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Developing a Distributed Drone Delivery System with a Hybrid Behavior Planning System

  • Daniel Krakowczyk
  • Jannik Wolff
  • Alexandru Ciobanu
  • Dennis Julian Meyer
  • Christopher-Eyk HrabiaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11117)

Abstract

The demand for fast and reliable parcel shipping is globally rising. Conventional delivery by land requires good infrastructure and causes high costs, especially on the last mile. We present a distributed and scalable drone delivery system based on the contract net protocol for task allocation and the ROS hybrid behaviour planner (RHBP) for goal-oriented task execution. The solution is tested on a modified multi-agent systems simulation platform (MASSIM). Within this environment, the solution scales up well and is profitable across different configurations.

Keywords

Task allocation Unmanned aerial vehicle (UAV) Drone delivery Multi-agent systems Multi-agent simulation 

References

  1. 1.
    MASSim: Multi-agent systems simulation platform. https://github.com/agentcontest/massim/. Accessed 13 May 2018
  2. 2.
    Aknine, S., Pinson, S., Shakun, M.F.: An extended multi-agent negotiation protocol. Auton. Agents Multi-Agent Syst. 8, 5–45 (2004)CrossRefGoogle Scholar
  3. 3.
    Amador, S., Okamoto, S., Zivan, R.: Dynamic multi-agent task allocation with spatial and temporal constraints. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, pp. 1495–1496. International Foundation for Autonomous Agents and Multiagent Systems (2014)Google Scholar
  4. 4.
    Bozdag, E.: A survey of extensions to the contract net protocol. Technical report, CiteSeerX-Scientific Literature Digital Library and Search Engine (2008)Google Scholar
  5. 5.
    Busoniu, L., Babuska, R., De Schutter, B.: A comprehensive survey of multiagent reinforcement learning. IEEE Trans Syst. Man Cybern. Part C 38(2), 156–172 (2008)CrossRefGoogle Scholar
  6. 6.
    Davis, R., Smith, R.G., Erman, L.: Negotiation as a metaphor for distributed problem solving. In: Readings in Distributed Artificial Intelligence, pp. 333–356. Elsevier (1988)Google Scholar
  7. 7.
    De Weerdt, M., Clement, B.: Introduction to planning in multiagent systems. Multiagent Grid Syst. 5(4), 345–355 (2009)CrossRefGoogle Scholar
  8. 8.
    Dellarocas, C., Klein, M., Rodriguez-Aguilar, J.A.: An exception-handling architecture for open electronic marketplaces of contract net software agents. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 225–232. ACM (2000)Google Scholar
  9. 9.
    Dorling, K., Heinrichs, J., Messier, G.G., Magierowski, S.: Vehicle routing problems for drone delivery. IEEE Trans. Syst. Man Cybern.: Syst. 47(1), 70–85 (2017)CrossRefGoogle Scholar
  10. 10.
    Durfee, E., Lesser, V.: Partial global planning: a coordination framework for distributed hypothesis formation. IEEE Trans. Syst. Man Cybern. 21(5), 1167–1183 (1991)CrossRefGoogle Scholar
  11. 11.
    Erol, K., Hendler, J., Nau, D.S.: HTN planning: complexity and expressivity. In: AAAI, vol. 94, pp. 1123–1128 (1994)Google Scholar
  12. 12.
    Ettlinger, M., Sarp, B., Hrabia, C.E., Albayrak, S.: An evaluation framework for UAV surveillance applications. In: The 31st Annual European Simulation and Modelling Conference 2017, pp. 356–362, October 2017Google Scholar
  13. 13.
    Fitoussi, D., Tennenholtz, M.: Choosing social laws for multi-agent systems: minimality and simplicity. Artif. Intell. 119(1–2), 61–101 (2000)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Georgeff, M.: Communication and interaction in multi-agent planning. In: Proceedings of the National Conference on Artificial Intelligence. Elsevier (1984)Google Scholar
  15. 15.
    Happe, J., Berger, J.: CoUAV: a multi-UAV cooperative search path planning simulation environment. In: Proceedings of the 2010 Summer Computer Simulation Conference, pp. 86–93. Society for Computer Simulation International (2010)Google Scholar
  16. 16.
    Hirayama, K., Yokoo, M.: Distributed partial constraint satisfaction problem. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 222–236. Springer, Heidelberg (1997).  https://doi.org/10.1007/BFb0017442CrossRefGoogle Scholar
  17. 17.
    Hrabia, C.E., Wypler, S., Albayrak, S.: Towards goal-driven behaviour control of multi-robot systems. In: 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), pp. 166–173. IEEE (2017)Google Scholar
  18. 18.
    Jones, E.G., Dias, M.B., Stentz, A.: Learning-enhanced market-based task allocation for oversubscribed domains. In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007, pp. 2308–2313. IEEE (2007)Google Scholar
  19. 19.
    Knabe, T., Schillo, M., Fischer, K.: Improvements to the FIPA contract net protocol for performance increase and cascading applications, October 2002Google Scholar
  20. 20.
    Liu, J., Jing, H., Tang, Y.Y.: Multi-agent oriented constraint satisfaction. Artif. Intell. 136(1), 101–144 (2002)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Morganti, E., Seidel, S., Blanquart, C., Dablanc, L., Lenz, B.: The impact of e-commerce on final deliveries: alternative parcel delivery services in France and Germany. Transp. Res. Procedia 4, 178–190 (2014)CrossRefGoogle Scholar
  22. 22.
    Panescu, D., Pascal, C.: An extended contract net protocol with direct negotiation of managers. In: Borangiu, T., Trentesaux, D., Thomas, A. (eds.) Service Orientation in Holonic and Multi-Agent Manufacturing and Robotics. SCI, vol. 544, pp. 81–95. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-04735-5_6CrossRefGoogle Scholar
  23. 23.
    Quigley, M., et al.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, Kobe, Japan, vol. 3, p. 5 (2009)Google Scholar
  24. 24.
    Scott, J., Scott, C.: Drone delivery models for healthcare (2017)Google Scholar
  25. 25.
    Thiels, C.A., Aho, J.M., Zietlow, S.P., Jenkins, D.H.: Use of unmanned aerial vehicles for medical product transport. Air Med. J. 34(2), 104–108 (2015)CrossRefGoogle Scholar
  26. 26.
    Walsh, W.E., Wellman, M.P.: A market protocol for decentralized task allocation. In: 1998 Proceedings of the International Conference on Multi Agent Systems, pp. 325–332. IEEE (1998)Google Scholar
  27. 27.
    Weiss, G.: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, Cambridge (1999)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Daniel Krakowczyk
    • 1
  • Jannik Wolff
    • 1
  • Alexandru Ciobanu
    • 1
  • Dennis Julian Meyer
    • 1
  • Christopher-Eyk Hrabia
    • 1
    Email author
  1. 1.DAI-LabTechnische Universität BerlinBerlinGermany

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