Abstract Unit square visibility graphs (USV) are described by axis-parallel visibility between unit squares placed in the plane. If the squares are required to be placed on integer grid coordinates, then USV become unit square grid visibility graphs (USGV), an alternative characterisation of the well-known rectilinear graphs. We extend known combinatorial results for USGV and we show that, in the weak case (i.e., visibilities do not necessarily translate into edges of the represented combinatorial graph), the area minimisation variant of their recognition problem is $${{\,\mathrm{{\textsf{N}}{\textsf{P}}}\,}}$$ N P -hard. We also provide combinatorial insights with respect to USV, and as our main result, we prove their recognition problem to be $${{\,\mathrm{{\textsf{N}}{\textsf{P}}}\,}}$$ N P -hard, which settles an open question.

Abstract We explore the optimization of closed space‐filling curves under orientation objectives. By solidifying material along the closed curve, solid layers of 3D prints can be manufactured in a single continuous extrusion motion. The control over orientation enables the deposition to align with specific directions in different areas, or to produce a locally uniform distribution of orientations, patterning the solidified volume in a precisely controlled manner. Our optimization framework proceeds in two steps. First, we cast a combinatorial problem, optimizing Hamiltonian cycles within a specially constructed graph. We rely on a stochastic optimization process based on local operators that modify a cycle while preserving its Hamiltonian property. Second, we use the result to initialize a geometric optimizer that improves the smoothness and uniform coverage of the cycle while further optimizing for alignment and orientation objectives.

Unit square (grid) visibility graphs (USV and USGV, resp.) are described by axis-parallel visibility between unit squares placed (on integer grid coordinates) in the plane. We investigate combinatorial properties of these graph classes and the hardness of variants of the recognition problem, i.e., the problem of representing USGV with fixed visibilities within small area and, for USV, the general recognition problem.

We introduce a new, simple method for answering reverse and nearest neighbor queries for moving objects, e.g., mobile players in a multiplayer game, where a trajectory is known to the system for each object, at least in the short-term. See [Rahmati 2014].

Regret minimizing sets are a recent approach to representing a dataset D by a small subset R of size r of representative data points. The set R is chosen such that executing any top-1 query on R rather than D is minimally perceptible to any user. However, such a subset R may not exist, even for modest sizes, r. In this paper, we introduce the relaxation to k -regret minimizing sets, whereby a top-1 query on R returns a result imperceptibly close to the top- k on D. We show that, in general, with or without the relaxation, this problem is NP-hard. For the specific case of two dimensions, we give an efficient dynamic programming, plane sweep algorithm based on geometric duality to find an optimal solution. For arbitrary dimension, we give an empirically effective, greedy, randomized algorithm based on linear programming. With these algorithms, we can find subsets R of much smaller size that better summarize D , using small values of k larger than 1.

This paper presents a kinetic data structure (KDS) for solutions to the all nearest neighbors problem and the closest pair problem in the plane. For a set P of n moving points where the trajectory of each point is an algebraic function of constant maximum degree s, our kinetic algorithm uses O(n) space and O(n log n) preprocessing time, and processes O(n2β22s+2(n)log n) events with total processing time O(n2β22s+2(n)log2 n), where βs(n) is an extremely slow-growing function. In terms of the KDS performance criteria, our KDS is efficient, responsive (in an amortized sense), and compact.

In this paper, we examine the merit of a simple and lightweight stochastic transmission strategy based on the ALOHA protocol for underwater wireless sensor networks (UWSNs). We use a stochastic scheduling approach in which time is slotted, and each network component transmits according to some probability during each slot. We present objective functions for assigning the transmission probabilities that are aimed at optimizing network performance with respect to the overall network latency and the overall network reliability. We show that there is an easily distributed heuristic policy based on local network density that works well in practice. We also evaluate our approach using numerical simulations. The evaluation results show that even without using explicit control signaling, our lightweight stochastic scheduling method is effective for data transmission in underwater sensor networks.

In this paper, we present an algorithm for simultaneously refining a probability distribution function (PDF) for the pose of a sensor network (i.e. the locations of the sensors), and inferring the spatial variations of measured environmental parameters. Our approach iteratively refines a network pose PDF by assuming that environmental parameters vary smoothly. Both our physical experiments, which sensed wireless signal strength as the environmental variable, and our numerical simulations demonstrate that the approach has promise.

