Path Planning for Autonomous Inland Vessels Using A*BG

  • Linying ChenEmail author
  • Rudy R. Negenborn
  • Gabriel Lodewijks
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9855)


To meet the transportation demand and maintain sustainable development, many countries are aiming to promote the competitive position of inland waterway shipping in the transport system. Autonomous transport is seen as a possibility for maritime transport to meet today’s and tomorrow’s challenges. In realizing autonomous navigation, path planning plays an important role. Being the most widely used path planning algorithm for robotics and land-based vehicles, in this paper we analyze A* and its extensions for waterborne applications. We hereby exploit the fact that for vessels optimal paths typically have heading changes only at the corners of obstacles to propose a more efficient modified A* algorithm, A*BG, for autonomous inland vessels. Two locations where ship accidents frequently occur are considered in simulation experiments, in which the performance of A*, A*PS, Theta* and A*BG are compared.


Path Planning Model Predictive Control Buffer Area Visibility Graph Autonomous Navigation 
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.



This research is supported by the China Scholarship Council under Grant 201426950041.


  1. 1.
    de Berg, M., Cheong, O., van Kreveld, M., Overmars, M.: Computational Geometry: Algorithms and Applications, pp. 323–333. Springer, New York (2008)CrossRefzbMATHGoogle Scholar
  2. 2.
    Campbell, S., Naeem, W., Irwin, G.: A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres. Ann. Rev. Control 36(2), 267–283 (2012)CrossRefGoogle Scholar
  3. 3.
    Daniel, K., Nash, A., Koenig, S., Felner, A.: Theta*: any-angle path planning on grids. J. Artif. Intell. Res. 39(2010), 533–579 (2010)zbMATHMathSciNetGoogle Scholar
  4. 4.
    Duchoň, F., Babinec, A., Kajan, M., Beňo, P., Florek, M., Fico, T., Jurišica, L.: Path planning with modified a star algorithm for a mobile robot. Procedia Eng. 96, 59–69 (2014)CrossRefGoogle Scholar
  5. 5.
    Economic commission for Europe: inventory of most important bottlenecks and missing links in the E waterway network. Technical report. ECE/TRANS/SC.3/159/Rev.1, Economic Commission for Europe, Inland Transport Committee, United Nations (2013)Google Scholar
  6. 6.
    European Commission: Naiades II: Towards quality inland waterway transport. Technical report COM 623, European Commission (2013)Google Scholar
  7. 7.
    European Commission: The European Union explained: Transport. Technical report European Commission (2014)Google Scholar
  8. 8.
    Froese, J.: Safe and efficient port approach by vessel traffic management in waterways. In: Ocampo-Martinez, C., Negenborn, R.R. (eds.) Transport of Water versus Transport over Water. Operations Research/Computer Science Interfaces Series, vol. 58, pp. 281–296. Springer, New York (2015)CrossRefGoogle Scholar
  9. 9.
    Google Maps: Port of Rotterdam, Street map (2016).,4.4343202,10.75z
  10. 10.
    Hekkenberg, R.: Technological challenges and developments in European inland waterway transport. In: Ocampo-Martinez, C., Negenborn, R.R. (eds.) Transport of Water versus Transport over Water. Operations Research/Computer Science Interfaces Series, vol. 58, pp. 297–313. Springer, New York (2015)CrossRefGoogle Scholar
  11. 11.
    Kuwata, Y., Wolf, M., Zarzhitsky, D., Huntsberger, T.: Safe maritime autonomous navigation with COLREGS using velocity obstacles. IEEE J. Oceanic Eng. 39(1), 110–119 (2014)CrossRefGoogle Scholar
  12. 12.
    Lazarowska, A.: Ship’s trajectory planning for collision avoidance at sea based on ant colony optimisation. J. Navig. 68(2), 291–307 (2015)CrossRefGoogle Scholar
  13. 13.
    Li, S., Negenborn, R.R., Lodewijks, G.: Distributed constraint optimization for addressing vessel rotation planning problems. Eng. Appl. Artif. Intell. 48(2016), 159–172 (2016)CrossRefGoogle Scholar
  14. 14.
    Movares Projectteam MNV’13: Monitoring nautische veiligheid 2013. Technical report, Rijkswaterstaat Water, Verkeer en Leefomgeving, Afdeling Veiligheidsmanagement en Verkeersveiligheid (2013)Google Scholar
  15. 15.
  16. 16.
  17. 17.
  18. 18.
    Sariff, N., Buniyamin, N.: An overview of autonomous mobile robot path planning algorithms. In: Proceedings of the 4th Student Conference on Research and Development, pp. 183–188. Selangor, Malaysia (2006)Google Scholar
  19. 19.
    Shah, B.C., Švec, P., Bertaska, I.R., Sinisterra, A.J., Klinger, W., Ellenrieder, K., Dhanak, M., Gupta, S.K.: Resolution-adaptive risk-aware trajectory planning for surface vehicles operating in congested civilian traffic. Autonomous Robots, first Online (2015)Google Scholar
  20. 20.
    Siegwart, R., Nourbakhsh, I.R., Scaramuzza, D.: Introduction to Autonomous Mobile Robots, 2nd edn. MIT Press, Cambridge (2011)Google Scholar
  21. 21.
    Statheros, T., Howells, G., Maier, K.M.: Autonomous ship collision avoidance navigation concepts, technologies and techniques. J. Navig. 61(1), 129–142 (2008)CrossRefGoogle Scholar
  22. 22.
    Uras, T., Koenig, S.: An empirical comparison of any-angle path-planning algorithms. In: Proceedings of the 8th Annual Symposium on Combinatorial Search, pp. 206–210. Ein Gedi, Israel (2015)Google Scholar
  23. 23.
    Vaneck, T.W.: Fuzzy guidance controller for an autonomous boat. IEEE Control Syst. Mag. 17(2), 43–51 (1997)CrossRefGoogle Scholar
  24. 24.
    Vantorre, M., Delefortrie, G., Eloot, K., Laforce, E.: Experimental investigation of ship-bank interaction forces. In: Proceedings of International Conference on Marine Simulation and Ship Maneuverability, pp. 1–9. Kanazawa, Japan (2003)Google Scholar
  25. 25.
    Verstichel, J., Causmaecker, P.D., Spieksma, F., Berghe, G.V.: The generalized lock scheduling problem: an exact approach. Trans. Res. Part E: Logistics Transp. Rev. 65, 16–34 (2014)CrossRefzbMATHGoogle Scholar
  26. 26.
    Xin, J., Negenborn, R.R., Corman, F., Lodewijks, G.: Control of interacting machines in automated container terminals using a sequential planning approach for collision avoidance. Transp. Res. Part C: Emerg. Technol. 60(2015), 377–396 (2015)CrossRefGoogle Scholar
  27. 27.
    Zheng, H., Negenborn, R.R., Lodewijks, G.: Predictive path following with arrival time awareness for waterborne AGVs. Transportation Research Part C: Emerging Technologies (2015).

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Linying Chen
    • 1
    Email author
  • Rudy R. Negenborn
    • 1
  • Gabriel Lodewijks
    • 1
  1. 1.Department of Maritime and Transport TechnologyDelft University of TechnologyDelftThe Netherlands

Personalised recommendations