A Free Market Architecture for Distributed Control of a Multirobot System
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T h e R o b o tic s In stitute A Free Market Architecture for Distributed Control of a Multirobot System The Robotics Institute Carnegie Mellon University M. Bernardine Dias Tony Stentz July 26, 2000
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A Free Market Architecture for Distributed Control of a Multirobot System. M. Bernardine Dias Tony Stentz July 26, 2000. The Robotics Institute Carnegie Mellon University. Motivation and Outline. Outline: Introduction Related Work The Free Market Architecture - PowerPoint PPT Presentation
Transcript of A Free Market Architecture for Distributed Control of a Multirobot System
The Robotics Institute
A Free Market Architecture for Distributed Control of a Multirobot System
The Robotics InstituteCarnegie Mellon University
M. Bernardine Dias Tony Stentz
July 26, 2000
The Robotics Institute IAS-6 July 26, 2000
Motivation and Outline
Outline: Introduction Related Work The Free Market Architecture Initial Implementation Results Future Directions Acknowledgements and Questions
Motivation: Effective control of multi-robot systems
The Robotics Institute IAS-6 July 26, 2000
Software Architecture ModelsCentralized Distributed
• optimal• intractable• brittle• sluggish• communication heavy
• suboptimal• tractable• robust• nimble• communication light
The Robotics Institute IAS-6 July 26, 2000
Arkin, R. C., “Cooperation without Communication: Multiagent Schema-Based Robot Navigation” 1992Arkin, R. C. et al., “AuRA: Principles and Practice in Review” 1997Brooks, R. A., “Elephants Don’t Play Chess” 1990Brumitt, B. L. et al., “Dynamic Mission Planning for Multiple Mobile Robots” 1996Golfarelli, M. et al., “A Task-Swap Negotiation Protocol Based on the Contract Net Paradigm” 1997Jensen, R. M. et al., “OBDD-based Universal Planning: Specifying and Solving Planning Problems for Synchronized Agents in Non-Deterministic Domains” 1999Johnson, N. F. et al., “Volatility and Agent Adaptability in a Self-Organizing Market” 1998Lux, T. et al., “Scaling and Criticality in a Stochastic Multi-Agent Model of a Financial Market” 1999Matarić, M. J., “Issues and Approaches in the Design of Collective Autonomous Agents” 1995Pagello, E. et al., “Cooperative Behaviors in Multi-Robot Systems through Implicit Communication” 1999Parker, L. E., “ALLIANCE: An Architecture for Fault Tolerant Multi-Robot Cooperation” 1998Schneider-Fontán, M.. Et al., “Territorial Multi-Robot Task Division” 1998Schneider-Fontán, M. et al., “A Study of Territoriality: The Role of Critical Mass in Adaptive Task Division” 1996Schwartz, R. et al., “Negotiation On Data Allocation in Multi-Agent Environments” 1997Shehory, O. et al., “Methods for Task Allocation via Agent Coalition Formation” 1998Smith, R., “The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver” 1980Švestka, P. et al., “Coordinated Path Planning for Multiple Robots” 1998Tambe, M., “Towards Flexible Teamwork” 1997Veloso, M. et al., “Anticipation: A Key for Collaboration in a Team of Agents” 1998Wellman, M. et al., “Market-Aware Agents for a Multiagent World” 1998Zeng, D. et al.., “Benefits of Learning in Negotiation” 1997
Related Work
Sandholm, T. et al., “Issues in Automated Negotiation and Electronic Commerce: Extending the Contract Net Framework” 1995
The Robotics Institute IAS-6 July 26, 2000
Free Market Architecture Robots in a team are organized as an economy Team mission is best achieved when the economy
maximizes production and minimizes costs Robots interact with each other to exchange money
for tasks to maximize profit Robots are both self-interested and benevolent,
since it is in their self interest to do global good
The Robotics Institute IAS-6 July 26, 2000
Architecture Features Revenue, cost and profit Negotiation and price Competition vs. cooperation Role determined via comparative advantage Self organization Learning and adaptation
The Robotics Institute IAS-6 July 26, 2000
Simple Reasoning
Robot 1 profit = 20Robot 2 profit = 30
Subcontract: (150 + 110) / 2 = 130Robot 1 profit: 40 (20)Robot 2 profit: 50 (30)
Robot 1
Robot 2
Task A = 120 Task B = 180
5075
110
100
60
Robot 1
Robot 2
Task A = 120 Task B = 180
5075
110
100
60
More Complex Reasoning
The Robotics Institute IAS-6 July 26, 2000
Architectural Framework
Resources
Locomotor Sensors CPURadio
RolesMapper Comm Leader
Negotiations
RobotExec
TasksSend
Message to “B”
Map Area “X”
NegotiationProtocol
LearningModule
Other Agents
The Robotics Institute IAS-6 July 26, 2000
Agent Interaction
Operator Exec
Revenue paid
Tasks performed
Operator(GUI)
Robots
The Robotics Institute IAS-6 July 26, 2000
Simple Mapping Simulation
Initial Final
Initial Assignments
R2
R1Final Tours
R2
R1
The Robotics Institute IAS-6 July 26, 2000
More Complex Mapping Simulation
Initial Final
The Robotics Institute IAS-6 July 26, 2000
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The Robotics Institute IAS-6 July 26, 2000
Current Status Mapping example of architecture implemented Robot platforms up and running
The Robotics Institute IAS-6 July 26, 2000
Future Work
Port architecture to robot test-bed Implement roles Synchronous -> asynchronous Limit communication Implement multi-task negotiation Implement broken deals with penalties Implement architecture in other robotic test-beds Benchmark against other architectures
The Robotics Institute IAS-6 July 26, 2000
AcknowledgementsThe authors thank the members of the Cognitive Colonies
group for their valuable contribution: Vanessa De GennaroBruce DigneyBrian FredrickMartial HebertDave KachmarBart NabbeCharles SmartScott Thayer