This paper surveys parameterized complexity results for hard geometric algorithmic problems. It includes fixed-parameter tractable problems in graph drawing, geometric graphs, geometric covering and several other areas, together with an overview of the algorithmic techniques used. Fixed-parameter intractability results are surveyed as well. Finally, we give some directions for future research.

In this note we describe the contents of a video illustrating analgorithm for computing the 3D visibility skeleton ofa set of disjoint convex polytopes. The video can be foundat http://www.cs.mcgill.ca/~lzhang15/video/ with file name socg07visidemo.mov.

This paper presents a novel directional morphometry method for surfaces using first order derivatives. Non-directional surface morphometry has been previously used to detect regions of cortical atrophy using brain MRI data. However, evaluating directional changes on surfaces requires computing gradients to obtain a full metric tensor. Non-directionality reduces the sensitivity of deformationbased morphometry to area-preserving deformations. By proposing a method to compute directional derivatives, this paper enables analysis of directional deformations on surfaces. Moreover, the proposed method exhibits improved numerical accuracy when evaluating mean curvature, compared to the so-called cotangent formula. The directional deformation of folding patterns was measured in two groups of surfaces and the proposed methodology allowed to detect morphological differences that were not detected using previous non-directional morphometry. The methodology uses a closed-form analytic formalism rather than numerical approximation and is readily generalizable to any application involving surface deformation.

Motivated by visibility problems in three dimensions, we investigate the complexity and construction of the set of tangent lines in a scene of three-dimensional polyhedra. We prove that the set of lines tangent to four possibly intersecting convex polyhedra in $\mathbb{R}^3$ with a total of n edges consists of $\Theta(n^2)$ connected components in the worst case. In the generic case, each connected component is a single line, but our result still holds for arbitrarily degenerate scenes. More generally, we show that a set of k possibly intersecting convex polyhedra with a total of n edges admits, in the worst case, $\Theta(n^2k^2)$ connected components of maximal free line segments tangent to at least four polytopes. Furthermore, these bounds also hold for possibly occluded lines rather than maximal free line segments. Finally, we present an $O(n^2 k^2 \log n)$ time and $O(nk^2)$ space algorithm that, given a scene of k possibly intersecting convex polyhedra, computes all the minimal free line segments that are tangent to any four of the polytopes and are isolated transversals to the set of edges they intersect; in particular, we compute at least one line segment per connected component of tangent lines.

Minimum distance localization is the problem of finding the shortest possible path for a robot to eliminate ambiguity regarding its position in the environment. We consider the problem of minimum distance localization in self-similar environments, where the robot's sensor has limited visibility, and describe two randomized algorithms that solve the problem. Our algorithms reduce the risk of requiring impractical observations and solve the problem without excessive computation. Our results are validated using numerical simulations.

We prove that the lines tangent to four possibly intersecting convex polyhedra in ℝ3 with n edges in total form Θ(n2) connected components in the worst case. In the generic case, each connected component is a single line, but our result still holds for arbitrary degenerate scenes. More generally, we show that a set of kconvex polyhedra with a total of n edges admits, in the worst case, Θ(n2k2)connected components of (possibly occluded) lines tangent to any four of these polyhedra. We also show a lower bound of Ω(n2k2) on the number of non-occluded maximal line segments tangent to any four of these k convex polyhedra.

info:eu-repo/semantics/published

We consider the problem of reconfiguring a linkage of rigid straight segments from a given start to a given target position with a continuous nonintersecting motion. The problem is nontrivial even for trees in two dimensions since it is known that not all configurations can be reconfigured to a straight position. We show that deciding reconfigurability for trees in two dimensions and for chains in three dimensions is PSPACE-complete.

We present new lowest energy configurations for several large benchmark problems for the two-dimensional hydrophobic-hydrophilic model. We found these solutions with a generic implementation of tabu search using an apparently novel set of transformations that we call pull moves. Our experiments show that our algorithm can find these best solutions in 3 to 14 hours, on average. Pull moves appear quite effective and may also be useful for other local search algorithms for the problem. Additionally, we prove that pull moves are complete; that is, any pair of valid configurations are mutually reachable through a sequence of pull moves. Our implementation was developed with the Human-Guided Search (HuGS) middleware, which allows rapid development of interactive optimization systems.

