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Distributed algorithms for partitioning a swarm of autonomous mobile robots

机译:用于划分一大堆自主移动机器人的分布式算法

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A number of recent studies address systems of mobile autonomous robots from a distributed computing point of view. Although such systems employ robots that are relatively weak and simple (i.e., dimensionless, oblivious and anonymous), they are nevertheless expected to have strong fault tolerance capabilities as a group. This paper studies the partitioning problem, where n robots must divide themselves into k size-balanced groups, and examines the impact of common orientation on the solvability of this problem. First, deterministic crash-fault-tolerant algorithms are given for the problem in the asynchronous full-compass and semi-synchronous half-compass models, and a randomized algorithm is given for the semi-synchronous no-compass model. Next, the role of common orientation shared by the robots is examined. Necessary and sufficient conditions for the partitioning problem to be solvable are given in the different timing models. Finally, the problem is proved to be unsolvable in the no-compass synchronous model.
机译:从分布式计算的角度来看,许多最新研究针对移动自主机器人的系统。尽管这样的系统使用相对较弱且简单的机器人(即,无量纲,遗忘和匿名),但仍希望它们具有整体强大的容错能力。本文研究了分区问题,其中n个机器人必须将自己划分为k个大小平衡的组,并研究了通用方向对该问题可解性的影响。首先,在异步全罗盘和半同步半罗盘模型中针对该问题给出了确定性的容错算法,而对于半同步无罗盘模型则给出了随机算法。接下来,检查机器人共享的共同朝向的作用。在不同的时序模型中给出了解决分区问题的充要条件。最后,证明该问题在无罗盘同步模型中是无法解决的。

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