Advertisement

Context-Aware Route Planning

  • Adriaan W. ter Mors
  • Cees Witteveen
  • Jonne Zutt
  • Fernando A. Kuipers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6251)

Abstract

In context-aware route planning, there is a set of transportation agents each with a start and destination location on a shared infrastructure. Each agent wants to find a shortest-time route plan without colliding with any of the other agents, or ending up in a deadlock situation. We present a single-agent route planning algorithm that is both optimal and conflict-free. We also present a set of experiments that compare our algorithm to finding a conflict-free schedule along a fixed path. In particular, we will compare our algorithm to the approach where the shortest conflict-free schedule is chosen along one of k shortest paths. Although neither approach can guarantee optimality with regard to the total set of agent route plans — and indeed examples can be constructed to show that either approach can outperform the other — our experiments show that our approach consistently outperforms fixed-path scheduling.

Keywords

Short Path Random Graph Destination Location Agent Plan Route Planning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Vis, I.F.: Survey of research in the design and control of automated guided vehicle systems. European Journal of Operational Research 170(3), 677–709 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Hatzack, W., Nebel, B.: The operational traffic problem: Computational complexity and solutions. In: ECP’01, pp. 49–60 (2001)Google Scholar
  3. 3.
    Trüg, S., Hoffmann, J., Nebel, B.: Applying automatic planning systems to airport ground-traffic control - a feasibility study. In: Biundo, S., Frühwirth, T., Palm, G. (eds.) KI 2004. LNCS (LNAI), vol. 3238, pp. 183–197. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    ter Mors, A.W.: The world according to MARP: multi-agent route planning. PhD thesis, Delft University of Technology (March 2010)Google Scholar
  5. 5.
    Desaulniers, G., Langevin, A., Riopel, D., Villeneuve, B.: Dispatching and conflict-free routing of automated guided vehicles: An exact approach. International Journal of Flexible Manufacturing Systems 15(4), 309–331 (2004)CrossRefGoogle Scholar
  6. 6.
    Kim, C.W., Tanchoco, J.M.: Conflict-free shortest-time bidirectional AGV routeing. International Journal of Production Research 29(1), 2377–2391 (1991)CrossRefzbMATHGoogle Scholar
  7. 7.
    Lee, J.H., Lee, B.H., Choi, M.H.: A real-time traffic control scheme of multiple AGV systems for collision-free minimum time motion: a routing table approach. IEEE Transactions on Man and Cybernetics, Part A 28(3), 347–358 (1998)CrossRefGoogle Scholar
  8. 8.
    Yen, J.Y.: Finding the K shortest loopless paths in a network. Management Science 17(11), 712–716 (1971)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    ter Mors, A.W., Zutt, J., Witteveen, C.: Context-aware logistic routing and scheduling. In: ICAPS, pp. 328–335 (2007)Google Scholar
  10. 10.
    Suurballe, J.: Disjoint paths in a network. Networks 4(2), 125–145 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Korilis, Y.A., Lazar, A.A., Orda, A.: Achieving network optima using Stackelberg routing strategies. IEEE/ACM Transactions on Networking 5(1), 161–173 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Adriaan W. ter Mors
    • 1
  • Cees Witteveen
    • 1
  • Jonne Zutt
    • 1
  • Fernando A. Kuipers
    • 1
  1. 1.Delft University of TechnologyThe Netherlands

Personalised recommendations