Autonomous Agents and Multi-Agent Systems

, Volume 23, Issue 3, pp 344–383 | Cite as

Petri Net Plans

A framework for collaboration and coordination in multi-robot systems
  • V. A. Ziparo
  • L. Iocchi
  • Pedro U. Lima
  • D. Nardi
  • P. F. Palamara
Article

Abstract

Programming the behavior of multi-robot systems is a challenging task which has a key role in developing effective systems in many application domains. In this paper, we present Petri Net Plans (PNPs), a language based on Petri Nets (PNs), which allows for intuitive and effective robot and multi-robot behavior design. PNPs are very expressive and support a rich set of features that are critical to develop robotic applications, including sensing, interrupts and concurrency. As a central feature, PNPs allow for a formal analysis of plans based on standard PN tools. Moreover, PNPs are suitable for modeling multi-robot systems and the developed behaviors can be executed in a distributed setting, while preserving the properties of the modeled system. PNPs have been deployed in several robotic platforms in different application domains. In this paper, we report three case studies, which address complex single robot plans, coordination and collaboration.

Keywords

Petri Nets Multi-robot systems Formal models Plan representation and execution 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Akharware, N. (2005). Pipe2: Platform independent petri net editor. M.Sc. thesis, Imperial College of Science, Technology and Medicine, University of London, London, UK.Google Scholar
  2. 2.
    Calisi, D., Censi, A., Iocchi, L., & Nardi, D. (2008, September). OpenRDK: a modular framework for robotic software development. In Proceedings of international conference on intelligent robots and systems (IROS), pp. 1872–1877.Google Scholar
  3. 3.
    Calisi D., Farinelli A., Iocchi L., Nardi D. (2007) Multi-objective exploration and search for autonomous rescue robots. Journal of Field Robotics, Special Issue on Quantitative Performance Evaluation of Robotic and Intelligent Systems 24: 763–777Google Scholar
  4. 4.
    Celaya, J. R., Desrochers, A. A., & Graves, R. J. (2007). Modeling and analysis of multi-agent systems using petri nets. In IEEE international conference on systems, man and cybernetics (ISIC), pp. 1439–1444.Google Scholar
  5. 5.
    Chaimowicz, L., Campos, M. F. M., & Kumar, V. (2002, May). Dynamic role assignment for cooperative robots. In Proceedings of the 2002 IEEE international conference on robotics and automation (ICRA02), pp. 292–298, Washington, DCGoogle Scholar
  6. 6.
    Cohen P. R., Levesque H. J. (1991) Teamwork. Special Issue on Cognitive Science and Artificial Intelligence 25: 486–512Google Scholar
  7. 7.
    Coradeschi S., Saffiotti A. (2003) An introduction to the anchoring problem. Robotics and Autonomous Systems 43(2–3): 85–96CrossRefGoogle Scholar
  8. 8.
    Cost, R. S., Chen, Y., Finin, T., Labrou, Y. K., & Peng, Y. (2000). Using colored petri nets for conversation modeling, Vol. 1916 of Lecture Notes in AI (pp. 178–192). Berlin: Springer.Google Scholar
  9. 9.
    Costelha, H., & Lima, P. (2007). Modelling, analysis and execution of robotic tasks using petri nets. In IEEE/RSJ international conference on Intelligent robots and systems (IROS), pp. 1449–1454, October 29–November 2, 2007.Google Scholar
  10. 10.
    De Giacomo, G., Iocchi, L., Nardi, D., & Rosati, R. (1997). Planning with sensing for a mobile robot. In Proceedings of 4th European conference on planning (ECP’97).Google Scholar
  11. 11.
