Optimal Convex Hull Formation on a Grid by Asynchronous Robots with Lights

Rory Hector, R. Vaidyanathan, Gokarna Sharma, J. Trahan
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引用次数: 8

Abstract

We consider the distributed setting of n autonomous mobile robots that operate in Look-Compute-Move (LCM) cycles and communicate with other robots using a constant number of colored lights (the robots with lights model). We assume obstructed visibility where a robot cannot see another robot if a third robot is positioned between them on the straight line connecting them. In addition, we consider a grid-based terrain embedded in the 2-dimensional Euclidean plane that restricts each robot movement to one of the four neighboring grid points from its current position. This grid setting is a natural discretization of the 2-dimensional real plane and extends the robot swarm model in directions of greater applicability. The Convex Hull Formation problem is to relocate the n robots (starting at arbitrary, but distinct, initial positions) so that each robot is positioned on a vertex of a convex hull. In this paper, we provide two asynchronous algorithms for Convex Hull Formation, both using a constant number of colors. Key measures of the algorithms’ performance include the time taken and the space occupied (measured as the perimeter of the smallest rectangle enclosing the convex hull formed). The first O(max{n2, D})-time and O(n2)-perimeter algorithm serves to introduce key ideas, where D is the diameter of the initial configuration. The second algorithm runs in $O\left( {\max \left\{ {{n^{\frac{3}{2}}},D} \right\}} \right)$ time with a perimeter of $O\left( {{n^{\frac{3}{2}}}} \right)$. We also prove lower bounds of $\Omega \left( {{n^{\frac{3}{2}}}} \right)$ for both the time and perimeter for any Convex Hull Formation algorithm; that is, we establish our second algorithm as optimal in both time and perimeter.
带光的异步机器人在网格上的最优凸壳队形
我们考虑n个自主移动机器人的分布式设置,这些机器人以Look-Compute-Move (LCM)周期运行,并使用恒定数量的彩灯(带灯的机器人模型)与其他机器人通信。我们假设一个机器人看不到另一个机器人,如果第三个机器人在连接它们的直线上被放置在它们之间。此外,我们考虑了嵌入在二维欧几里得平面中的基于网格的地形,该地形将每个机器人的运动限制在其当前位置的四个相邻网格点之一。这种网格设置是对二维真实平面的自然离散化,将机器人群模型向适用性更强的方向进行了扩展。凸壳形成问题是重新定位n个机器人(从任意但不同的初始位置开始),以便每个机器人都位于凸壳的一个顶点上。在本文中,我们提供了两种异步的凸包生成算法,它们都使用了常数的颜色。算法性能的关键指标包括所花费的时间和所占用的空间(以包围凸包形成的最小矩形的周长来衡量)。第一个O{(maxn2, D)}时间和O(n2)周长算法用于介绍关键思想,其中D是初始配置的直径。第二种算法运行时间为$O\left( {\max \left\{ {{n^{\frac{3}{2}}},D} \right\}} \right)$,周长为$O\left( {{n^{\frac{3}{2}}}} \right)$。我们还证明了任意凸包生成算法的时间和周长的下界$\Omega \left( {{n^{\frac{3}{2}}}} \right)$;也就是说,我们建立了第二种算法在时间和周长上都是最优的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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