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COMRADES

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This presentation is the original version used to obtain my PhD degree in IT&C with major in Intelligent Systems.

This presentation is the original version used to obtain my PhD degree in IT&C with major in Intelligent Systems.

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  • 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, 2012Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 1 / 81
  • 2. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesOutline 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 WorkJes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 2 / 81
  • 3. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesOutline 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 WorkJes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination 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 ReferencesBackground and MotivationRescue 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.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 5 / 81
  • 6. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesBackground and MotivationDisaster 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.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 6 / 81
  • 7. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesBackground and Motivation72-Golden Hours for Robotic UsageFigure: A typical behavior in victimlocalization [49].Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 7 / 81
  • 8. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesBackground and Motivation72-Golden Hours for Robotic UsageFigure: A typical behavior in victimlocalization [49]. Figure: Autonomy trends towards 2015 [7].Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 7 / 81
  • 9. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesProblem Statement and ObjectivesProblem Statement How do we coordinate and control multiple robots so as to achieve cooperative behavior for assisting in disaster and emergency response?Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 8 / 81
  • 10. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesProblem Statement and ObjectivesGeneral 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.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 9 / 81
  • 11. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesProblem Statement and ObjectivesParticular Objectives 1 Modularize search and rescue missions.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 10 / 81
  • 12. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesProblem Statement and ObjectivesParticular 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 oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 10 / 81
  • 13. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesProblem Statement and ObjectivesParticular 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 oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 10 / 81
  • 14. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesProblem Statement and ObjectivesParticular 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 oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 10 / 81
  • 15. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesProblem Statement and ObjectivesParticular 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 oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 10 / 81
  • 16. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesProblem Statement and ObjectivesParticular 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 oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 10 / 81
  • 17. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesOutline 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 WorkJes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 11 / 81
  • 18. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesRelevant ImplementationsReal 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 oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 12 / 81
  • 19. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesRelevant ImplementationsA 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, ...Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 13 / 81
  • 20. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesRelevant ImplementationsTestbed 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]Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination 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 CooperationFigure: Major challenges for networked Task Allocationrobots. Image from [24]. Resource Management Strategies Performance Metrics Components’ Performance
  • 22. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesStandards and Significant ResultsUSAR 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].Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 16 / 81
  • 23. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesStandards and Significant ResultsCoordination, 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].Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 17 / 81
  • 24. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesStandards and Significant ResultsRecognition 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]Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 18 / 81
  • 25. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesStandards and Significant ResultsRoadmap 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.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 19 / 81
  • 26. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesOutline 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 WorkJes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination 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 ReferencesMaSE: DesignDesign 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.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 29 / 81
  • 36. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesMaSE: DesignBehaviors as a ServiceJes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 30 / 81
  • 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 ReferencesMaSE: Design2-leveled Hybrid Coordination: FSA + BBC Figure: Classic and new artificial intelligence approaches. Edited from [45].Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 33 / 81
