1 / 15

A Free Market Architecture for Distributed Control of a Multirobot System

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

tovi
Download Presentation

A Free Market Architecture for Distributed Control of a Multirobot System

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

  3. Software Architecture Models Centralized Distributed • optimal • intractable • brittle • sluggish • communication heavy • suboptimal • tractable • robust • nimble • communication light

  4. Related Work Arkin, R. C., “Cooperation without Communication: Multiagent Schema-Based Robot Navigation” 1992 Arkin, R. C. et al., “AuRA: Principles and Practice in Review” 1997 Brooks, R. A., “Elephants Don’t Play Chess” 1990 Brumitt, B. L. et al., “Dynamic Mission Planning for Multiple Mobile Robots” 1996 Golfarelli, M. et al., “A Task-Swap Negotiation Protocol Based on the Contract Net Paradigm” 1997 Jensen, R. M. et al., “OBDD-based Universal Planning: Specifying and Solving Planning Problems for Synchronized Agents in Non-Deterministic Domains” 1999 Johnson, N. F. et al., “Volatility and Agent Adaptability in a Self-Organizing Market” 1998 Lux, T. et al., “Scaling and Criticality in a Stochastic Multi-Agent Model of a Financial Market” 1999 Matarić, M. J., “Issues and Approaches in the Design of Collective Autonomous Agents” 1995 Pagello, E. et al., “Cooperative Behaviors in Multi-Robot Systems through Implicit Communication” 1999 Parker, L. E., “ALLIANCE: An Architecture for Fault Tolerant Multi-Robot Cooperation” 1998 Schneider-Fontán, M.. Et al., “Territorial Multi-Robot Task Division” 1998 Schneider-Fontán, M. et al., “A Study of Territoriality: The Role of Critical Mass in Adaptive Task Division” 1996 Schwartz, R. et al., “Negotiation On Data Allocation in Multi-Agent Environments” 1997 Shehory, O. et al., “Methods for Task Allocation via Agent Coalition Formation” 1998 Smith, R., “The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver” 1980 Švestka, P. et al., “Coordinated Path Planning for Multiple Robots” 1998 Tambe, M., “Towards Flexible Teamwork” 1997 Veloso, M. et al., “Anticipation: A Key for Collaboration in a Team of Agents” 1998 Wellman, M. et al., “Market-Aware Agents for a Multiagent World” 1998 Zeng, D. et al.., “Benefits of Learning in Negotiation” 1997 Sandholm, T. et al., “Issues in Automated Negotiation and Electronic Commerce: Extending the Contract Net Framework” 1995

  5. 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

  6. Architecture Features • Revenue, cost and profit • Negotiation and price • Competition vs. cooperation • Role determined via comparative advantage • Self organization • Learning and adaptation

  7. Task A = 120 Task A = 120 Task B = 180 Task B = 180 60 60 50 50 100 100 75 75 110 Robot 1 110 Robot 1 Robot 2 Robot 2 Simple Reasoning More Complex Reasoning Subcontract: (150 + 110) / 2 = 130 Robot 1 profit: 40 (20) Robot 2 profit: 50 (30) Robot 1 profit = 20 Robot 2 profit = 30

  8. Architectural Framework Negotiations Learning Module Negotiation Protocol Robot Exec Other Agents Tasks Map Area “X” Send Message to “B” Roles Mapper Leader Comm Resources Radio Sensors Locomotor CPU

  9. Agent Interaction Robots Tasks performed Operator (GUI) Revenue paid Operator Exec

  10. R2 R2 Initial Final R1 R1 Initial Assignments Final Tours Simple Mapping Simulation

  11. Final Initial More Complex Mapping Simulation

  12. X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Adaptive Response to Dynamic Conditions Cities Tours

  13. Current Status • Mapping example of architecture implemented • Robot platforms up and running

  14. 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

  15. Acknowledgements The authors thank the members of the Cognitive Colonies group for their valuable contribution: Vanessa De Gennaro Bruce Digney Brian Fredrick Martial Hebert Dave Kachmar Bart Nabbe Charles Smart Scott Thayer

More Related