Distributed target identification in robotic swarms
Distributed target identification in robotic swarms
复制标题
机器人群中的分布式目标识别
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
H. Bülthoff
中科院分区：
文献类型：
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作者：
P. Stegagno;Caterina Massidda;H. Bülthoff
识别共同动作目标的能力对于开发能够与环境交互的多机器人团队至关重要。在大多数现有系统中，识别是基于颜色编码、形状识别或复杂视觉系统单独进行的。这些方法通常对对象采取广泛的观点，并对其进行整体观察。这种假设有时在实践中很难实现，特别是在由大量传感和计算能力有限的小型机器人组成的群体系统中。在本文中，我们提出了一种使用朴素贝叶斯分类器的分布式版本的低信息空间分布式传感器的异构群进行目标识别的方法。尽管个体感知能力有限，但如果机器人合作共享它们从有限的角度收集到的信息，贝叶斯定律的递归应用就可以进行识别。仿真结果显示了该方法的有效性，突出了所开发算法的一些特性。
The ability to identify the target of a common action is fundamental for the development of a multi-robot team able to interact with the environment. In most existing systems, the identification is carried on individually, based on either color coding, shape identification or complex vision systems. Those methods usually assume a broad point of view over the objects, which are observed in their entirety. This assumption is sometimes difficult to fulfill in practice, and in particular in swarm systems, constituted by a multitude of small robots with limited sensing and computational capabilities. In this paper, we propose a method for target identification with a heterogeneous swarm of low-informative spatially-distributed sensors employing a distributed version of the naive Bayes classifier. Despite limited individual sensing capabilities, the recursive application of the Bayes law allows the identification if the robots cooperate sharing the information that they are able to gather from their limited points of view. Simulation results show the effectiveness of this approach highlighting some properties of the developed algorithm.