A Sweep Line Algorithm for Polygonal Chain Intersection and Its Applications
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Automatic Generation of Smooth Paths Bounded by Polygonal Chains
Automatic Generation of Smooth Paths Bounded by Polygonal Chains T. Berglund1, U. Erikson1, H. Jonsson1, K. Mrozek2, and I. S¨oderkvist3 1 Department of Computer Science and Electrical Engineering, Lule˚a University of Technology, SE-971 87 Lule˚a, Sweden {Tomas.Berglund,Ulf.Erikson,Hakan.Jonsson}@sm.luth.se 2 Q Navigator AB, SE-971 75 Lule˚a, Sweden [email protected] 3 Department of Mathematics, Lule˚a University of Technology, SE-971 87 Lule˚a, Sweden [email protected] Abstract We consider the problem of planning smooth paths for a vehicle in a region bounded by polygonal chains. The paths are represented as B-spline functions. A path is found by solving an optimization problem using a cost function designed to care for both the smoothness of the path and the safety of the vehicle. Smoothness is defined as small magnitude of the derivative of curvature and safety is defined as the degree of centering of the path between the polygonal chains. The polygonal chains are preprocessed in order to remove excess parts and introduce safety margins for the vehicle. The method has been implemented for use with a standard solver and tests have been made on application data provided by the Swedish mining company LKAB. 1 Introduction We study the problem of planning a smooth path between two points in a planar map described by polygonal chains. Here, a smooth path is a curve, with continuous derivative of curvature, that is a solution to a certain optimization problem. Today, it is not known how to find a closed form of an optimal path. -
Sweeping the Sphere
Sweeping the Sphere Joao˜ Dinis Margarida Mamede Departamento de F´ısica CITI, Departamento de Informatica´ Faculdade de Ciencias,ˆ Universidade de Lisboa Faculdade de Cienciasˆ e Tecnologia, FCT Campo Grande, Edif´ıcio C8 Universidade Nova de Lisboa 1749-016 Lisboa, Portugal 2829-516 Caparica, Portugal [email protected] [email protected] Abstract—We introduce the first sweep line algorithm for and Hong [5], [6], which is O(n log n) worst case optimal, computing spherical Voronoi diagrams, which proves that where n is the number of sites. An asymptotically slower Fortune’s method can be extended to points on a sphere alternative in the worst case is the randomized incremental surface. This algorithm is similar to Fortune’s plane sweep algorithm, sweeping the sphere with a circular line instead of algorithm of Clarkson and Shor [7], whose expected running a straight one. time is O(n log n). The well-known Quickhull algorithm of Like its planar counterpart, the novel linear-space algorithm Barber et al. [8] can be seen as an efficient variation of the has worst-case optimal running time. Furthermore, it copes previous algorithm. very well with degeneracies and is easy to implement. Experi- A different approach is to adapt the randomized incremen- mental results show that the performance of our algorithm is very similar to that of Fortune’s algorithm, both with synthetic tal algorithm that computes the planar Delaunay triangula- data sets and with real data. tion [9], [10]. The essential operation used in the incremental The usual solutions make use of the connection between step, which checks if a circle defined by three sites contains a convex hulls and spherical Delaunay triangulations. -
Uwaterloo Latex Thesis Template
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Waterloo's Institutional Repository Approximation Algorithms for Geometric Covering Problems for Disks and Squares by Nan Hu A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Mathematics in Computer Science Waterloo, Ontario, Canada, 2013 c Nan Hu 2013 I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Abstract Geometric covering is a well-studied topic in computational geometry. We study three covering problems: Disjoint Unit-Disk Cover, Depth-(≤ K) Packing and Red-Blue Unit- Square Cover. In the Disjoint Unit-Disk Cover problem, we are given a point set and want to cover the maximum number of points using disjoint unit disks. We prove that the problem is NP-complete and give a polynomial-time approximation scheme (PTAS) for it. In Depth-(≤ K) Packing for Arbitrary-Size Disks/Squares, we are given a set of arbitrary-size disks/squares, and want to find a subset with depth at most K and maxi- mizing the total area. We prove a depth reduction theorem and present a PTAS. In Red-Blue Unit-Square Cover, we are given a red point set, a blue point set and a set of unit squares, and want to find a subset of unit squares to cover all the blue points and the minimum number of red points. -
Voronoi Diagram of Polygonal Chains Under the Discrete Frechet Distance
