Robots that cooperatively enhance their plans

  • Silvia Botelho
  • Rachid Alami


This paper presents a general architecture for multi-robot cooperation and then focuses on a scheme called “M+ Cooperative task achievement”. Its originality comes from the robots ability to detect and treat — in a distributed and cooperative manner — resource conflict situations as well as sources of inefficiency. We illustrate its use through a simulated system, which allows a number of robots to plan and perform cooperatively a set of servicing tasks in a hospital environment.


Negotiation Process Task Allocation Social Rule Individual Plan Task Achievement 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    R. Alami, S. Fleury, M. Herrb, F. Ingrand, and F. Robert. Multi-robot cooperation in the martha project. IEEE Robotics and Automation Magazine, Special Issues: Robotics and Automation in Europe ,1997.Google Scholar
  2. 2.
    S. S. C. Botelho. A distributed scheme for task planning and negotiation in multi-robot systems. In ECAI’98 ,1998.Google Scholar
  3. 3.
    S. S. C. Botelho and R. Alami. M+: a scheme for multi-robot cooperation through negotiated task allocation and achievement. In IEEE ICRA ’99 ,1999.Google Scholar
  4. 4.
    C. Boutilier and Brafman R. Planning with concurren t interaction actions. In AAAI’97 ,1997.Google Scholar
  5. 5.
    A. Brumitt, B. Stentz. Dynamic mission planning for multiple mobile robots. In IEEE ICRA ’96 ,1996.Google Scholar
  6. 6.
    Y. Cao, A. Fukuna, and A. Kahng. Cooperative mobile robotics: Antecedents and directions. Autonomous Robots ,4:7–27, 1997.CrossRefGoogle Scholar
  7. 7.
    B. Clement and E. Durfee. Top-down search for coordinating the hierarchical plans of multiple agents. In Third International Conference on Autonomous Agents ,pages 252–259. Association of Computing Machinery, 1999.CrossRefGoogle Scholar
  8. 8.
    K. Decker and V. Lesser. Generalizing the partial global planning algorithm. In Int Journal of Cooperative Information Systems 92 ,1992.Google Scholar
  9. 9.
    M. DesJardins, E. Durfee, Ortiz C., and M. Wolverton. A survey of research in distributed, continual planning. AI Magazine ,pages 13–22, 1999.Google Scholar
  10. 10.
    O. Despouys and F Ingrand. Propice-plamtoward a unified framework for planning and execution. In ECP’99 ,1999.Google Scholar
  11. 11.
    E. Durfee and V. Lesser. Using partial global plans to coordinate distributed problem solvers. In IJCAI87 ,1987.Google Scholar
  12. 12.
    E. Ephrati, M. Perry, and J. S. Rosenschein. Plan execution motivation in multi-agent systems. In AIPS ,1994.Google Scholar
  13. 13.
    T. Fukuda, Y. Kawauchi, M. Buss, and H. Asama. A study on dynamically reconfigurable robotic systems. Jsme International Journal ,34(2):295–302, 1991.Google Scholar
  14. 14.
    N. Jennings. Controlling cooperative problem solving in industrial multi-agent systems using joint intentions. Artificial Intelligence ,75, 1995.Google Scholar
  15. 15.
    J. Koehler, B. Nebel, J. Hoffmann, and Dimopoulos Y. Extending planning graphs to an adl subset. In ECP97 ,1997.Google Scholar
  16. 16.
    R. Mackenzie, D. Arkin. Multiagent mission and execution. Autonomous Robots ,4:29–52, 1997.CrossRefGoogle Scholar
  17. 17.
    M. Mataric. Interaction and Intelligent Behaviour. PhD thesis, Massachusetts Institute of Technology, 1994.Google Scholar
  18. 18.
    L. Parker. Alliance: An architecture for fault tolerant multirobot cooperation. IEEE Trans. on Robotics and Automation ,14(2):220–239, 1998.CrossRefGoogle Scholar
  19. 19.
    M.E. Pollack. Planning in dynamic enviroments: the dipart system. Advanced Planning Thechnology: Technology Achievements of the ARPA/Rome Laboratory Planning Initiative ,1996.Google Scholar
  20. 20.
    S. Qutub, R. Alami, and F Ingrand. How to solve deadlock situations within the plan-merging paradigm for multi-robot cooperation. In IEEE IROS’97 ,1997.Google Scholar
  21. 21.
    J. S. Roseschein and G Zlotkin. Rules of and encounter: Designing convention for automated negotiation among computers. Artificial Intelligence -MIT press ,1994.Google Scholar
  22. 22.
    Y. Shoham and M. Tennenholtz. On social laws for artificial agent societies: off-line design. Artificial Intelligence ,(75):231–252, 1995.CrossRefGoogle Scholar
  23. 23.
    R. Smith. The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers ,c-29(12), 1980.Google Scholar
  24. 24.
    G Sullivan, A. Glass, B. Grosz, and S. Kraus. Intention reconciliation in the context of teamwork: an initial empirical investigation. Cooperative Information Agents III, Lecture Notes in Artificial Intelligence ,1652:138–151, 1999.Google Scholar
  25. 25.
    M. Tambe. Agent architectures for flexible, practical teamwork. In First International Conference on Autonomous Agents ,1998.Google Scholar
  26. 26.
    T. Vidal, M. Ghallab, and R. Alami. Incremental mission allocation to a large team of robots. In IEEE International Conference on Robotics and Automation ,April 1996.Google Scholar
  27. 27.
    S. Yuta and S. Premvuti. Coordination autonomous and centralized decision making to achieve cooperative behaviors between multiple mobile robots. In IEEE IROS’92 ,1992.Google Scholar

Copyright information

© Springer-Verlag Tokyo 2000

Authors and Affiliations

  • Silvia Botelho
    • 1
  • Rachid Alami
    • 1
  1. 1.LAAS-CNRSToulouse Cedex 4France

Personalised recommendations