Applied Intelligence

, Volume 37, Issue 2, pp 267–279 | Cite as

An efficient algorithm for optimal routing applied to convoy merging manoeuvres in urban environments

  • Fernando Valdés
  • Roberto Iglesias
  • Felipe EspinosaEmail author
  • Miguel A. Rodríguez


This paper describes an innovative routing strategy for intelligent transportation units willing to perform merging manoeuvres with a moving convoy. In particular, we consider a transportation unit located inside a city (pursuer unit), and which wishes to join a convoy that is constantly moving around the city. We first describe a solution that considers idealistic conditions, i.e., the traveling time along each street is constant. We then go on to improve our first approach to deal with the realistic random nature of the traveling times experienced by the pursuer and by the convoy leader. Our search strategy applies Dynamic Programming to achieve a meeting point that is optimal in two ways: on one hand, the optimal destination is the one closest to the current pursuer’s position; on the other hand, the optimal meeting point must minimize the time that elapses until the pursuer meets the convoy (considering that the pursuer must always arrive first). Calculating the optimal path to every possible destination is highly inefficient, error prone and time consuming. On the contrary, we propose an efficient search strategy that will achieve the meeting point and the path to it, both optimally and in a very short time. This enables the real-time application of our approach either for finding new solutions or for re-planning old ones owing to unexpected real-conditions.