Voronoi Diagram of Polygonal Chains under the Discrete Fr´echet Distance Sergey Bereg1, Kevin Buchin2,MaikeBuchin2, Marina Gavrilova3, and Binhai Zhu4 1 Department of Computer Science, University of Texas at Dallas, Richardson, TX 75083, USA [email protected] 2 Department of Information and Computing Sciences, Universiteit Utrecht, The Netherlands {buchin,maike}@cs.uu.nl 3 Department of Computer Science, University of Calgary, Calgary, Alberta T2N 1N4, Canada [email protected] 4 Department of Computer Science, Montana State University, Bozeman, MT 59717-3880, USA [email protected] Abstract. Polygonal chains are fundamental objects in many applica- tions like pattern recognition and protein structure alignment. A well- known measure to characterize the similarity of two polygonal chains is the (continuous/discrete) Fr´echet distance. In this paper, for the first time, we consider the Voronoi diagram of polygonal chains in d-dimension under the discrete Fr´echet distance. Given a set C of n polygonal chains in d-dimension, each with at most k vertices, we prove fundamental proper- ties of such a Voronoi diagram VDF (C). Our main results are summarized as follows. dk+ – The combinatorial complexity of VDF (C)isatmostO(n ). dk – The combinatorial complexity of VDF (C)isatleastΩ(n )fordi- mension d =1, 2; and Ω(nd(k−1)+2) for dimension d>2. 1 Introduction The Fr´echet distance was first defined by Maurice Fr´echet in 1906 [8]. While known as a famous distance measure in the field of mathematics (more specifi- cally, abstract spaces), it was first applied in measuring the similarity of polygo- nal curves by Alt and Godau in 1992 [2]. -
Visibility Graphs and Cell Decompositions
Visibility Graphs and Cell Decompositions CIS 390 Kostas Daniilidis With material from Chapter 5 - Roadmaps Principles of Robot Motion: Theory, Algorithms, and Implementation by Howie ChosetÂet al. The MIT Press © 2005 For polygonal obstacles in 2D • Assume robot is a point • Any shortest path from start to goal among a set of disjoint polygonal obstacles is a polygonal path whose inner vertices are convex vertices of the obstacles (imagine a tight rope from start to end) Visibility graph • Vertices: all vertices of the polygonal obstacles plus the start and the goal • Edges: all line segments between vertices that do not intersect obstacles • It is not a planar graph: edges are crossing (not at vertices) Visibility graph construction • Brute force O(n^3) algorithm visits – all n vertices – applies a rotational plane sweep that connects with all other n vertices – and determines whether each segment intersects any of the O(n) edges • We can do better by sorting the vertices. Sweep Line Algorithm • Input: A set of vertices {νi} (whose edges do not intersect) and a vertex ν • Output: A subset of vertices from {νi} that are within line of sight of ν • 1: For each vertex vi, calculate αi, the angle from the horizontal axis to the line segment • ννi. • 2: Create the vertex list E , containing the αi 's sorted in increasing order. • 3: Create the active list S, containing the sorted list of edges that intersect the horizontal • half-line emanating from ν. • 4: for all αi do • 5: if νi is visible to ν then • 6: Add the edge (ν, νi )to the visibility graph. -
Sweep Line Algorithm
Sweep Line Algorithm • Event C: Intersection point – Report the point. – Swap the two intersecting line segments in the sweep line status data structure. – For the new upper segment: test it against its predecessor for an intersection. – Insert intersection point (if found) into event queue. – Similar for new lower segment (with successor). • Complexity: – k such events – O(lg n) each – O(k lg n) total. Jacobs University Visualization and Computer Graphics Lab CH08-320201: Algorithms and Data Structures 550 Example a3 b4 Sweep Line status: s3 e1 s4 s0, s1, s2, s3 Event Queue: a2 a4 b3 a4, b1, b2, b0, b3, b4 s2 b1 b0 s1 b2 s0 a1 a0 Jacobs University Visualization and Computer Graphics Lab CH08-320201: Algorithms and Data Structures 551 Example a3 b4 Actions: s3 Insert s4 to SLS e1 s4 Test s4-s3 and s4-s2. a2 a4 b3 Add e1 to EQ s2 b1 Sweep Line status: b0 s1 b2 s0, s1, s2, s4, s3 s0 a1 a0 Event Queue: b1, e1, b2, b0, b3, b4 Jacobs University Visualization and Computer Graphics Lab CH08-320201: Algorithms and Data Structures 552 Example a3 b4 Actions: s3 e1 s4 Delete s1 from SLS Test s0-s2. a2 a4 b3 Add e2 to EQ s2 Sweep Line status: e2 b0 b1 s1 b2 s0, s2, s4, s3 s0 a1 a0 Event Queue: e1, e2, b2, b0, b3, b4 Jacobs University Visualization and Computer Graphics Lab CH08-320201: Algorithms and Data Structures 553 Example a3 b4 Actions: s3 e1 s4 Swap s3 and s4 in SLS Test s3-s2. -
Parallel Range, Segment and Rectangle Queries with Augmented Maps
