Advertisement

Grid-Based Angle-Constrained Path Planning

  • Konstantin YakovlevEmail author
  • Egor Baskin
  • Ivan Hramoin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9324)

Abstract

Square grids are commonly used in robotics and game development as spatial models and well known in AI community heuristic search algorithms (such as A*, JPS, Theta* etc.) are widely used for path planning on grids. A lot of research is concentrated on finding the shortest (in geometrical sense) paths while in many applications finding smooth paths (rather than the shortest ones but containing sharp turns) is preferable. In this paper we study the problem of generating smooth paths and concentrate on angle constrained path planning. We put angle-constrained path planning problem formally and present a new algorithm tailored to solve it – LIAN. We examine LIAN both theoretically and empirically. We show that it is sound and complete (under some restrictions). We also show that LIAN outperforms the analogues when solving numerous path planning tasks within urban outdoor navigation scenarios.

Keywords

Path planning Path finding Heuristic search Grids Grid worlds Angle constrained paths A* Theta* LIAN 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lozano-Pérez, T., Wesley, M.A.: An algorithm for planning collision-free paths among polyhedral obstacles. Communications of the ACM 22(10), 560–570 (1979)CrossRefGoogle Scholar
  2. 2.
    Bhattacharya, P., Gavrilova, M.L.: Roadmap-based path planning-Using the Voronoi diagram for a clearance-based shortest path. IEEE Robotics & Automation Magazine 15(2), 58–66 (2008)CrossRefGoogle Scholar
  3. 3.
    Kallmann, M.: Navigation queries from triangular meshes. In: Boulic, R., Chrysanthou, Y., Komura, T. (eds.) MIG 2010. LNCS, vol. 6459, pp. 230–241. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Yap, P.: Grid-Based Path-Finding. In: Cohen, R., Spencer, B. (eds.) Canadian AI 2002. LNCS (LNAI), vol. 2338, pp. 44–55. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Sturtevant, N.R.: Benchmarks for grid-based pathfinding. IEEE Transactions on Computational Intelligence and AI in Games 4(2), 144–148 (2012)CrossRefGoogle Scholar
  6. 6.
    Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)CrossRefGoogle Scholar
  7. 7.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4(2), 100–107 (1968)CrossRefGoogle Scholar
  9. 9.
    Likhachev, M., Gordon, G., Thrun, S.: ARA*: Anytime A* with Provable Bounds on Sub-Optimality, Advances in Neural Information Processing Systems 16 (NIPS). MIT Press, Cambridge (2004)Google Scholar
  10. 10.
    Botea, A., Muller, M., Schaeffer, J.: Near optimal hierarchical path finding. Journal of Game Development 1(1), 7–28 (2004)Google Scholar
  11. 11.
    Likhachev, M., Stentz, A.: R* Search. In: Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence. AAAI press, Menlo Park (2008)Google Scholar
  12. 12.
    Nash, A., Daniel, K., Koenig, S., Felner, A.: Theta*: any-angle path planning on grids. In: Proceedings of the National Conference on Artificial Intelligence, vol. 22, No. 2, p. 1177. AAAI Press, Menlo Park (2007)Google Scholar
  13. 13.
    Harabor, D., Grastien, A.: Online graph pruning for pathfinding on grid maps. In: AAAI 2011 (2011)Google Scholar
  14. 14.
    Kuwata, Y., Karaman, S., Teo, J., Frazzoli, E., How, J.P., Fiore, G.: Real-time motion planning with applications to autonomous urban driving. IEEE Transactions on Control Systems Technology 17(5), 1105–1118 (2009)CrossRefGoogle Scholar
  15. 15.
    Munoz, P., Rodriguez-Moreno, M.: Improving efficiency in any-angle path-planning algorithms. In: 2012 6th IEEE International Conference Intelligent Systems (IS), pp. 213–218. IEEE (2012)Google Scholar
  16. 16.
    Kim, H., Kim, D., Shin, J.U., Kim, H., Myung, H.: Angular rate-constrained path planning algorithm for unmanned surface vehicles. Ocean Engineering 84, 37–44 (2014)CrossRefGoogle Scholar
  17. 17.
    Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Systems Journal 4(1), 25–30 (1965)CrossRefGoogle Scholar
  18. 18.
    Pitteway, M.L.V.: Algorithms of conic generation. In: Fundamental Algorithms for Computer Graphics, pp. 219–237. Springer, HeidelbergGoogle Scholar
  19. 19.
  20. 20.
    Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. Journal of Artificial Intelligence Research 36(1), 267–306 (2009)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Konstantin Yakovlev
    • 1
    Email author
  • Egor Baskin
    • 1
  • Ivan Hramoin
    • 1
  1. 1.Institute for Systems Analysis of Russian Academy of SciencesMoscowRussia

Personalised recommendations