Intelligent transportation systems Optimization and decision making Real-time route planners Convoy manoeuvring Search algorithm Dynamic programming 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Caravani P, De Santis E, Graziosi F, Panizzi E (2006) Communication control and driving assistance to a platoon of vehicles in heavy traffic and scarce visibility. IEEE Trans on Intelligent Transportation Systems 7(4):377–392. doi: 10.1109/TITS.2006.884890 CrossRefGoogle Scholar
  2. 2.
    Comyn P (2010) General motors shows its vision of the future. IRISHTIMES.COM, September 10. Accessed on August 05, 2011
  3. 3.
    GM News (2010) Unveils EN-V concept: a vision for future urban mobility. 2010-03-25. Accessed on August 05, 2011
  4. 4.
    Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1:269–271 MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Randria I, Ben Khelifa MM, Bouchouicha M, Abellard P (2007) A comparative study of six basic approaches for path planning towards an autonomous navigation. In: The 33rd annual conference of the IEEE industrial electronics society Google Scholar
  6. 6.
    Chiong R, Sutanto JH, Japutra W (2008) A comparative study on informed and uninformed search for intelligent travel planning in Borneo Island. In: Int symposium on information technology Google Scholar
  7. 7.
    Nilsson N (1971) Problem-solving methods in artificial intelligence. McGraw Hill, New York Google Scholar
  8. 8.
    Koening S, Likhachev M (2005) Fast replanning for navigation in unknown terrain. IEEE Transactions on Robotics 21(3):354–363 CrossRefGoogle Scholar
  9. 9.
    Yue H, Shao CH (2007) Study on the application of A shortest path search algorithm in dynamic urban traffic. In: Third IEEE int conference on natural computation Google Scholar
  10. 10.
    Vascak J, Rutrich M (2008) Path planning in dynamic environment using fuzzy cognitive maps. In: 6th international symposium on applied machine intelligence and informatics Google Scholar
  11. 11.
    Galip Bayrak A, Polat F (2010) Formation preserving path finding in 3-D terrains. J Appl Intell. doi: 10.1007/s10489-010-0265-9 Google Scholar
  12. 12.
    Korf RE (1990) Real-time heuristic search. Artif Intell 42(2–3):189–211 zbMATHCrossRefGoogle Scholar
  13. 13.
    Koenig S (2004) A comparison of fast search methods for real-time situated agents. In: Proceedings of the international conference on autonomous agents and multi-agent systems, vol 2, pp 864–871 Google Scholar
  14. 14.
    Koenig S, Likhachev M, Furcy D (2004) Lifelong planning A. Artificial Intelligence Journal 155(1–2):93–146 MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Koenig S, Likhachev M (2002) D lite. In: Proceedings of the national conference on artificial intelligence, pp 476–483 Google Scholar
  16. 16.
    Likhachev M, Ferguson D, Gordon G, Stentz A, Thrun S (2005) Anytime dynamic A: an anytime, replanning algorithm. In: Proceedings of the international conference on automated planning and scheduling Google Scholar
  17. 17.
    Chou-Yuan L, Zne-Jung L, Shih-Wei L, Kuo-Ching Y (2010) An enhanced and colony optimization (EACO) applied to capacitated vehicle routing problem. J Appl Intell. doi: 10.1007/s10489-008-0136-9 Google Scholar
  18. 18.
    Hanshar F, Ombuki-Berman B (2007) Dynamic vehicle routing using genetic algorithms. J Appl Intell. doi: 10.1007/s10489-006-0033-z zbMATHGoogle Scholar
  19. 19.
    Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge Google Scholar
  20. 20.
    Alton K, Mitchell IM (2008) Efficient dynamic programming for optimal multi-location robot rendezvous. In: 47th IEEE conference on decision and control, pp 2794–2799 CrossRefGoogle Scholar
  21. 21.
    Sadegh N (2008) Time-optimal motion planning of autonomous vehicles in the presence of obstacles. In: American control conference, pp 1830–1835 CrossRefGoogle Scholar
  22. 22.
    Cho JH, Kim HS, Choi HR (2010) An intermodal transport network planning algorithm using dynamic programming. A case of study: from Busan to Rotterdam in intermodal freight routing. J Appl Intell. doi: 10.1007/s10489-010-0223-6 Google Scholar
  23. 23.
    Bellman R (2003) Dynamic programming. Dover, New York. ISBN:0-486-42809-5 zbMATHGoogle Scholar
  24. 24.
    Yu S, Ye F, Wang H, Mabu S, Shimada K, Hirasawa K (2008) A global routing strategy in dynamic traffic environments with a combination of Q value-based dynamic programming and Boltzmann distribution. In: SICE annual conference Google Scholar
  25. 25.
    Mainali MK, Shimada K, Mabu S, Hirasawa K (2008) Optimal route of road networks by dynamic programming. In: IEEE international joint conference on neural networks Google Scholar
  26. 26.
    Mainali MK, Shimada K, Mabu S, Hirasawa K (2008) Multi-objective optimal route search for road networks by dynamic programming. In: SICE annual conference Google Scholar
  27. 27.
    Nilsson N (1998) Artificial intelligence. A new synthesis. Morgan Kaufmann, San Mateo zbMATHGoogle Scholar
  28. 28.
    Pearl J (1984) Heuristics: intelligent search strategies for computer problem solving. Addison-Wesley, Reading. ISBN:0-201-05594-5 Google Scholar
  29. 29.
    Player/Stage Project (2011) Accessed on August 05
  30. 30.
    Espinosa F, Salazar M, Valdes F, Bocos A (2008) Communication architecture based on player/stage and sockets for cooperative guidance of robotic units. In: 16th Mediterranean conference on control and automation, pp 1423–1428 CrossRefGoogle Scholar
  31. 31.
    Byung-Jung L, Jeong-Hoon J, Gyun W (2008) A case study on programming intelligent swarm robots using Pyro environment and player/stage simulator. In: International conference on convergence and hybrid information technology, pp 3–6 Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Fernando Valdés
    • 1
  • Roberto Iglesias
    • 2
  • Felipe Espinosa
    • 1
    Email author
  • Miguel A. Rodríguez
    • 2
  1. 1.Electronics Department, Polytechnic SchoolUniversity of AlcaláAlcalá de HenaresSpain
  2. 2.Department of Electronics and Computer ScienceUniversity of Santiago de CompostelaSantiago de CompostelaSpain

Personalised recommendations