LOGARITHMIC-TIME LINK PATH QUERIES IN A SIMPLE POLYGON

1995 ◽  
Vol 05 (04) ◽  
pp. 369-395 ◽  
Author(s):  
ESTHER M. ARKIN ◽  
JOSEPH S.B. MITCHELL ◽  
SUBHASH SURI
Keyword(s):  
Data Structure ◽  
Shortest Path ◽  
Simple Polygon ◽  
Additive Constant ◽  
Time And Space ◽  
Link Distance ◽  
Path Distance ◽  
Path Queries ◽  
Shortest Path Distance ◽  
Small Additive

We develop a data structure for answering link distance queries between two arbitrary points in a simple polygon. The data structure requires O(n3) time and space for its construction and answers link distance queries in O(log n) time, after which a minimum-link path can be reported in time proportional to the number of links. Here, n denotes the number of vertices of the polygon. Our result extends to link distance queries between pairs of segments or polygons. We also propose a simpler data structure for computing a link distance approximately, where the error is bounded by a small additive constant. Finally, we also present a scheme for approximating the link and the shortest path distance simultaneously.

scholarly journals Optimal Shortest Path and Minimum-Link Path Queries between Two Convex Polygons inside a Simple Polygonal Obstacle

1997 ◽  
Vol 07 (01n02) ◽  
pp. 85-121 ◽  
Author(s):  
Yi-Jen Chiang ◽  
Roberto Tamassia
Keyword(s):  
Data Structure ◽  
Shortest Path ◽  
Dynamic Environment ◽  
Simple Polygon ◽  
Efficient Algorithms ◽  
Optimal Time ◽  
Convex Polygons ◽  
Separation Problem ◽  
Dynamic Case ◽  
Path Queries

We present efficient algorithms for shortest-path and minimum-link-path queries between two convex polygons inside a simple polygon P, which acts as an obstacle to be avoided. Let n be the number of vertices of P, and h the total number of vertices of the query polygons. We show that shortest-path queries can be performed optimally in time O( log h + log n) (plus O(k) time for reporting the k edges of the path) using a data structure with O(n) space and preprocessing time, and that minimum-link-path queries can be performed in optimal time O( log h + log n) (plus O(k) to report the k links), with O(n3) space and preprocessing time. We also extend our results to the dynamic case, and give a unified data structure that supports both queries for convex polygons in the same region of a connected planar subdivision [Formula: see text]. The update operations consist of insertions and deletions of edges and vertices. Let n be the current number of vertices in [Formula: see text]. The data structure uses O(n) space, supports updates in O( log 2 n) time, and performs shortest-path and minimum-link-path queries in times O( log h+ log 2n) (plus O(k) to report the k edges of the path) and O( log h + k log 2 n), respectively. Performing shortest-path queries is a variation of the well-studied separation problem, which has not been efficiently solved before in the presence of obstacles. Also, it was not previously known how to perform minimum-link-path queries in a dynamic environment, even for two-point queries.

FINDING AN OPTIMAL BRIDGE BETWEEN TWO POLYGONS

2002 ◽  
Vol 12 (03) ◽  
pp. 249-261 ◽  
Author(s):  
XUEHOU TAN
Keyword(s):  
Hierarchical Structure ◽  
Shortest Path ◽  
Line Segment ◽  
Euclidean Distance ◽  
Geodesic Distance ◽  
Simple Polygon ◽  
Time Algorithm ◽  
Search Trees ◽  
Bridge Problem ◽  
Path Queries

Let π(a,b) denote the shortest path between two points a, b inside a simple polygon P, which totally lies in P. The geodesic distance between a and b in P is defined as the length of π(a,b), denoted by gd(a,b), in contrast with the Euclidean distance between a and b in the plane, denoted by d(a,b). Given two disjoint polygons P and Q in the plane, the bridge problem asks for a line segment (optimal bridge) that connects a point p on the boundary of P and a point q on the boundary of Q such that the sum of three distances gd(p′,p), d(p,q) and gd(q,q′), with any p′ ∈ P and any q′ ∈ Q, is minimized. We present an O(n log 3 n) time algorithm for finding an optimal bridge between two simple polygons. This significantly improves upon the previous O(n2) time bound. Our result is obtained by making substantial use of a hierarchical structure that consists of segment trees, range trees and persistent search trees, and a structure that supports dynamic ray shooting and shortest path queries as well.

Walking an Unknown Street with Limited Sensing

2019 ◽  
Vol 33 (13) ◽  
pp. 1959042
Author(s):  
Qi Wei ◽  
Xuehou Tan ◽  
Yonggong Ren
Keyword(s):  
Data Structure ◽  
Lower Bound ◽  
Mobile Robot ◽  
Shortest Path ◽  
Competitive Strategy ◽  
Visible Region ◽  
Simple Polygon ◽  
Limited Sensing

This paper studies a searching problem in an unknown street. A simple polygon [Formula: see text] with two distinguished vertices, [Formula: see text] and [Formula: see text], is called a street if the two boundary chains from [Formula: see text] to [Formula: see text] are mutually weakly visible. We use a mobile robot to locate [Formula: see text] starting from [Formula: see text]. Assume that the robot has a limited sensing capability that can only detect the constructed edges (also called gaps) on the boundary of its visible region, but cannot measure any angle or distance. The robot does not have knowledge of the street in advance. We present a new competitive strategy for this problem and prove that the length of the path generated by the robot is at most 9-times longer than the shortest path. We also propose a matching lower bound to show that our strategy is optimal. Compared with the previous strategy, we further relaxed the restriction that the robot should take a marking device and use the data structure S-GNT. The analysis of our strategy is tight.

