eLIAN: Enhanced Algorithm for Angle-Constrained Path Finding

  • Anton AndreychukEmail author
  • Natalia Soboleva
  • Konstantin Yakovlev
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 934)


Problem of finding 2D paths of special shape, e.g. paths comprised of line segments having the property that the angle between any two consecutive segments does not exceed the predefined threshold, is considered in the paper. This problem is harder to solve than the one when shortest paths of any shape are sought, since the planer’s search space is substantially bigger as multiple search nodes corresponding to the same location need to be considered. One way to reduce the search effort is to fix the length of the path’s segment and to prune the nodes that violate the imposed constraint. This leads to incompleteness and to the sensitivity of the’s performance to chosen parameter value. In this work we introduce a novel technique that reduces this sensitivity by automatically adjusting the length of the path’s segment on-the-fly, e.g. during the search. Embedding this technique into the known grid-based angle-constrained path finding algorithm LIAN, leads to notable increase of the planner’s effectiveness, e.g. success rate, while keeping efficiency, e.g. runtime, overhead at reasonable level. Experimental evaluation shows that LIAN with the suggested enhancements, dubbed eLIAN, solves up to 20% of tasks more compared to the predecessor. Meanwhile, the solution quality of eLIAN is nearly the same as the one of LIAN.


Path planning Path finding Grid Angle-constrained LIAN 



The work was partially supported by the “RUDN University Program 5–100” and by the special program of the presidium of Russian Academy of Sciences.


  1. 1.
    Botea, A., Müller, M., Schaeffer, J.: Near optimal hierarchical path-finding. J. Game Dev. 1(1), 7–28 (2004)Google Scholar
  2. 2.
    Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965)CrossRefGoogle Scholar
  3. 3.
    Daniel, K., Nash, A., Koenig, S., Felner, A.: Theta*: any-angle path planning on grids. J. Artif. Intell. Res. 39, 533–579 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)CrossRefGoogle Scholar
  5. 5.
    Harabor, D., Grastien, A., Oz, D., Aksakalli, V.: Optimal any-angle pathfinding in practice. J. Artif. Intell. Res. 56, 89–118 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Harabor, D.D., Grastien, A.: Online graph pruning for pathfinding on grid maps. In: Proceedings of The 25th AAAI Conference on Artificial Intelligence (AAAI-2011), pp. 1114–1119 (2011)Google Scholar
  7. 7.
    Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)CrossRefGoogle Scholar
  8. 8.
    Kim, H., Kim, D., Shin, J.U., Kim, H., Myung, H.: Angular rate-constrained path planning algorithm for unmanned surface vehicles. Ocean Eng. 84, 37–44 (2014)CrossRefGoogle Scholar
  9. 9.
    Nash, A., Koenig, S., Tovey, C.: Lazy theta*: any-angle path planning and path length analysis in 3D. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI-2010), pp. 147–154. AAAI Press (2010)Google Scholar
  10. 10.
    Silver, D.: Cooperative pathfinding. In: Proceedings of The 1st Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-2005), pp. 117–122 (2005)Google Scholar
  11. 11.
    Sturtevant, N.R.: Benchmarks for grid-based pathfinding. IEEE Trans. Comput. Intell. AI Games 4(2), 144–148 (2012)CrossRefGoogle Scholar
  12. 12.
    Thrun, S.: Learning occupancy grid maps with forward sensor models. Auton. Robots 15(2), 111–127 (2003)CrossRefGoogle Scholar
  13. 13.
    Xu, H., Shu, L., Huang, M.: Planning paths with fewer turns on grid maps. In: Proceedings of The 6th Annual Symposium on Combinatorial Search (SoCS-2013), pp. 193–201 (2013)Google Scholar
  14. 14.
    Yakovlev, K., Baskin, E., Hramoin, I.: Grid-based angle-constrained path planning. In: Hölldobler, S., Krötzsch, M., Peñaloza, R., Rudolph, S. (eds.) KI 2015. LNCS (LNAI), vol. 9324, pp. 208–221. Springer, Cham (2015). Scholar
  15. 15.
    Yap, P.: Grid-based path-finding. In: Cohen, R., Spencer, B. (eds.) AI 2002. LNCS (LNAI), vol. 2338, pp. 44–55. Springer, Heidelberg (2002). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Anton Andreychuk
    • 1
    Email author
  • Natalia Soboleva
    • 2
  • Konstantin Yakovlev
    • 2
    • 3
    • 4
  1. 1.Peoples’ Friendship University of RussiaMoscowRussia
  2. 2.National Research University Higher School of EconomicsMoscowRussia
  3. 3.Federal Research Center “Computer Science and Control” of Russian Academy of SciencesMoscowRussia
  4. 4.Moscow Institute of Physics and TechnologyDolgoprudnyRussia

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