents.


Resource Conflicts. This is perhaps the principal aspect concerning MRS coordination
(or control). Sharing of space, tasks and resources such as information, knowledge, or
hardware capabilities (e.g., cooperative manipulation), requires for coordination among
the actions of each robot in order for not interfering with each other, and end up devel-
oping autonomous, coherent and high-performance operations. This may additionally
require for robots taking into account the actions executed by others in order for being
more efficient and faster at task development (e.g., avoiding the typical issue of “every-
one going everywhere”). Typical resource conflicts also deal with the rational division,
distribution and allocation of tasks for achieving an specific goal, mission or global task.

Cooperation Level. This aspect considers specifically how robots are cooperating in
a given system. The usual is to have robots operating together towards a common
goal, but there is also cooperation through competitive approaches. Also, there are
types of cooperation called innate or eusocial, and intentional, which implies direct
communication through actions in the environment or messaging.

Navigation Problems. Inherent problems for mobile robots in the physical world in-
clude geometrical navigational issues such as path planning, formation control, pattern
generations, collision-avoidance, among others. Each robot in the team must have an
individual architecture for correct navigation, but it is the group architecture where nav-
igational control should be organized.

Adaptivity and Learning. This final element considers the capabilities to adapt to
changes in the environment or in the MRS in order to optimize task performance and
efficiently deal with dynamics and uncertainty. Typical approaches involve reinforce-
ment learning techniques for automatically finding the correct values for the control
parameters that will lead to a desired cooperative behavior, which can be a difficult and
time-consuming task for a human designer.

Perhaps the first important aspect this dissertation concerns is the implementation of a
group architecture that consolidates the infrastructure for a team of multiple robots for search
and rescue operations. For these means it is included in Appendix A a deeper context on this
topic. From those readings the following list of the characteristics that an architecture must
have for successful performance and relevance in a multi-disciplinary research area such as
rescue robotics, which involves rapidly-changing software and hardware technologies. So, an
appropriate group architecture must consider:

• Robotic task and domain independence.

• Robot hardware and software abstraction.

• Extendibility and scalability.

• Reusability.

• Simple upgrading.

• Simple integration of new components and devices.


• Simple debugging and prototyping.

• Support for parallelism.

• Support for modularity.

• Use of standardized tools.

These characteristics are fully considered in the implementations concerning this dis-
sertation and are detailed further in this document. What is more, the architectural design
involves the need for a coordination and cooperation mechanism for confronting the disaster
response requirements. This implies not only solving individual robot control problems but
also the resource conflicts and navigational problems that arise. For this means information
on robotic control is included.

Mobile Robots Control and Autonomy
A typical issue when defining robotic control is to find where it fits among robotic software.
According to [29] there are two basic perspectives: 1) Some designers refer exclusively to
robot motion control including maintaining velocities and accelerations at a given set point,
and orientation according to certain path. Also, they consider a “low-level” control for which
the key is to ensure steady-states, quick response time and other control theory aspects. 2) On
the other hand, other designers consider robotic control to the ability of the robot to follow
directions towards a goal. This means that planning a path to follow resides in a way of “high-
level” control that constantly sends the commands or directions to the robot control in order
to reach a defined goal. So, it turns difficult to find a clear division between each perspective.
Fortunately, a general definition for robotic control states that: “it is the process of
taking information about the environment, through the robot’s sensors, processing it as nec-
essary in order to make decisions about how to act, and then executing those actions in the
environment”– Matari´ [177]. Thus, robotic control typically requires the integration of mul-
c
tiple disciplines such as biology, control theory, kinematics, dynamics, computer engineering,
and even psychology, organization theory and economics. So, this integration implies the
need for multiple levels of control supporting the idea of the necessity for the individual and
group architectures.
Accordingly, from the two perspectives and the definition, we can refer that robotic
control happens essentially at two major levels for which we can embrace the concepts of
platform control and activity control provided by R. Murphy in [204]. The first one is the one
that moves the robot fluidly and efficiently through any given environment by changing (and
maintaining) kinematic variables such as velocity and acceleration. This control is usually
achieved with classic control theory such as PID controllers and thus can be classified as a
low-level control. The next level refers to the navigational control, which main concern is to
keep the robot operational in terms of avoiding collisions and dangerous situations, and to be
able to take the robot from one location to another. This control typically includes additional
problems such as localization and environment representation (mapping). So, generally it
needs to use other control strategies lying under artificial intelligence such as behavior-based
control and probabilistic methods, and thus being classified as a high-level control.


