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Random disambiguation paths for traversing a mapped hazard field

Author

Listed:
  • Carey E. Priebe
  • Donniell E. Fishkind
  • Lowell Abrams
  • Christine D. Piatko

Abstract

We consider the problem of safely and swiftly navigating through a spatial arrangement of potential hazard detections in which each detection has associated with it a probability that the detection is indeed a true hazard. When in close proximity to a detection, we assume the ability—for a cost—to determine whether or not the hazard is real. Our approach to this problem involves a new object, the random disambiguation path (RDP), which is a curve‐valued random variable parametrized by a binary tree with particular properties. We prove an admissibility result showing that there is positive probability that the use of an RDP reduces the expected traversal length compared to the conventional shortest zero‐risk path, and we introduce a practically computable additive‐constant approximation to the optimal RDP. The theoretical considerations are complemented by simulation and example. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005

Suggested Citation

  • 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.
    • Handle: RePEc:wly:navres:v:52:y:2005:i:3:p:285-292
    DOI: 10.1002/nav.20071
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    References listed on IDEAS

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    1. Dimitri P. Bertsekas & John N. Tsitsiklis, 1991. "An Analysis of Stochastic Shortest Path Problems," Mathematics of Operations Research, INFORMS, vol. 16(3), pages 580-595, August.
    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 & 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.
    2. X Ye & D E Fishkind & L Abrams & C E Priebe, 2011. "Sensor information monotonicity in disambiguation protocols," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 142-151, January.
    3. 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.
    4. 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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