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The reset disambiguation policy for navigating stochastic obstacle fields

Author

Listed:
  • Vural Aksakalli
  • Donniell E. Fishkind
  • Carey E. Priebe
  • Xugang Ye

Abstract

The problem we consider is a stochastic shortest path problem in the presence of a dynamic learning capability. Specifically, a spatial arrangement of possible obstacles needs to be traversed as swiftly as possible, and the status of the obstacles may be disambiguated (at a cost) en route. No efficiently computable optimal policy is known, and many similar problems have been proven intractable. In this article, we adapt a policy which is optimal for a related problem and prove that this policy is indeed also optimal for a restricted class of instances of our problem. Otherwise, this policy is generally suboptimal but, nonetheless, it is both effective and efficiently computable. Examples/simulations are provided in a mine countermeasures application. Of central use is the Tangent Arc Graph, a polynomially sized topological superimposition of exponentially many visibility graphs. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011

Suggested Citation

  • Vural Aksakalli & Donniell E. Fishkind & Carey E. Priebe & Xugang Ye, 2011. "The reset disambiguation policy for navigating stochastic obstacle fields," Naval Research Logistics (NRL), John Wiley & Sons, vol. 58(4), pages 389-399, June.
    • Handle: RePEc:wly:navres:v:58:y:2011:i:4:p:389-399
    DOI: 10.1002/nav.20454
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    References listed on IDEAS

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    1. Carey E. Priebe & Donniell E. Fishkind & Lowell Abrams & Christine D. Piatko, 2005. "Random disambiguation paths for traversing a mapped hazard field," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(3), pages 285-292, April.
    2. Priebe, Carey E. & Naiman, Daniel Q. & Cope, Leslie M., 2001. "Importance sampling for spatial scan analysis: computing scan statistic p-values for marked point processes," Computational Statistics & Data Analysis, Elsevier, vol. 35(4), pages 475-485, February.
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    Cited by:

    1. Vural Aksakalli & O. Furkan Sahin & Ibrahim Ari, 2016. "An AO* Based Exact Algorithm for the Canadian Traveler Problem," INFORMS Journal on Computing, INFORMS, vol. 28(1), pages 96-111, February.
    2. Vural Aksakalli & Ibrahim Ari, 2014. "Penalty-Based Algorithms for the Stochastic Obstacle Scene Problem," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 370-384, May.

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