The document discusses the coordination of multiple robotic agents for disaster and emergency response, focusing on the development of a mobile robotic system to facilitate search and rescue operations. It outlines the problem statement, objectives, relevant implementations, and proposed solutions, including the design and experiments conducted. The study emphasizes the importance of innovative algorithms and multi-agent systems to enhance the efficacy and efficiency of rescue missions.

1. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Coordination of Multiple Robotic Agents for
Disasters and Emergency Response
A USAR First Responders Approach
Jes´s Salvador Cepeda Barrera
u
Tecnol´gico de Monterrey
o
November 22nd, 2012
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 1 / 81

2. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Outline
1 Introduction
Background and Motivation
Problem Statement and Objectives
2 State of the Art
Relevant Implementations
Standards and Significant Results
3 Solution Detail
MaSE: Analysis
MaSE: Design
4 Experiments and Results
Service-oriented Robotics (SOR)
More Sophisticated Operations
5 Conclusions
Summary of Contributions
Future Work
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 2 / 81

3. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Outline
1 Introduction
Background and Motivation
Problem Statement and Objectives
2 State of the Art
Relevant Implementations
Standards and Significant Results
3 Solution Detail
MaSE: Analysis
MaSE: Design
4 Experiments and Results
Service-oriented Robotics (SOR)
More Sophisticated Operations
5 Conclusions
Summary of Contributions
Future Work
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 3 / 81

4. World Risk Report
Figure: World Risk Report 2012.

5. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Background and Motivation
Rescue Robotics, Robots for Disaster Response
Application Domains
Definition
Search and Rescue
The essence of USAR is to save lives but, other
possibilities include [35, 49]:
search, reconnaissance and mapping,
rubble removal, structural inspection,
in-situ medical assessment and intervention,
acting as a mobile beacon or repeater,
serving as a surrogate,
adaptively shoring unstable rubble,
logistics support, instant deployment,
among other human-impossible tasks.
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 5 / 81

6. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Background and Motivation
Disaster Response
Persistent Disaster Response Operational Procedure [31, 49, 35].
1) Gather the facts.
2) Asses damage.
3) Identify and acquire resources.
4) Establish rescue priorities.
5) Develop a rescue plan.
6) Conduct the search and rescue operations.
Search , cover , follow walls , analyse debris ,
listen for survivors , develop everything that is considered
as useful for saving lives. According to [49], this step is
the one that takes the longest time.
7) Evaluate progress.
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o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 6 / 81

7. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Background and Motivation
72-Golden Hours for Robotic Usage
Figure: A typical behavior in victim
localization [49].
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 7 / 81

8. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Background and Motivation
72-Golden Hours for Robotic Usage
Figure: A typical behavior in victim
localization [49].
Figure: Autonomy trends towards 2015 [7].
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 7 / 81

9. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Problem Statement and Objectives
Problem Statement
How do we coordinate and control multiple robots so as to
achieve cooperative behavior for assisting in disaster and
emergency response?
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 8 / 81

10. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Problem Statement and Objectives
General Objective
Create a MRS capable of developing USAR operations in-
cluding the individuals and group control architectures,
as well as creating the computational algorithms for their
efficient interoperability towards mission completion.
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 9 / 81

11. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Problem Statement and Objectives
Particular Objectives
1 Modularize search and rescue missions.
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12. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Problem Statement and Objectives
Particular Objectives
1 Modularize search and rescue missions.
2 Determine the basic control structure for each agent
in the multi-agent robotic system.
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 10 / 81

13. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Problem Statement and Objectives
Particular Objectives
1 Modularize search and rescue missions.
2 Determine the basic control structure for each agent
in the multi-agent robotic system.
3 Create a distributed system structure for
coordination and control of a MRS for USAR.
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 10 / 81

14. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Problem Statement and Objectives
Particular Objectives
1 Modularize search and rescue missions.
2 Determine the basic control structure for each agent
in the multi-agent robotic system.
3 Create a distributed system structure for
coordination and control of a MRS for USAR.
4 Develop innovative algorithms and computational
models for mobile robots coordination and
cooperation towards USAR operations.
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 10 / 81

15. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Problem Statement and Objectives
Particular Objectives
1 Modularize search and rescue missions.
2 Determine the basic control structure for each agent
in the multi-agent robotic system.
3 Create a distributed system structure for
coordination and control of a MRS for USAR.
4 Develop innovative algorithms and computational
models for mobile robots coordination and
cooperation towards USAR operations.
5 Create the mechanism for synchronization of the MRS
actions in order to go coherently and efficiently
towards mission accomplishment.
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 10 / 81

16. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Problem Statement and Objectives
Particular Objectives
1 Modularize search and rescue missions.
2 Determine the basic control structure for each agent
in the multi-agent robotic system.
3 Create a distributed system structure for
coordination and control of a MRS for USAR.
4 Develop innovative algorithms and computational
models for mobile robots coordination and
cooperation towards USAR operations.
5 Create the mechanism for synchronization of the MRS
actions in order to go coherently and efficiently
towards mission accomplishment.
6 Demonstrate results.
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 10 / 81

17. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Outline
1 Introduction
Background and Motivation
Problem Statement and Objectives
2 State of the Art
Relevant Implementations
Standards and Significant Results
3 Solution Detail
MaSE: Analysis
MaSE: Design
4 Experiments and Results
Service-oriented Robotics (SOR)
More Sophisticated Operations
5 Conclusions
Summary of Contributions
Future Work
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 11 / 81

18. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Relevant Implementations
Real Implementations: No significant results.
Figure: 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 [33] and [32] Figure: Mine rescue: a) (SE),
b)(BE), c) (VE), d) Inuktun in a
BE [34].
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 12 / 81

19. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Relevant Implementations
A Particular Need for Rescue Robots
Rescue Robots [35, 49]
Built for specific purposes,
Witnessed Human Errors [31]. Robots do not look for
Untrained volunteers, relatives,
Too many volunteers, Instant deployable,
Encountered priorities, No emotions, no frustrations,
Bureaucracy/Formalities, Usually expendable,
Emotions, frustrations, ... Highly capable for search
and coverage, wall following,
sensing under harsh
environments, ...
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 13 / 81

20. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Relevant Implementations
Testbed Implementations
Figure: MRS for search and
monitoring: a) Piper J3 UAVs; b) Figure: Demonstration of integrated
heterogeneous UGVs. Edited search operations: a) robots at
from [23] initial positions, b) robots searching
for human target, c) alert of target
found, d) display nearest UGV view
of the target. Edited from [23]
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 14 / 81

21. Open Issues
Research Challenges [35, 49, 47]
Mobility
Power
Sensors and Perceptions
Suitable Communications
Localization and Mapping
Control Infrastructures
Autonomy Levels
Human-Robot Interfacing
Cooperation
Figure: Major challenges for networked Task Allocation
robots. Image from [24].
Resource Management
Strategies
Performance Metrics
Components’ Performance

22. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Standards and Significant Results
USAR International Standards: RoboCup Rescue
Figure: Standardized test arenas for
rescue robotics: a) Red Arena, b)
Orange Arena, c) Yellow Arena.
Image from [9]
Figure: Standardized evaluations
provided by the NIST [38].
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 16 / 81

23. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Standards and Significant Results
Coordination, Localization and Mapping
Figure: Coordinated Figure: Real model
exploration using Figure: Visual and generated maps
costs and utilities. localization and path of a 60 m. hall using
Edited from [8] following. Edited a MRS.Image
from [20] from [21].
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 17 / 81

24. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Standards and Significant Results
Recognition and User Interfacing
Figure: Human and human-behavior Figure: Interface for multi-robot
vision-based recognition. Edited rescue systems. Image from [37]
from [17, 36]
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u Tecnol´gico de Monterrey
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 18 / 81

25. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Standards and Significant Results
Roadmap to 2015 [49]
Unmanned vehicles will be more capable to search and
gather information from disasters.
HRI: augmented autonomy and intelligence on robots.
Robots should be able to enter the rubble and navigate over and
inside the debris.
Robot emergency diagnosis of victims should be possible as well as
3D mapping in real time.
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 19 / 81

26. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Outline
1 Introduction
Background and Motivation
Problem Statement and Objectives
2 State of the Art
Relevant Implementations
Standards and Significant Results
3 Solution Detail
MaSE: Analysis
MaSE: Design
4 Experiments and Results
Service-oriented Robotics (SOR)
More Sophisticated Operations
5 Conclusions
Summary of Contributions
Future Work
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 20 / 81

27. Multi-Agent Systems Engineering: MaSE [51]

28. Capturing Goals
Figure: USAR Requirements [46, 3, 13, 15, 44, 48, 35, 49, 47]

29. Applying Use Cases: SD-I
Figure: Sequence Diagram I: Explore and
Cover [27, 28, 29, 30, 4, 39, 14, 41, 1, 8, 50, 19, 6, 42, 18, 22, 33, 35]

30. Applying Use Cases: SD-IIa
Figure: Sequence Diagram IIa: Recognize and
Identify [26, 29, 39, 5, 43, 25, 17, 36, 16, 40]

31. Applying Use Cases: SD-IIb
Figure: Sequence Diagram IIb: Recognize and
Identify [26, 29, 39, 5, 43, 25, 17, 36, 16, 40]

32. Applying Use Cases: SD-III
Figure: Sequence Diagram III: Support and
Relief [8, 6, 13, 3, 40, 24, 49, 35, 15, 44]

33. Roles and Agent Classes I

34. Roles and Agent Classes II

35. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
MaSE: Design
Design as a Service
Advantages
Modularity and scalability.
Compositional functionality.
Organized, simple abstraction.
Manageability of heterogeneity.
Ease of integrating new robots.
Inherent distributed structure.
Fully meshed data interchange.
Support dynamic discovery.
Rapid prototyping.
Highly reusable/upgradeable.
Figure: Service-oriented design.
Devices and language
independent.
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 29 / 81

36. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
MaSE: Design
Behaviors as a Service
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o
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37. Agent Classes
Figure: Generic robot architecture overview.
Figure: Coordination methods for behavior-based control. Edited from [2].

38. Assembling Agent Classes
Figure: Generic group architecture overview.

39. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
MaSE: Design
2-leveled Hybrid Coordination: FSA + BBC
Figure: Classic and new artificial intelligence approaches. Edited from [45].
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 33 / 81

40. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
MaSE: Design
Implementations and Communications Topology
Figure: Service-oriented group architecture [12].
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 34 / 81

41. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Outline
1 Introduction
Background and Motivation
Problem Statement and Objectives
2 State of the Art
Relevant Implementations
Standards and Significant Results
3 Solution Detail
MaSE: Analysis
MaSE: Design
4 Experiments and Results
Service-oriented Robotics (SOR)
More Sophisticated Operations
5 Conclusions
Summary of Contributions
Future Work
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 35 / 81

42. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Robotic Resources
Figure: Robotic platforms used in experiments: MobileRobots Simulated
Pioneer 3DX and Pioneer 3AT, and Dr. Robot Jaguar V2.
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43. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Service-oriented Robotics (SOR)
Exploiting SOR, Local & Remote DSS
Figure: Local and remote data interchange.
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 37 / 81

44. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Service-oriented Robotics (SOR)
Flexible Integrated Service, Fast Prototyping
Figure: Highly transparent simulation to reality [10].
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 38 / 81

45. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Service-oriented Robotics (SOR)
Testing Multiple Service Providers
Figure: Developed tests with off-the-shelf provided services [10].
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 39 / 81

46. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Service-oriented Robotics (SOR)
Testing the Network
Figure: Subscription process and messaging in the proposed architecture [12].
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u Tecnol´gico de Monterrey
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 40 / 81

47. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Service-oriented Robotics (SOR)
Testing Primitive Behaviors Services
Figure: Object recognition/track/approach
for victim/threat behaviors.
Figure: Wall Following, Seek and Disperse
behaviors.
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 41 / 81

48. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Service-oriented Robotics (SOR)
Testing Composite Behaviors Services
Figure: Flocking and Exploration behaviors (Video). [11]
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 42 / 81

49. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Service-oriented Robotics (SOR)
Interesting Observation, Complexity Reduction
Figure: Comparison between a) most popular literature
and b) our behavior-based autonomous exploration. [11]
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u Tecnol´gico de Monterrey
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 43 / 81

50. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Service-oriented Robotics (SOR)
An effective behavior, Avoid Past
Figure: 8 possible 45◦ heading cases with 3 neighbor waypoints to
evaluate so as to define a CCW, CW or ZERO angular acceleration
command. Search for visited waypoints is done through a hashtable, thus
producing an algorithm complexity of O(1). [11]
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 44 / 81

51. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Service-oriented Robotics (SOR)
The coordination behavior, Disperse
Figure: Disperse behavior activates just in the case two or more robots
get into a predefined comfort zone. Thus, for m robots near in a pool of
n robots, where m ≤ n, we call for appropriate dispersion action
concerning an algorithm complexity of O(m2 ). [11]
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u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 45 / 81

52. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Service-oriented Robotics (SOR)
The emergent behavior, Explore
Figure: Using a Finite State Automata (FSA) we achieve our Explore
emergent behavior, where we fuse the outputs of the triggered
behaviors. [11]
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 46 / 81

53. Autonomous Exploration Robustness
Figure: Qualitative appreciation for autonomous exploration across different scenarios. [11]

54. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
More Sophisticated Operations
Interesting Results: Single Robot
Figure: Autonomous exploration results using one robot. [11]
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55. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
More Sophisticated Operations
Interesting Results: Multi-Robot (1)
Figure: Autonomous exploration results using multiple robots. [11]
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 49 / 81

56. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
More Sophisticated Operations
Interesting Results: Multi-Robot (2)
Figure: Autonomous exploration results using multiple robots. [11]
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57. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
More Sophisticated Operations
Subsystems Control Service
Figure: Operator Control Unit (OCU) for robot control.
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58. System Control Service
Figure: Operator Control Unit (OCU) for robot coordination.

59. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
More Sophisticated Operations
Localization
Figure: Localization service.
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 53 / 81

60. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
More Sophisticated Operations
Single Robot Exploration
Figure: Single Robot Real Implementations Results (Video). [11]
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 54 / 81

61. Multi-Robot Exploration
Figure: Multi-Robot Real Implementations Results (Video). [11]

62. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Outline
1 Introduction
Background and Motivation
Problem Statement and Objectives
2 State of the Art
Relevant Implementations
Standards and Significant Results
3 Solution Detail
MaSE: Analysis
MaSE: Design
4 Experiments and Results
Service-oriented Robotics (SOR)
More Sophisticated Operations
5 Conclusions
Summary of Contributions
Future Work
Jes´s Salvador Cepeda Barrera
u Tecnol´gico de Monterrey
o
Coordination of Multiple Robotic Agents for Disasters and Emergency Response 56 / 81

63. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Summary of Contributions
Summary of Contributions (1)
Main Contributions
Identified USAR requirements and modularization into fundamen-
tal tasks accommodated in generic sequence diagrams for USAR
operations.
Created primitive and composite, service-oriented behaviors and
coupled them in a behavior-based architecture for controlling indi-
vidual agents.
Implemented a hybrid, generic infrastructure that served as the
distributed, semi-autonomous, robotic coordinator for coupling
the MRS.
Inherently studied the emergence of rescue robotic team behav-
iors and their applicability in real disasters. Includes an effective
algorithm for single and multi-robot autonomous exploration.
A profound literature review.
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 57 / 81

64. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Summary of Contributions
Summary of Contributions (2)
The Autonomous Exploration Algorithm
Coordinating without any bidding/negotiation process, and without
requiring any sophisticated targeting/mapping technique.
No need for a-priori knowledge of the environment and without cal-
culating explicit resultant forces.
No need for static roles neither relay robots so that we are free of
leaving line-of-sight, and we are not depending on every robot’s func-
tionality for task completion.
Decreases computational complexity from typical O(n2 T ) (n robots,
T frontiers) to O(1) when robots are dispersed and O(m2 ) whenever
m robots need to disperse.
Robust across multiple adverse scenarios.
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 58 / 81

65. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Summary of Contributions
Summary of Contributions (3)
Literature
Cepeda, J. S.; Chaimowicz, L. & Soto, R. Exploring Microsoft
Robotics Studio as a Mechanism for Service-Oriented Robotics
Latin American Robotics Symposium and Intelligent Robotics Meet-
ing, IEEE Computer Society, 2010 , 0, 7-12. [10]
Cepeda, J. S.; Soto, R.; Gordillo, J. & Chaimowicz, L. Towards a
Service-Oriented Architecture for Teams of Heterogeneous Au-
tonomous Robots Artificial Intelligence (MICAI), 2011 10th Mexican
International Conference on, 2011 , 102-108. [12]
Cepeda, J. S.; Chaimowicz, L.; Soto, R.; Gordillo, J.; Alan´
ıs-Reyes, E.
& Carrillo-Arce, L. C. A Behavior-Based Strategy for Single and
Multi-Robot Autonomous Exploration Sensors, 2012 , Special Is-
sue: New Trends towards Automatic Vehicle Control and Perception
Systems. [11]
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 59 / 81

66. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Future Work
Future Work
Enhanced Autonomy & Complete Deployments
Implement better 2D and 3D localization methods.
Develop complete Explore + Recognize + Support tests.
Enable for autonomous state transitions at coordinator.
Take advantage in SOR capabilities so as to explore far reaches, add
more robots and/or behaviors.
Develop support behaviors such as helping with a victim/threat.
Provide adaptivity and learning capabilities, additional metrics: task
effectiveness, task time development, task time communicating, task
coverage, robotic resources fan out, RBA statistics, number of
targets/reports...
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 60 / 81

67. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
Future Work
Closing Remark
There is still a long way in terms of mobility, uncertainty and 3D loca-
tions management; all essential for implementing a MRS. Nevertheless,
by providing these alternative approaches we can have a good resource
for evaluation purposes that will lead us to address complex problems and
effectively resolve them the way they are.
In the end, we think that if more people start working with this trend
of SOA-based robotics and thus more service independent providers are
active, robotics research could step forward in a faster and more effective
way with more sharing of solutions.
You end up with a tremendous respect for a human being if
you’re a roboticist. Joseph Engelberger, quoted in Robotics
Age, 1985.
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Coordination of Multiple Robotic Agents for Disasters and Emergency Response 61 / 81

68. Thank you !
You can find a copy of this thesis at: http://goo.gl/WUYS3
You can find this thesis’ behavior services and more at:
http://erobots.codeplex.com/

69. Introduction State of the Art Solution Detail Experiments and Results Conclusions References
References I
R.C. Arkin and J. Diaz.
Line-of-sight constrained exploration for reactive multiagent robotic
teams.
In Advanced Motion Control, 2002. 7th International Workshop on,
pages 455 – 461, 2002.
Ronald C. Arkin.
Behavior-Based Robotics.
The MIT Press, 1998.
S. Balakirsky, Stefano Carpin, Alexander Kleiner, Michael Lewis,
Arnoud Visser, Jijun Wang, and Vittorio Amos Ziparo.
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