Elmar Langetepe

Voronoi Diagram for services neighboring a highway

We are given a transportation line where displacements happen at a bigger speed than in the rest of the plane. A shortest time path is a path between two points which takes less than or equal time to any other. We consider the time to follow a shortest time path to be the time distance between the two points. In this paper, we give a simple algorithm for computing the Time Voronoi Diagram, that is, the Voronoi Diagram of a set of points using the time distance.

Smallest Color-Spanning Objects

Motivated by questions in location planning, we show for a set of colored point sites in the plane how to compute the smallest-- by perimeter or area--axis-parallel rectangle and the narrowest strip enclosing at least one site of each color.

Abstract Voronoi diagrams revisited

Abstract Voronoi diagrams [R. Klein, Concrete and Abstract Voronoi Diagrams, Lecture Notes in Computer Science, vol. 400, Springer-Verlag, 1987] were designed as a unifying concept that should include as many concrete types of diagrams as possible. To ensure that abstract Voronoi diagrams, built from given sets of bisecting curves, are finite graphs, it was required that any two bisecting curves intersect only finitely often; this axiom was a cornerstone of the theory. In [A.G. Corbalan, M. Mazon, T. Recio, Geometry of bisectors for strictly convex distance functions, International Journal of Computational Geometry and Applications 6 (1) (1996) 45-58], Corbalan et al. gave an example of a smooth convex distance function whose bisectors have infinitely many intersections, so that it was not covered by the existing AVD theory. In this paper we give a new axiomatic foundation of abstract Voronoi diagrams that works without the finite intersection property.

On the Competitive Complexity of Navigation Tasks

A strategy S solving a navigation task T is called competitive with ratio r if the cost of solving any instance t of T does not exceed r times the cost of solving t optimally. The competitive complexity of task T is the smallest possible value r any strategy S can achieve. We discuss this notion, and survey some tasks whose competitive complexities are known. Then we report on new results and ongoing work on the competitive complexity of exploring an unknown cellular environment.

A fast algorithm for approximating the detour of a polygonal chain

Let C be a simple polygonal chain of n edges in the plane, and let p and q be two arbitrary points on C. The detour of C on (p, q) is defined to be the length of the subchain of C that connects p with q, divided by the Euclidean distance between p and q. Given an e > 0, we compute in time O(1/e n log n) a pair of points on which the chain makes a detour at least 1/(1 + e) times the maximum detour.

Self-Approaching Curves

We present a new class of curves which are self-approaching in the following sense. For any three consecutive points a, b, c on the curve the point b is closer to c than a to c. This is a generalisation of curves with increasing chords which are self-approaching in both directions. We show a tighter upper bound of 5.3331... for the length of a self-approaching curve over the distance between its endpoints.

Exploring simple grid polygons

We investigate the online exploration problem of a short-sighted mobile robot moving in an unknown cellular room without obstacles. The robot has a very limited sensor; it can determine only which of the four cells adjacent to its current position are free and which are blocked, i.e., unaccessible for the robot. Therefore, the robot must enter a cell in order to explore it. The robot has to visit each cell and to return to the start. Our interest is in a short exploration tour, i.e., in keeping the number of multiple cell visits small. For abitrary environments without holes we provide a strategy producing tours of length

Generalized self-approaching curves

S \leq C + \frac{1}{2} E -- 3

How to find a point on a line within a fixed distance

, where C denotes the number of cells – the area – , and E denotes the number of boundary edges – the perimeter – of the given environment. Further, we show that our strategy is competitive with a factor of

Competitive Online Approximation of the Optimal Search Ratio

\frac43