Let B be a point robot moving in the plane, whose path is constrained to have curvature at most 1, and let $\poly$ be a convex polygon with n vertices. We study the collision-free, optimal path-planning problem for B moving between two configurations inside $\poly$. (A configuration specifies both a location and a direction of travel.) We present an O(n 2 log n) time algorithm for determining whether a collision-free path exists for B between two given configurations. If such a path exists, the algorithm returns a shortest one. We provide a detailed classification of curvature-constrained shortest paths inside a convex polygon and prove several properties of them, which are interesting in their own right. For example, we prove that any such shortest path is comprised of at most eight segments, each of which is a circular arc of unit radius or a straight-line segment. Some of the properties are quite general and shed some light on curvature-constrained shortest paths amid obstacles.

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We consider the problem of localizing a robot in a known environment modeled by a simple polygon P. We assume that the robot has a map of P but is placed at an unknown location inside P. From its initial location, the robot sees a set of points called the visibility polygon V of its location. In general, sensing at a single point will not suffice to uniquely localize the robot, since the set H of points in P with visibility polygon V may have more than one element. Hence, the robot must move around and use range sensing and a compass to determine its position (i.e., localize itself). We seek a strategy that minimizes the distance the robot travels to determine its exact location. We show that the problem of localizing a robot with minimum travel is NP-hard. We then give a polynomial time approximation scheme that causes the robot to travel a distance of at most (k - 1)d, where k = |H|, which is no greater than the number of reflex vertices of P, and d is the length of a minimum length tour that would allow the robot to verify its true initial location by sensing. We also show that this bound is the best possible.

We show that deciding whether a tree can be drawn in the plane so that it is the Euclidean minimum spanning tree of the locations of its vertices is NP-hard.

The link center of a simple polygon P is the set of points x inside P at which the maximal link-distance from x to any other point in P is minimized, where the link distance between two points x, y inside P is defined as the smallest number of straight edges in a polygonal path inside P connecting x to y. We prove several geometric properties of the link center and present an algorithm that calculates this set in time Ο (n2), where n is the number of sides of P. We also give an Ο(n log n) algorithm for finding a point x in an approximate link center, namely the maximal link distance from x to any point in P is at most one more than the value attained from the link center.

The mover’s problem is the following: can an object in 3-dimensional space be moved from one given position to another while avoiding obstacles? It is known that the general version of this problem involving objects with movable joints is PSPACE hard, even for a simple tree-like structure moving in a 3-dimensional region. In this paper, we investigate a 2-dimensional mover’s problem in which the object is a robot arm with an arbitrary number of joints. In particular, we give a polynomial time algorithm for moving an arm confined within a circle from one given configuration to another. We also give a polynomial time algorithm for moving the arm from its initial position to a position in which the end of the arm reaches a given point within the circle. Finally, we show that 148 circles suffice to cover the boundary of the reachable region of a joint in an arm enclosed in a circle and that the boundary can be computed in polynomial time.

This paper is motivated by questions concerning the planning of motion in robotics. In particular, it is concerned with the motion of planar linkages from the complexity point of view. There are two main results. First, a planar linkage can be constrained to stay inside a bounded region whose boundary consists of straight lines by the addition of a polynomial number of new links. Second, the question of whether a planar linkage in some initial configuration can be moved so that a designated joint reaches a given point in the plane is PSPACE-hard.

The classical mover's problem is the following: can a rigid object in 3-dimensional space be moved from one given position to another while avoiding obstacles? It is known that a more general version of this problem involving objects with movable joints is PSPACE-complete, even for a simple tree-like structure. In this paper, we investigate a 2-dimensional mover's problem in which the object being moved is a robot arm with an arbitrary number of joints. We reduce the mover's problem for arms constrained to move within bounded regions whose boundaries are made up of straight lines to the mover's problem for a more complex linkage that is not constrained. We prove that the latter problem is PSPACE-hard even in 2-dimensional space and then turn to special cases of the mover's problem for arms. In particular, we give a polynomial time algorithm for moving an arm confined within a circle from one given configuration to another. We also give a polynomial time algorithm for moving the arm from its initial position to a position in which the end of the arm reaches a given point within the circle.