Improved Anytime D* Algorithm

  • Weiya Yue
  • John Franco
  • Qiang Han
  • Weiwei CaoEmail author
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 247)


In the dynamic domain, agents often operate in the terrain which is only incompletely known and can be dynamically updated on the fly. In this case, dynamic navigation algorithm, which is required to find out an optimal solution to its goal, has been an important component in planning. However, under the environments where time is more critical than optimality, a sub-optimal solution is required. Therefore the challenge for practical applications is to find a high sub-optimal solution in limited time. The dynamic algorithm Anytime D*(AD*) is currently the best anytime algorithm which aims to return a high sub-optimal solution with short corresponding time and control of sub-optimality. In this chapter, a new algorithm named Improved Anytime D*(IAD*) is introduced. By reducing the search space, experiment results show IAD* better outperforms Anytime D* in various random benchmarks.


Anytime algorithm Artificial intelligence D* lite Incremental algorithm Navigation algorithm Planning 



This work was supported by University Research Council’s (URC), 2012 Summer Graduate Student Research Fellowships funded by University of Cincinnati.


  1. 1.
    Davis HW, Bramanti-Gregor A, Wang J (1988) The advantages of using depth and breadth components in heuristic search. Methodol Intell Syst 3:19–28Google Scholar
  2. 2.
    Hansen EA, Zhou R (2007) Anytime heuristic search. J Artif Intell Res 28:267–297MathSciNetzbMATHGoogle Scholar
  3. 3.
    Harris L (1974) The heuristic search under conditions of error. Artif Intell 5(3):217–234CrossRefzbMATHGoogle Scholar
  4. 4.
    Koenig S, Likhachev M (2002) D*lite. In: Eighteenth national conference on, artificial intelligence, pp 476–483Google Scholar
  5. 5.
    S. Koenig, Likhachev M (2002) Improved fast replanning for robot navigation in unknown, Terrain, pp 968–975Google Scholar
  6. 6.
    Koenig S, Likhachev M (2002) Incremental a*. Advances in neural information processing systems, pp 1539–1546Google Scholar
  7. 7.
    Koenig S, Likhachev M, Furcy D (2004) Lifelong planning a*. Artif Intell J 155(1–2):93–146MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Korf R (1993) Linear-space best-first search. Artif Intell 62(1):41–78MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    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 schedulingGoogle Scholar
  10. 10.
    Likhachev M, Gordon G, Thrun S (2003) Ara*: anytime a* with provable bounds on sub-optimality. Adv Neural Inf Process SystGoogle Scholar
  11. 11.
    Pohl I (1970) Heuristic search viewed as path finding in a graph. Artif Intell 1(3):193–204MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Stentz A (1995) The focussed d* algorithm for real-time replanning. In: Proceedings of the international joint conference on, artificial intelligence, pp 1652–1659Google Scholar
  13. 13.
    Stentz A (1997) Optimal and efficient path planning for partially-known environments, vol 388. The Kluwer international series in engineering and computer science, pp 203–220Google Scholar
  14. 14.
    Thayer JT, Ruml W (2008) Faster than weighted a*: an optimal approach to bounded suboptimal search. In: Proceedings of the international conference on automated planning and schedulingGoogle Scholar
  15. 15.
    Yue W, Franco J (2009) Avoiding unnecessary calculations in robot navigation. In: Proceedings of world congress on engineering and computer science, pp 718–723Google Scholar
  16. 16.
    Yue W, Franco J (2010) A new way to reduce computing in navigation algorithm. J Eng Lett 18(4):\(\text{ EL }\_\text{18 }\_\text{4 }\_\text{03 }\) Google Scholar
  17. 17.
    Yue W, Franco J, Cao W, Han Q (2012) A new anytime dynamic navigation algorithm. In: Proceedings of the world congress on engineering and computer science 2012, WCECS 2012, vol 1, pp 17–22, San Francisco, USA, 24–26 Oct 2012Google Scholar
  18. 18.
    Yue W, Franco J, Cao W, Yue H (2011) Id* lite: improved d* lite algorithm. In: Proceedings of 26th symposium on applied, computing, pp 1364–1369Google Scholar
  19. 19.
    Zhou R, Hansen E (2002) Multiple sequence alignment using anytime a*. In: Proceedings of conference on articial, intelligence, pp 975–976Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CincinnatiCincinnatiUSA
  2. 2.Institute of Information EngneeringChinese Academy of ScienceBeijingChina

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