Consequently, we must clarify that this dissertation supposes that there is already a
robust, working low-level platform control for every robot. So, there is the need for developing
the high-level activity control for each unit and the whole MRS to operate in search and
rescue missions. In that way, this need for the activity control leads us to three major design
issues [159]:
1. It is not clear how a robot control system should be decomposed; meaning particular
problems at intra-robot control (individuals) that differ from inter-robot control (group).
2. The interactions between separate subsystems are not limited to directly visible connect-
ing links; interactions are also mediated via the environment so that emergent behavior
is a possibility.
3. As system complexity grows, the number of potential interactions between the compo-
nents of the system also grows.
Moreover, the control system must address and demonstrate characteristics presented in
Table 1.2. What is important to notice is that coordination of multi-robot teams in dynamic
environments is a very challenging task. Fundamentally, for having a successfully controlled
robotic team, every action performed by each robot during the cooperative operations must
take into account not only the robot’s perceptions but also its properties, the task requirements,
information flow, teammates’ status, and the global and local characteristics of the environ-
ment. Additionally, there must exist a coordination mechanism for synchronizing the actions
of the multiple robots. This mechanism should help in the exchange of necessary informa-
tion for mission accomplishment and task execution, as well as provide the flexibility and
reliability for efficient and robust interoperability.
Furthermore, for fulfilling controller needs, robotics community has been highly con-
cerned in creating standardized frameworks for developing robotic software. Since they are
significant for this dissertation, information on them is included in Appendix B, particularly
focusing in Service-Oriented Robotics (SOR). Robotic control as well as individuals and
group architectures must consider the service-oriented approach as a way of promoting its
importance and reusability capabilities. In this way, software development concerning this
dissertation turns to be capable of being implemented among different resources and circum-
stances and thus becoming a more interesting, relevant and portable solution with a better
impact.

1.2.3 Search and Rescue Robotics
Having explained briefs on disasters and mobile robots, it is appropriate to merge both re-
search fields and refer about robotics intended for disaster response. In spite of all the pre-
viously referred possibilities for robotics in search and rescue operations, this technology is
new and its acceptance as well as its hardware and software completeness will take time. Ac-
cording to [204], as of 2006, rescue robotics took place only in four major disasters: World
Trade Center, and hurricanes Katrina, Rita and Wilma. Also, in 2011, in the nuclear disaster
at Fukushima, Japan, robots were barely used because of problems such as mobility in harsh
environments where debris is scattered all over with tangled steel beams and collapsed struc-
tures, difficulties in communication because of thick concrete walls and lots of metal, and


Table 1.2: Important concepts and characteristics on the control of multi-robot systems. Based
on [53, 11, 2, 24].
Situatedness The robots are entities situated and surrounded by the real world. They
do not operate upon abstract representations.
Embodiment Each robot has a physical presence (a body). This has consequences in
its dynamic interactions with the world.
Reactivity The robots must take into account events with time bounds compatible
with the correct and efficient achievement of their goals.
Coherence Referring that robots should appear to an observer to have coherence of
actions towards goals.
Relevance / The active behavior should be relevant to the local situation residing on
Locality the robot’s sensors.
Adequacy / The behavior selection mechanism must go towards the mission accom-
Consistency plishment guided by their tasks’ objectives.
Representation The world aspect should be shared between behaviors and also trigger
for new behaviors.
Emergence Given a group of behaviors there is an inherent global behavior with
group and individual’s implications.
Synthesis To automatically derive a program for mission accomplishing.
Communication Increase performance by explicit information sharing.
Cooperation Proposing that robots should achieve more by operating together.
Interference Creation of protocols for avoiding unnecessary redundancies.
Density N number of robots should be able to do in 1 unit of time, what 1 robot
should in N units of time.
Individuality Interchangeability results in robustness because of repeatability or un-
necessary robots operating.
Learning / Automate the acquisition of new behaviors and the tuning and modifi-
Adaptability cation of existing ones according to the current situation.
Robustness The control should be able to exploit the redundancy of the processing
functions. This implies to be decentralized to some extent.
Programmability A useful robotic system should be able to achieve multiple tasks de-
scribed at an abstract level. Its functions should be easily combined
according to the task to be executed.
Extendibility Integration of new functions and definition of new tasks should be easy.
Scalability The approach should easily scale to any number of robots.
Flexibility The behaviors should be flexible to support many social patterns.
Reliability The robot can act correctly in any given situation over time.