Parallel Range, Segment and Rectangle Queries with Augmented Maps Yihan Sun Guy E. Blelloch Carnegie Mellon University Carnegie Mellon University [email protected] [email protected] Abstract The range, segment and rectangle query problems are fundamental problems in computational geometry, and have extensive applications in many domains. Despite the significant theoretical work on these problems, efficient implementations can be complicated. We know of very few practical implementations of the algorithms in parallel, and most implementations do not have tight theoretical bounds. In this paper, we focus on simple and efficient parallel algorithms and implementations for range, segment and rectangle queries, which have tight worst-case bound in theory and good parallel performance in practice. We propose to use a simple framework (the augmented map) to model the problem. Based on the augmented map interface, we develop both multi-level tree structures and sweepline algorithms supporting range, segment and rectangle queries in two dimensions. For the sweepline algorithms, we also propose a parallel paradigm and show corresponding cost bounds. All of our data structures are work-efficient to build in theory (O(n log n) sequential work) and achieve a low parallel depth (polylogarithmic for the multi-level tree structures, and O(n) for sweepline algorithms). The query time is almost linear to the output size. We have implemented all the data structures described in the paper using a parallel augmented map library. Based on the library each data structure only requires about 100 lines of C++ code. We test their performance on large data sets (up to 108 elements) and a machine with 72-cores (144 hyperthreads). -
Variations of Enclosing Problem Using Axis Parallel Square(S): a General Approach
American Journal of Computational Mathematics, 2014, 4, 197-205 Published Online June 2014 in SciRes. http://www.scirp.org/journal/ajcm http://dx.doi.org/10.4236/ajcm.2014.43016 Variations of Enclosing Problem Using Axis Parallel Square(s): A General Approach Priya Ranjan Sinha Mahapatra Department of Computer Science and Engineering, University of Kalyani, Kalyani, India Email: [email protected] Received 16 December 2013; revised 6 January 2014; accepted 15 January 2014 Copyright © 2014 by author and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Let P be a set of n points in two dimensional plane. For each point pP∈ , we locate an axis- parallel unit square having one particular side passing through p and enclosing the maximum number of points from P . Considering all points pP∈ , such n squares can be reported in On( log n) time. We show that this result can be used to (i) locate m (> 2) axis-parallel unit squares which are pairwise disjoint and they together enclose the maximum number of points from P (if exists) and (ii) find the smallest axis-parallel square enclosing at least k points of P , 2 ≤≤kn. Keywords Axis-Parallel Unit Square, Sweep Line Algorithm, Maximium Enclosing Problem, K-Enclosing Problem 1. Introduction Given a set P= { pp12,,, pn } points in a plane, enclosing problem in computational geometry is concerned with finding the smallest geometrical object of a given type that encloses all the points of P . Some well known instances of the enclosing problem are finding minimum enclosing circle [1], minimum area triangle [2], minimum area rectangle [3], minimum bounding box [4], and smallest width annulus [5]. -
Dynamic Convex Hull for Simple Polygonal Chains in Constant Amortized Time Per Update
EuroCG 2015, Ljubljana, Slovenia, March 16{18, 2015 Dynamic Convex Hull for Simple Polygonal Chains in Constant Amortized Time per Update Norbert Bus∗ Lilian Buzer∗ Abstract Our contribution In this paper we give an on-line algorithm to construct the dynamic convex hull of a We present a new algorithm to construct a dynamic simple polygonal chain in the Euclidean plane sup- convex hull in the Euclidean plane, supporting inser- porting deletion of points from the back of the chain tion and deletion of points. Both operations require and insertion of points in the front of the chain. Both amortized constant time. At each step the vertices of operations require amortized constant time consid- the convex hull are accessible in constant time. The ering the real-RAM model. The main idea of the algorithm is on-line, does not require prior knowledge algorithm is to maintain two convex hulls, one for of all the points. The only assumptions are that the efficiently handling insertions and one for deletions. points have to be located on a simple polygonal chain These two hulls constitute the convex hull of the and that the insertions and deletions must be carried polygonal chain. out in the order induced by the polygonal chain. 2 Overview of our algorithm 1 Introduction Our algorithm works in phases. For a precise for- mulation let us first define some necessary notations. The convex hull of a set of n points in a Euclidean A polygonal chain S in the Euclidean plane, with space is the smallest convex set containing all the n vertices, is defined as an ordered list of vertices points. -
Geometric Search·Intersection
