A fundamental problem in computational geometry is to compute an obstacle-avoiding Euclidean shortest path between two points in the plane. The case of this problem on polygonal obstacles is well studied. In this paper, we consider the problem version on curved obstacles, commonly modeled as splinegons. A splinegon can be viewed as replacing each edge of a polygon by a convex curved edge (polygons are special splinegons). Each curved edge is assumed to be of O(1) complexity. Given in the plane two points s and t and a set of @math pairwise disjoint splinegons with a total of @math vertices, we compute a shortest s-to-t path avoiding the splinegons, in @math time, where k is a parameter sensitive to the structures of the input splinegons and is upper-bounded by @math . In particular, when all splinegons are convex, @math is proportional to the number of common tangents in the free space (called "free common tangents") among the splinegons. We develop techniques for solving the problem on the general (non-convex) splinegon domain, which also improve several previous results. In particular, our techniques produce an optimal output-sensitive algorithm for a basic visibility problem of computing all free common tangents among @math pairwise disjoint convex splinegons with a total of @math vertices. Our algorithm runs in @math time and @math space, where @math is the number of all free common tangents. Even for the special case where all splinegons are convex polygons, the previously best algorithm for this visibility problem takes @math time.
The polygon case of (i.e., @math contains polygons only) is well studied. By constructing the visibility graph @cite_22 , a shortest @math - @math path can be found in @math time, where @math is the size of the visibility graph. By building a shortest path map, Storer and Reif solved this case in @math time @cite_15 . Mitchell @cite_9 gave the first subquadratic, @math time algorithm for it based on the continuous Dijkstra approach. Also using the continuous Dijkstra approach and a conforming planar subdivision, Hershberger and Suri @cite_26 presented an @math time solution. An @math time algorithm was given in @cite_4 (a preliminary version is in @cite_29 and full details are in @cite_12 ). Thus, our algorithm improves the results in @cite_4 @cite_15 and is faster than the @math time solution @cite_26 for small value @math , say @math . Very recently, an unrefereed report @cite_32 announced an algorithm for the polygon case based on the continuous Dijkstra approach with an @math time. Our algorithm is superior to it when @math .
@cite_26 @cite_4 @cite_22 @cite_9 @cite_29 @cite_32 @cite_15 @cite_12
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We give a subquadratic (O(n3 2+∊) time and O(n) space) algorithm for computing Euclidean shortest paths in the plane in the presence of polygonal obstacles; previous time bounds were at least quadratic in n, in the worst case. The method avoids use of visibility graphs, relying instead on the continuous Dijkstra paradigm. The output is a shortest path map (of size O(n)) with respect to a given source point, which allows shortest path length queries to be answered in time O(log n). The algorithm extends to the case of multiple source points, yielding a method to compute a Voronoi diagram with respect to the shortest path metric. We present an algorithm to find an Euclidean Shortest Path from a source vertex @math to a sink vertex @math in the presence of obstacles in @math . Our algorithm takes @math time and @math space. Here, @math is the time to triangulate the polygonal region, @math is the number of obstacles, and @math is the number of vertices. This bound is close to the known lower bound of @math time and @math space. Our approach involve progressing shortest path wavefront as in continuous Dijkstra-type method, and confining its expansion to regions of interest. The well-known @math -disjoint path problem ( @math -DPP) asks for pairwise vertex-disjoint paths between @math specified pairs of vertices @math in a given graph, if they exist. The decision version of the shortest @math -DPP asks for the length of the shortest (in terms of total length) such paths. Similarly the search and counting versions ask for one such and the number of such shortest set of paths, respectively. We restrict attention to the shortest @math -DPP instances on undirected planar graphs where all sources and sinks lie on a single face or on a pair of faces. We provide efficient sequential and parallel algorithms for the search versions of the problem answering one of the main open questions raised by Colin de Verdiere and Schrijver for the general one-face problem. We do so by providing a randomised @math algorithm along with an @math time randomised sequential algorithm. We also obtain deterministic algorithms with similar resource bounds for the counting and search versions. In contrast, previously, only the sequential complexity of decision and search versions of the "well-ordered" case has been studied. For the one-face case, sequential versions of our routines have better running times for constantly many terminals. In addition, the earlier best known sequential algorithms (e.g. ) were randomised while ours are also deterministic. The algorithms are based on a bijection between a shortest @math -tuple of disjoint paths in the given graph and cycle covers in a related digraph. This allows us to non-trivially modify established techniques relating counting cycle covers to the determinant. We further need to do a controlled inclusion-exclusion to produce a polynomial sum of determinants such that all "bad" cycle covers cancel out in the sum allowing us to count "good" cycle covers. The problem of determining shortest paths through a weighted planar polygonal subdivision with n vertices is considered. Distances are measured according to a weighted Euclidean metric: The length of a path is defined to be the weighted sum of (Euclidean) lengths of the subpaths within each region. An algorithm that constructs a (restricted) “shortest path map” with respect to a given source point is presented. The output is a partitioning of each edge of the subdivion into intervals of e-optimality, allowing an e-optimal path to be traced from the source to any query point along any edge. The algorithm runs in worst-case time O ( ES ) and requires O ( E ) space, where E is the number of “events” in our algorithm and S is the time it takes to run a numerical search procedure. In the worst case, E is bounded above by O ( n 4 ) (and we give an O( n 4 ) lower bound), but it is likeky that E will be much smaller in practice. We also show that S is bounded by O ( n 4 L ), where L is the precision of the problem instance (including the number of bits in the user-specified tolerance e). Again, the value of S should be smaller in practice. The algorithm applies the “continuous Dijkstra” paradigm and exploits the fact that shortest paths obey Snell's Law of Refraction at region boundaries, a local optimaly property of shortest paths that is well known from the analogous optics model. The algorithm generalizes to the multi-source case to compute Voronoi diagrams.