  • 40. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesMaSE: DesignImplementations and Communications Topology Figure: Service-oriented group architecture [12].Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 34 / 81
  • 41. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesOutline 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 WorkJes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 35 / 81
  • 42. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesRobotic Resources Figure: Robotic platforms used in experiments: MobileRobots Simulated Pioneer 3DX and Pioneer 3AT, and Dr. Robot Jaguar V2.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 36 / 81
  • 43. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesService-oriented Robotics (SOR)Exploiting SOR, Local & Remote DSS Figure: Local and remote data interchange.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 37 / 81
  • 44. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesService-oriented Robotics (SOR)Flexible Integrated Service, Fast Prototyping Figure: Highly transparent simulation to reality [10].Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 38 / 81
  • 45. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesService-oriented Robotics (SOR)Testing Multiple Service Providers Figure: Developed tests with off-the-shelf provided services [10].Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 39 / 81
  • 46. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesService-oriented Robotics (SOR)Testing the Network Figure: Subscription process and messaging in the proposed architecture [12].Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 40 / 81
  • 47. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesService-oriented Robotics (SOR)Testing Primitive Behaviors Services Figure: Object recognition/track/approach for victim/threat behaviors.Figure: Wall Following, Seek and Dispersebehaviors.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 41 / 81
  • 48. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesService-oriented Robotics (SOR)Testing Composite Behaviors Services Figure: Flocking and Exploration behaviors (Video). [11]Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 42 / 81
  • 49. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesService-oriented Robotics (SOR)Interesting Observation, Complexity Reduction Figure: Comparison between a) most popular literature and b) our behavior-based autonomous exploration. [11]Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 43 / 81
  • 50. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesService-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]Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 44 / 81
  • 51. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesService-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]Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 45 / 81
  • 52. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesService-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]Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 46 / 81
  • 53. Autonomous Exploration RobustnessFigure: Qualitative appreciation for autonomous exploration across different scenarios. [11]
  • 54. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesMore Sophisticated OperationsInteresting Results: Single Robot Figure: Autonomous exploration results using one robot. [11]Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 48 / 81
  • 55. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesMore Sophisticated OperationsInteresting Results: Multi-Robot (1) Figure: Autonomous exploration results using multiple robots. [11]Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 49 / 81
  • 56. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesMore Sophisticated OperationsInteresting Results: Multi-Robot (2) Figure: Autonomous exploration results using multiple robots. [11]Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 50 / 81
  • 57. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesMore Sophisticated OperationsSubsystems Control Service Figure: Operator Control Unit (OCU) for robot control.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 51 / 81
  • 58. System Control Service Figure: Operator Control Unit (OCU) for robot coordination.
  • 59. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesMore Sophisticated OperationsLocalization Figure: Localization service.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 53 / 81
  • 60. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesMore Sophisticated OperationsSingle Robot Exploration Figure: Single Robot Real Implementations Results (Video). [11]Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination 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 ReferencesOutline 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 WorkJes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 56 / 81
  • 63. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesSummary of ContributionsSummary 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.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 57 / 81
  • 64. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesSummary of ContributionsSummary 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.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 58 / 81
  • 65. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesSummary of ContributionsSummary 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]Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 59 / 81
  • 66. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesFuture WorkFuture 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...Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 60 / 81
  • 67. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesFuture WorkClosing 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.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination 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/WUYS3You 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 ReferencesReferences 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. Towards heterogeneous robot teams for disaster mitigation: Results and performance metrics from robocup rescue. Journal of Field Robotics, 24(11-12):943–967, 2007.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 63 / 81