    Giacomo G., Lespérance Y., Levesque H. J. (2000) Congolog, a concurrent programming language based on the situation calculus. Artificial Intelligence 121(1–2): 109–169MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    de Silva, L., Sardina S., & Padgham, L. (2009). First principles planning in bdi systems. In AAMAS ’09: Proceedings of the 8th international conference on Autonomous agents and multiagent systems, pp. 1105–1112. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2009.Google Scholar
  13. 13.
    Dias, M. B., & Stentz, A. T. (2001, August). A market approach to multirobot coordination. Technical Report CMU-RI-TR-01-26, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.Google Scholar
  14. 14.
    Dias, M. D., & Stentz, A. (2002, September) Opportunistic optimization for market-based multirobot control. In 2002 IEEE/RSJ international conference on Intelligent robots and systems (IROS’02), pp. 2714–2720.Google Scholar
  15. 15.
    Durfee, E. H. (1999). Distributed problem solving and planning. In G. Weiss (Ed.), Multiagent systems: A modern approach to distributed artificial intelligence (pp. 121–164). Cambridge: MIT Press.Google Scholar
  16. 16.
    Farinelli, A., Iocchi, L., Nardi, D., & Ziparo, V. A. (2006). Assignment of dynamically perceived tasks by token passing in multi-robot systems. Proceedings of the IEEE, Special issue on multi-robot systems, 94(7), 1271–1288. ISSN:0018-9219.Google Scholar
  17. 17.
    Ferber J. (1999) Multi-agent systems. Addison-Wesley, BostonGoogle Scholar
  18. 18.
    Fikes R., Nilsson N. (1971) STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2: 189–208MATHCrossRefGoogle Scholar
  19. 19.
    Firby, R. J. (1989). Adaptive execution in complex dynamic worlds. PhD thesis, Yale.Google Scholar
  20. 20.
    Gat, E. (1992). Integrating planning and reacting in a heterogeneous asynchronous architecture for controlling real-world mobile robots. In Proceedings of the tenth national conference on artificial intelligence, pp. 809–815.Google Scholar
  21. 21.
    Gat, E. (1997, February). ESL: A language for supporting robust plan execution in embedded autonomous agents. In Proceedings of the IEEE aerospace conference (Vol. 1, pp. 319–324). Aspen, CO: Snowmass.Google Scholar
  22. 22.
    Georgeff, M. P., & Lansky, A. L. (1986). Procedural knowledge. In Proceedings of the IEEE special issue on knowledge representation, Vol. 74, pp. 1383–1398.Google Scholar
  23. 23.
    Gerkey, B., & Matarić, M. J. (2000, December). Principled communication for dynamic multi-robot task allocation. In Proceedings of the international symposium on experimental robotics, pp. 353–362, Waikiki, Hawaii.Google Scholar
  24. 24.
    Giordano V., Ballal P., Lewis F., Turchiano B., Zhang J.B. (2006) Supervisory control of mobile sensor networks: Math formulation, simulation, and implementation. IEEE Transactions on Systems, Man and Cybernetics—Part B: Cybernetics 36(4): 554–562CrossRefGoogle Scholar
  25. 25.
    Gutnik G., Kaminka G. A. (2006) Representing conversations for scalable overhearing. Journal of Artificial Intelligence Research 25(1): 349–387Google Scholar
  26. 26.
    Herrero-Perez D., Martinez-Barbera H. (2010) Modeling distributed transportation systems composed of flexible automated guided vehicles in flexible manufacturing systems. IEEE Transactions on Industrial Informatics 6(2): 166–180CrossRefGoogle Scholar
  27. 27.
    Iocchi L., Nardi D., Piaggio M., Sgorbissa A. (2003) Distributed coordination in heterogeneous multi-robot systems. Autonomous Robots 15(2): 155–168CrossRefGoogle Scholar
  28. 28.
    Kaminka, G. A., & Frenkel, I. (2005). Flexible teamwork in behavior-based robots. In AAAI, pp. 108–113.Google Scholar
  29. 29.
