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Optimizing Visibility-based Search in Polygonal Domains
- Source :
- 19th Scandinavian Symposium on Algorithm Theory, SWAT 2024
- Publication Year :
- 2024
-
Abstract
- Given a geometric domain $P$, visibility-based search problems seek routes for one or more mobile agents ("watchmen") to move within $P$ in order to be able to see a portion (or all) of $P$, while optimizing objectives, such as the length(s) of the route(s), the size (e.g., area or volume) of the portion seen, the probability of detecting a target distributed within $P$ according to a prior distribution, etc. The classic watchman route problem seeks a shortest route for an observer, with omnidirectional vision, to see all of $P$. In this paper we study bicriteria optimization problems for a single mobile agent within a polygonal domain $P$ in the plane, with the criteria of route length and area seen. Specifically, we address the problem of computing a minimum length route that sees at least a specified area of $P$ (minimum length, for a given area quota). We also study the problem of computing a length-constrained route that sees as much area as possible. We provide hardness results and approximation algorithms. In particular, for a simple polygon $P$ we provide the first fully polynomial-time approximation scheme for the problem of computing a shortest route seeing an area quota, as well as a (slightly more efficient) polynomial dual approximation. We also consider polygonal domains $P$ (with holes) and the special case of a planar domain consisting of a union of lines. Our results yield the first approximation algorithms for computing a time-optimal search route in $P$ to guarantee some specified probability of detection of a static target within $P$, randomly distributed in $P$ according to a given prior distribution.
- Subjects :
- Computer Science - Computational Geometry
F.2.2
I.3.5
Subjects
Details
- Database :
- arXiv
- Journal :
- 19th Scandinavian Symposium on Algorithm Theory, SWAT 2024
- Publication Type :
- Report
- Accession number :
- edsarx.2402.05420
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.4230/LIPIcs.SWAT.2024.27