  • 70. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesReferences II T. Balch. Avoiding the past: a simple but effective strategy for reactive navigation. In Robotics and Automation, 1993. Proceedings., 1993 IEEE International Conference on, volume vol.1, pages 678 –685, may 1993. T. Balch and R.C. Arkin. Behavior-based formation control for multirobot teams. Robotics and Automation, IEEE Transactions on, 14(6):926 –939, dec 1998. Andreas Birk and Stefano Carpin. Rescue robotics - a crucial milestone on the road to autonomous systems. Advanced Robotics Journal, 20(5):595–605, 2006.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 64 / 81
  • 71. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesReferences III D. Bowen and S. MacKenzie. Autonomous collaborative unmanned vehicles: Technological drivers and constraints. Technical report, Defence Research and Development Canada, 2003. W. Burgard, M. Moors, C. Stachniss, and F.E. Schneider. Coordinated multi-robot exploration. Robotics, IEEE Transactions on, 21(3):376 – 386, june 2005. Stefano Carpin, Jijun Wang, Michael Lewis, Andreas Birk, and Adam Jacoff. High fidelity tools for rescue robotics: Results and perspectives. In Ansgar Bredenfeld, Adam Jacoff, Itsuki Noda, and Yasutake Takahashi, editors, RoboCup, volume 4020 of Lecture Notes in Computer Science, pages 301–311. Springer, 2005.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 65 / 81
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  • 74. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesReferences VI K. Chuengsatiansup, K. Sajjapongse, P. Kruapraditsiri, C. Chanma, N. Termthanasombat, Y. Suttasupa, S. Sattaratnamai, E. Pongkaew, P. Udsatid, B. Hattha, P. Wibulpolprasert, P. Usaphapanus, N. Tulyanon, M. Wongsaisuwan, W. Wannasuphoprasit, and P. Chongstitvatana. Plasma-rx: Autonomous rescue robots. In Robotics and Biomimetics, 2008. ROBIO 2008. IEEE International Conference on, pages 1986–1990, feb. 2009. N. Correll and A. Martinoli. Robust distributed coverage using a swarm of miniature robots. In Robotics and Automation, 2007 IEEE International Conference on, pages 379 –384, april 2007.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 68 / 81
  • 75. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesReferences VII Navneet Dalal and William Triggs. Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR05, 1(3):886–893, 2004. J. de Hoog, S. Cameron, and A. Visser. Role-based autonomous multi-robot exploration. In Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009. COMPUTATIONWORLD ’09. Computation World:, pages 482 –487, nov. 2009. D. Fox, J. Ko, K. Konolige, B. Limketkai, D. Schulz, and B. Stewart. Distributed multirobot exploration and mapping. Proceedings of the IEEE, 94(7):1325 –1339, july 2006.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 69 / 81
  • 76. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesReferences VIII P. Furgale and T. Barfoot. Visual path following on a manifold in unstructured three-dimensional terrain. In Robotics and Automation (ICRA), 2010 IEEE International Conference on, pages 534 –539, may 2010. M. Guarnieri, R. Kurazume, H. Masuda, T. Inoh, K. Takita, P. Debenest, R. Hodoshima, E. Fukushima, and S. Hirose. Helios system: A team of tracked robots for special urban search and rescue operations. In Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, pages 2795 –2800, oct. 2009.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 70 / 81
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  • 83. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesReferences XV Luciano C. A. Pimenta, Mac Schwager, Quentin Lindsey, Vijay Kumar, Daniela Rus, Renato C. Mesquita, and Guilherme Pereira. Simultaneous coverage and tracking (scat) of moving targets with robot networks. In WAFR, pages 85–99, 2008. Ioannis Rekleitis, Gregory Dudek, and Evangelos Milios. Multi-robot collaboration for robust exploration. Annals of Mathematics and Artificial Intelligence, 31:7–40, 2001. Martijn N. Rooker and Andreas Birk. Multi-robot exploration under the constraints of wireless networking. Control Engineering Practice, 15(4):435 – 445, 2007.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 77 / 81
  • 84. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesReferences XVI P.E. Rybski, N.P. Papanikolopoulos, S.A. Stoeter, D.G. Krantz, K.B. Yesin, M. Gini, R. Voyles, D.F. Hougen, B. Nelson, and M.D. Erickson. Enlisting rangers and scouts for reconnaissance and surveillance. Robotics Automation Magazine, IEEE, 7(4):14 –24, dec 2000. Harith Siddhartha, Rahul Sarika, and Kamalakar Karlapalem. Score vector : A new evaluation scheme for robocup rescue simuation competition 2009, 2009. Roland Siegwart and Illah R. Nourbakhsh. Introduction to Autonomous Mobile Robots. The MIT Press, 2004.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 78 / 81
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  • 86. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesReferences XVIII S. Tadokoro, T. Takamori, K. Osuka, and S. Tsurutani. Investigation report of the rescue problem at hanshin-awaji earthquake in kobe. In Intelligent Robots and Systems, 2000. (IROS 2000). Proceedings. 2000 IEEE/RSJ International Conference on, volume 3, pages 1880 –1885 vol.3, 2000. Satoshi Tadokoro. Rescue Robotics. DDT Project on Robots and Systems for Urban Search and Rescue. Springer, 2009.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 80 / 81
  • 87. Introduction State of the Art Solution Detail Experiments and Results Conclusions ReferencesReferences XIX Jindong Tan. A scalable graph model and coordination algorithms for multi-robot systems. In Advanced Intelligent Mechatronics. Proceedings, 2005 IEEE/ASME International Conference on, pages 1529 –1534, july 2005. Mark F Wood and Scott A Deloach. An overview of the multiagent systems engineering methodology. AgentOriented Software Engineering, 1957(January):207–221, 2001.Jes´s Salvador Cepeda Barrera u Tecnol´gico de Monterrey oCoordination of Multiple Robotic Agents for Disasters and Emergency Response 81 / 81

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