    King J., Pretty R. K., Gosine R. G. (2003) Coordinated execution of tasks in a multiagent environment. IEEE Transactions on Systems, Man, and Cybernetics, Part A 33(5): 615–619CrossRefGoogle Scholar
  30. 30.
    Kobt, Y. T., Beauchemin, S. S., & Barron, J. L. (2007). Petri net-based cooperation in multi-agent systems. In Proceedings of 4th Canadian conference on computer and robot vision, 2007Google Scholar
  31. 31.
    Konolige K. (1997) COLBERT: A language for reactive control in Saphira. Lecture Notes in Computer Science 1303: 31–50Google Scholar
  32. 32.
    Konolige K., Myers K. L., Ruspini E. H., Saffiotti A. (1997) The Saphira architecture: A design for autonomy. Journal of Experimental and Theoretical Artificial Intelligence 9(1): 215–235CrossRefGoogle Scholar
  33. 33.
    Kontes, G., & Lagoudakis, M. G. (2007). Coordinated team play in the four-legged robocup league. In Proceedings of IEEE international conference on Tools with artificial intelligence (ICTAI), Vol. 1, pp. 109–116.Google Scholar
  34. 34.
    Kress-Gazit H., Fainekos G. E., Pappa G. J. (2009) Temporal logic-based reactive mission and motion planning. IEEE Transactions on Robotics 25(6): 1370–1381CrossRefGoogle Scholar
  35. 35.
    Kuo, C.-H., & Lin, I.-H. (2006). Modeling and control of autonomous soccer robots using distributed agent oriented petri nets. In IEEE international conference on Systems, man and cybernetics (SMC apos), Vol. 5, pp. 4090–4095.Google Scholar
  36. 36.
    Loetzsch, M., Risler, M., & Jungel, M. (2006). Xabsl—A pragmatic approach to behavior engineering. In IEEE/RSJ international conference on Intelligent robots and systems, 2006, pp. 5124–5129.Google Scholar
  37. 37.
    Lima, D., & Milutinovic, P. (2002). Petri net models of robotic tasks. In IEEE international conference on Robotics and Automation (ICRA’02).Google Scholar
  38. 38.
    Maier, C., & Moldt, D. (2001). Object coloured petri nets—A formal technique for object oriented modelling. Concurrent object-oriented programming and petri nets: Advances in petri nets, pp. 406–427.Google Scholar
  39. 39.
    McCarthy J., Hayes P. (1969) Some philisophical problems from the standpoint of artificial intelligence. Machine Intelligence 4: 463–502MATHGoogle Scholar
  40. 40.
    Murata T. (1989) Petri nets: Properties, analysis and applications. Proceedings of the IEEE 77(4): 541–580CrossRefGoogle Scholar
  41. 41.
    Palamara, P. F., Ziparo, V. A., Iocchi, L., Nardi, D., Lima, P., & Costelha, H. (2008). A robotic soccer passing task using petri net plans (demo paper). In D. Parkes, J. P. Müller, L. Padgham, & S. Parsons (Eds.), Proceedings of 7th international conference on Autonomous agents and multiagent systems (AAMAS 2008) (pp. 1711–1712). Estoril, Portugal: IFAAMAS Press.Google Scholar
  42. 42.
    Parker L. E. (1998) ALLIANCE: An architecture for fault tolerant multirobot cooperation. IEEE Transactions on Robotics and Automation 14(2): 220–240CrossRefGoogle Scholar
  43. 43.
    Poutakidis, D., Padgham, L., & Winikoff, M. (1998). Debugging Multi-agent systems using design artifacts: The case of interaction protocols. In Proceedings of 1998 IEEE international conference on Systems, man and cybernetics, San Diego, USA.Google Scholar
  44. 44.
    Rao, A. S., & Georgeff, M. P. (1991). Modeling rational agents within a BDI-architecture. In J. Allen, R. Fikes, & E. Sandewall (Eds.), Proceedings of the second international conference on Principles of knowledge representation and reasoning. San Mateo: Morgan Kaufmann.Google Scholar
  45. 45.
    Reiter R. (2001) Knowledge in action: Logical foundations for describing and implementing dynamical systems. MIT Press, CambridgeGoogle Scholar
  46. 46.