Exact Distance Query in Large Graphs through Fast Graph Simplification

2020 ◽  
Author(s):  
Jun Liu ◽  
Yicheng Pan ◽  
Qifu Hu
Keyword(s):  
Graph Theory ◽  
Shortest Path ◽  
The Other ◽  
Large Networks ◽  
Large Graphs ◽  
Path Distance ◽  
Hub Network ◽  
Graph Simplification ◽  
Shortest Path Distance ◽  
Labeling Method

Abstract Shortest path distance query is one of the most fundamental problems in graph theory and applications. Nowadays, the scale of graphs becomes so large that traditional algorithms for shortest path are not available to answer the exact distance query quickly. Many methods based on two-hop labeling have been proposed to solve this problem. However, they cost too much either in preprocessing or query phase to handle large networks containing as many as tens of millions of vertices. In this paper, we propose a novel $k$-hub labeling method to address this problem in large networks with less preprocessing cost while keeping the query time in the microsecond level on average. Technically, two types of labels are presented in our construction, one for distance queries when the actual distance is at most $k-2$, which we call local label, and the other for further distance queries, which we call hub label. Our approach of $k$-hub labeling is essentially different from previous widely used two-hop labeling framework since we construct labels by using hub network structure. We conduct extensive experiments on large real-world networks and the results demonstrate the higher efficiency of our method in preprocessing phase and the much smaller space size of constructed index compared to previous efficient two-hop labeling method, with a comparatively fast query speed.

scholarly journals α-Probabilistic flexible aggregate nearest neighbor search in road networks using landmark multidimensional scaling

2020 ◽  
Author(s):  
Moonyoung Chung ◽  
Woong-Kee Loh
Keyword(s):  
Multidimensional Scaling ◽  
Shortest Path ◽  
Road Network ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Road Networks ◽  
Path Distance ◽  
Aggregate Nearest Neighbor ◽  
Shortest Path Distance

AbstractIn spatial database and road network applications, the search for the nearest neighbor (NN) from a given query object q is the most fundamental and important problem. Aggregate nearest neighbor (ANN) search is an extension of the NN search with a set of query objects $$Q = \{ q_0, \dots , q_{M-1} \}$$ Q = { q 0 , ⋯ , q M - 1 } and finds the object $$p^*$$ p ∗ that minimizes $$g \{ d(p^*, q_i), q_i \in Q \}$$ g { d ( p ∗ , q i ) , q i ∈ Q } , where g (max or sum) is an aggregate function and d() is a distance function between two objects. Flexible aggregate nearest neighbor (FANN) search is an extension of the ANN search with the introduction of a flexibility factor $$\phi \, (0 < \phi \le 1)$$ ϕ ( 0 < ϕ ≤ 1 ) and finds the object $$p^*$$ p ∗ and the set of query objects $$Q^*_\phi $$ Q ϕ ∗ that minimize $$g \{ d(p^*, q_i), q_i \in Q^*_\phi \}$$ g { d ( p ∗ , q i ) , q i ∈ Q ϕ ∗ } , where $$Q^*_\phi $$ Q ϕ ∗ can be any subset of Q of size $$\phi |Q|$$ ϕ | Q | . This study proposes an efficient $$\alpha $$ α -probabilistic FANN search algorithm in road networks. The state-of-the-art FANN search algorithm in road networks, which is known as IER-$$k\hbox {NN}$$ k NN , used the Euclidean distance based on the two-dimensional coordinates of objects when choosing an R-tree node that most potentially contains $$p^*$$ p ∗ . However, since the Euclidean distance is significantly different from the actual shortest-path distance between objects, IER-$$k\hbox {NN}$$ k NN looks up many unnecessary nodes, thereby incurring many calculations of ‘expensive’ shortest-path distances and eventually performance degradation. The proposed algorithm transforms road network objects into k-dimensional Euclidean space objects while preserving the distances between them as much as possible using landmark multidimensional scaling (LMDS). Since the Euclidean distance after LMDS transformation is very close to the shortest-path distance, the lookup of unnecessary R-tree nodes and the calculation of expensive shortest-path distances are reduced significantly, thereby greatly improving the search performance. As a result of performance comparison experiments conducted for various real road networks and parameters, the proposed algorithm always achieved higher performance than IER-$$k\hbox {NN}$$ k NN ; the performance (execution time) of the proposed algorithm was improved by up to 10.87 times without loss of accuracy.

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