physical presence within adverse environments because radiation affects electronics [227].
In short, the typical difficulty of sending robots inside major disasters is the need for a big
and slow robot that can overcome the referred challenges [217]. Not to mention the need
for robots capable of performing specific complex tasks like opening and closing doors and
valves, manipulating fire fighting hoses, or even carefully handling rubble to find survivors.
It is worth to mention that there are many types of robots proposed for search and rescue,
including robots that can withstand radiation and fire-fighter robots that shoot water to build-
ings, but the thing is that there is still not one all-mighty unit. For that reason, most typical
rescue robotics implementations in the United States and Japan reside in local incidents such
as urban fires, and search with unmanned vehicles (UxVs). In fact, most of the real implemen-
tations used robotics only as the eyes of the rescue teams in order to gather more information
from the environment as well as to monitor its conditions in order for better decision making.
And even that way, all the real operations allowed only for teleoperated robots and no auton-
omy at all [204]. Nevertheless, these real implementations are the ones responsible of having
a better understanding of the sensing and acting requirements as well as listing the possible
applications for robots in a search and rescue operation.
On the other hand, making use of the typical USAR scenarios where rescue robotics
research is implemented there are the contributions within the IEEE SSRR society and the
RoboCup Rescue. Main tasks include mobility and autonomy (act), search for victims and
hazards (sense), and simultaneous localization and mapping (SLAM) (reason). Also, human-
robot interactions have been deeply explored. The simulated software version of the RoboCup
Rescue has shown interesting contributions in exploration, mapping and victim detection al-
gorithms. Good sources describing some of these contributions can be found at [20, 19]. The
real testbed version has not only validated functionality of previously simulated contributions,
but also pushed the design of unmanned ground vehicles (UGVs) that show complex abilities
for mobility and autonomy. Also, it has leveraged the better usage of proprioceptive instru-
mentation for localization as well as exteroceptive instrumentation for mapping and victims
and hazards detection. Good examples of these contributions can be found at [224, 261].
So, even though the referred RoboCup contributions are simulated solutions far from
reaching a real disaster response operation, they are pushing the idea of having UGVs that can
enable rescuers to find victims faster as well as identifying possibilities for secondary damage.
Also, they are leveraging the possibility for other unmanned vehicles such as larger UGVs
that can be able to remove rubble faster than humans do, unmanned aerial vehicles (UAVs)
to extend the senses of the responders by providing a birds eye view of the situation, and
unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs) for similarly
extending and enhancing the rescuers’ senses [204].
In summary, some researchers are encouraging the development of practical technolo-
gies such as design of rescue robots, intelligent sensors, information equipment, and human
interfaces for assisting in urban search and rescue missions, particularly victim search, infor-
mation gathering, and communications [267]. Some other researchers are leveraging devel-
opments such as processing systems for monitoring and teleoperating multiple robots [108],
and creating expert systems on simple triage and rapid medical treatment of victims [80].
And there are few others intending the analysis and design of real USAR robot teams for
the RoboCup [261, 8], fire-fighting [206, 98], damaged building inspection [141], mine res-
cue [201], underwater exploration robots [203], and unmanned aerial systems for after-collapse


inspection [228]; but they are still in a premature phase not fully implemented and with no
autonomy at all. So, we can synthesize that researchers are addressing rescue robotics chal-
lenges in the following order of priority: mobility, teleoperation and wireless communica-
tions, human-robot interaction, and robotic cooperation [268]; and we can also refer that the
fundamental work is being leaded mainly by Robin Murphy, Satoshi Tadokoro, Andreas Birk,
among others (refer Chapter 2 for full details).
The truth is that there are a lot of open issues and fundamental problems in this barely
explored and challenging research field of rescue robotics. There is an explicit need for robots
helping to quickly locate, assess and even extricate victims who cannot be reached; and there
is an urgency for extending the rescuers’ ability to see and act in order to improve disaster
response operations, reduce risks of secondary damage, and even raise survival rates. Also,
there is an important number of robotics researchers around the globe focusing on particular
problems in the area, but there seems to be no direct (maybe less) effort towards generating
a collaborative rescue multi-robot system, which appears to be further in the future. In fact,
the RoboCup Rescue estimates a fully autonomous collaborative rescue robotic team by 2050,
which sounds pretty much as a reasonable timeline.

1.2.4 Problem Description
At this point we have presented several possibilities and problems that involve robotics for
disaster and emergency response. We have mentioned that robots come to fit well as rescuer
units for conducting search and rescue operations but several needs must be met. First we
defined the need for crafting an appropriate architecture for the individual robots as well as
for the complete multi-robot team. Next we added the necessity for appropriate robotic control
and the efficient coordination of units in order to take advantage of the inherent characteristics
of a MRS and be able to provide efficient and robust interoperability in dynamic environments.
Then we included the requirement for software design under the service-oriented paradigm.
Finally, we expressed that there is indeed a good number of relevant contributions using single
robots for search and rescue but that is not the case when using multiple robots. Thus, in
general the central problem this dissertation addresses is the following:

H OW DO WE COORDINATE AND CONTROL MULTIPLE ROBOTS SO AS TO ACHIEVE
COOPERATIVE BEHAVIOR FOR ASSISTING IN DISASTER AND EMERGENCY RE -
SPONSE , SPECIFICALLY, IN URBAN SEARCH AND RESCUE OPERATIONS ?

It has to be clear that this problem implies the use of multiple robotic agents working
together in a highly uncertain and dynamic environment where there are the special needs for
quick convergence, robustness, intelligence and efficiency. Also, even though the essential
purpose is to address navigational issues, other factors include: time, physical environmen-
tal conditions, communications management, security management, resources management,
logistics management, information management, strategy, and adaptivity [83]. So, we can
generalize by mentioning that the rescue robotic team must be prepared for navigating in
hostile dynamic environment where the time is critical, the sensitivity and multi-agent coop-
eration are crucial, and finally, strategy is vital to scope the efforts towards supporting human
rescuers to achieve faster and more secure USAR operations.

PhD Thesis - Coordination of Multiple Robotic Agents for Disaster and Emergency Response

PhD Thesis - Coordination of Multiple Robotic Agents for Disaster and Emergency Response

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