    Russell S. J., Norvig P. (2003) Artificial intelligence: A modern approach (2nd ed.). Pearson Education, SingaporeGoogle Scholar
  47. 47.
    Scherl, R., & Levesque, H. J. (1993). The frame problem and knowledge producing actions. In Proceedings of the 11th national conference on Artificial intelligence (AAAI’93), pp. 689–695.Google Scholar
  48. 48.
    Sheng, W., & Yang, Q. (2005, July 24–28). Peer-to-peer multi-robot coordination algorithms: Petri net based analysis and design. In Proceedings, 2005 IEEE/ASME international conference on Advanced intelligent mechatronics, pp. 1407–1412.Google Scholar
  49. 49.
    Simmons, R., & Apfelbaum, D. (1998, October). A task description language for robot control. In Proceedings of IEEE/RSJ international conference on Intelligent robots and systems (IROS), Vol. 3, pp. 1931–1937. Victoria, BC, Canada.Google Scholar
  50. 50.
    Sudeikat J., Braubach L., Pokahr A., Lamersdorf W. (2006) Validation of bdi agents. In: Bordini R., Dastani M., Dix J., El Fallah Seghrouchni A. (eds) The 4th international workshop on Programming multiagent systems (PROMAS-2006). Springer, Berlin, pp 185–200Google Scholar
  51. 51.
    Tambe M. (1997) Towards flexible teamwork. Journal of Artificial Intelligence Research 7: 83–124Google Scholar
  52. 52.
    Thrun S., Burgard W., Fox D. (2005) Probabilistic Robotics (Intelligent robotics and autonomous agents). The MIT Press, CambridgeGoogle Scholar
  53. 53.
    Vishwanadham N., Narahari Y. (1992) Performance modelling of automated manufacturing systems. Prentice Hall, New DelhiGoogle Scholar
  54. 54.
    Wang F. Y., Kyriakopoulos K. J., Tsolkas A., Saridis G. N. (1993) A petri-net coordination model for an intelligent mobile robot. IEEE Transactions on Robotics and Automation 9(3): 257–271CrossRefGoogle Scholar
  55. 55.
    Werger, B. B., & Mataric, M. J. (2000). Broadcast of local eligibility for multi-target observation. In DARS00, pp. 347–356.Google Scholar
  56. 56.
    Xu, D., Volz, R., Ioerger, T., & Yen, J. (2002). Modeling and verifying multi-agent behaviors using predicate/transition nets. In SEKE ’02: Proceedings of the 14th international conference on Software engineering and knowledge engineering (pp. 193–200), New York, NY: ACM.Google Scholar
  57. 57.
    Zimmermann, A., & Freiheit, J. (1998). TimeNETMS-an integrated modeling and performance evaluation tool for manufacturing systems. In Proceedings of 1998 IEEE international conference on Systems, man and cybernetics. San Diego, USA.Google Scholar
  58. 58.
    Ziparo, V. A., & Iocchi, L. (2006). Petri net plans. In Proceedings of fourth international workshop on modeling of objects, components, and agents (MOCA), pp. 267–290, Turku, Finland. Bericht 272, FBI-HH-B-272/06.Google Scholar
  59. 59.
    Ziparo, V. A., Iocchi, L., Nardi, D., Palamara, P. F., & Costelha, H. (2008). Petri net plans: a formal model for representation and execution of multi-robot plans. In AAMAS ’08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems (pp. 79–86). Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
  60. 60.
    Zlot, R., Stenz, A., Dias, M. B., & Thayer, S. (2002). Multi robot exploration controlled by a market economy. In IEEE international conference on robotics and automation (ICRA’02), pp. 3016–3023.Google Scholar

Copyright information

© The Author(s) 2010

Authors and Affiliations

  • V. A. Ziparo
    • 1
  • L. Iocchi
    • 1
  • Pedro U. Lima
    • 2
  • D. Nardi
    • 1
  • P. F. Palamara
    • 1
  1. 1.Dipartimento di Informatica e Sistemistica “Antonio Ruberti” (DIS)Sapienza University of RomeRomaItaly
  2. 2.Institute for Systems and Robotics (ISR), Instituto Superior Técnico (IST)LisbonPortugal

Personalised recommendations