6.3 Geometric Search Overview Geometric objects. Points, lines, intervals, circles, rectangles, polygons, ... This lecture. Intersection among N objects. Example problems. • 1D range search. • 2D range search. • Find all intersections among h-v line segments. ‣ range search • Find all intersections among h-v rectangles. ‣ space partitioning trees ‣ intersection search th Algorithms in Java, 4 Edition · Robert Sedgewick and Kevin Wayne · Copyright © 2009 · January 26, 2010 8:49:21 AM 2 1d range search Extension of ordered symbol table. • Insert key-value pair. • Search for key k. • Rank: how many keys less than k? • Range search: find all keys between k1 and k2. Application. Database queries. ‣ range search ‣ space partitioning trees insert B B ‣ intersection search Geometric interpretation. insert D B D Keys are point on a line. insert A A B D • insert I A B D I • How many points in a given interval? insert H A B D H I insert F A B D F H I insert P A B D F H I P count G to K 2 search G to K H I 3 4 1d range search: implementations 1d range search: BST implementation Ordered array. Slow insert, binary search for lo and hi to find range. Range search. Find all keys between lo and hi? Hash table. No reasonable algorithm (key order lost in hash). • Recursively find all keys in left subtree (if any could fall in range). • Check key in current node. • Recursively find all keys in right subtree (if any could fall in range). data structure insert rank range count range search searching in the range [F..T] red keys are used in compares ordered array N log N log N R + log N but are not in the range S hash table 1 N N N E X A R BST log N log N log N R + log N C H M L P black keys are N = # keys in the range R = # keys that match Range search in a BST BST. -
Efficient Geometric Algorithms in the EREW-PRAM
Purdue University Purdue e-Pubs Department of Computer Science Technical Reports Department of Computer Science 1989 Efficient Geometric Algorithms in the EREW-PRAM Danny Z. Chen Report Number: 89-928 Chen, Danny Z., "Efficient Geometric Algorithms in the EREW-PRAM" (1989). Department of Computer Science Technical Reports. Paper 789. https://docs.lib.purdue.edu/cstech/789 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. EFFICIENT GEOMETRIC ALGORITHMS IN THE EREW·PRAM Danny Z. Chen CSD-TR-928 December 1988 (Revised October 1990) Efficient Geometric Algorithms in the EREW-PRAM (Preliminary Version) Danny Z. Chen* Department of Computer Science Purdue University West Lafayette, IN 47907. Abstract We present a technique that can be used to obtain efficient parallel algorithms in the EREW-PRAM computational model. This technique enables us to optimally solve a number of geometric problems in O(logn) time using O(n/logn) EREW-PRAM processors, where n is the input size. These problerns include: computing the convex hull oCa sorted point set in the plane, computing the convex hull oCa simple polygon, finding the kernel ofa simple polygon, triangulating a sorted point set in the plane, triangulating monotone polygons and star-shaped polygons, computing the all dominating neighbors, etc. PRAM algorithms for these problems were previously known to be optimal (i.e., O(log n) time and O(n{logn) processors) only in the CREW-PRAM, which is a stronger model than the EREW-PRAM. 1 Introduction ·This research was partially supported by the Office of Naval Research under Grants NOOOl4-84-K-0502 and N00014.86-K-0689, the National Science Foundation under Grant DCR-8451393, and the National Library of Medicine under Grant ROI-LM05118. -
CSC-758: Computational Geometry Course Description
UNIVERSITY OF NEVADA LAS VEGAS Department of Computer Science CSC-758: Computational Geometry Course Description Computational geometry deals with the development and analysis of algorithms having geometric flavor. Knowledge of elementary data structures (arrays, heaps, balanced trees, etc) and algorithmic tools (asymptotic analysis, space time complexity, divide and conquer, dynamic programming, etc) are prerequisites for this course. Student Learning Outcomes The learning outcomes for the course. • Elementary geometric methods: points, lines and polygons. Line segments intersection. Simple closed path, inclusion in a polygon, inclusion in a convex polygon, range search, point location in planar subdivision and duality. • Convex hull: Graham's scan, Jarvin's march, divide and conquer approach, on-line algorithms, approximate algorithms, convex hull of simple polygons, lower bound proofs and diameter of a point set. • Proximity: Closest pair, triangulation, divide and conquer approach for closest pair, Voronoi diagram and their properties, dual of Voronoi diagram, construction of Voronoi diagram, Euclidean minimum spanning tree, gaps and covers. • Intersections: convex polygons, polygons, star polygons, line segments, half planes and plane sweep paradigm. • Mesh generation algorithms: Delaunay triangulation, quad-trees, and Quadrangulations. • Visibility and path planning: visibility properties of polygons, visibility graphs, applications of computational geometry in robotics, shortest s-t path inside a simple polygon, shortest s-t path