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Two Convergence Problems for Robots on Graphs

机译:图上机器人的两个收敛问题

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The class of robot convergence tasks has been shown to capture fundamental aspects of fault-tolerant computability. A set of asynchronous robots that may fail by crashing, start from unknown places in some given space, and have to move towards positions close to each other. In this article, we study the case where the space is uni-dimensional, modeled as a graph G. In graph convergence, robots have to end up on one or two vertices of the same edge. We consider also a variant of robot convergence on graphs, edge covering, where additionally it is required that not all robots end up on the same vertex. Remarkably, these two similar problems have very different computability properties, related to orthogonal fundamental issues of distributed computations: agreement and symmetry breaking. We characterize the graphs on which each of these problems is solvable, and give optimal time algorithms for the solvable cases. Although the results can be derived from known general topology theorems, the presentation serves as a self-contained introduction to the topology approach to distributed computing, and yields concrete algorithms and impossibility results.
机译:机器人收敛任务的类别已显示出可容错可计算性的基本方面。一组异步机器人可能会因崩溃而崩溃,从某个给定空间中的未知位置开始,并且必须朝彼此靠近的位置移动。在本文中,我们研究的空间是一维的,建模为图G。在图收敛中,机器人必须终止于同一边的一个或两个顶点上。我们还考虑了机器人收敛于图形,边沿覆盖的一种变体,此外,还要求并非所有的机器人都以相同的顶点结尾。值得注意的是,这两个相似的问题具有非常不同的可计算性,这与分布式计算的正交基本问题有关:一致性和对称性破坏。我们刻画了可以解决所有这些问题的图形,并针对可解决的情况给出了最佳时间算法。尽管可以从已知的一般拓扑定理中得出结果,但该演示文稿是对分布式计算的拓扑方法的独立介绍,并给出了具体的算法和不可能的结果。

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