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Decentralized control of mobile robotic sensors: Vicsek's model with cosine similarity measure

机译:移动机器人传感器的分散控制:Vicsek的余弦相似度测量模型

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We investigate the control of a team of randomly heading mobile robots to reach asymptotic consensus for almost sure. Vicsek's model is based on the state of an agent and its neighbours. The inspiration from Vicsek's model leads to a locally computed nearest neighbour rule. In the nearest neighbour rule, a mobile robotic sensor always assigns an equal weighting factor to itself and every nearest neighbour coming under its communication range. We consider Salton's cosine similarity measure among the nearest neighbours of a mobile robotic sensor and the nearest neighbours are not always equally weighted. Consequently, some of the nearest neighbour mobile robots become influential and play an effective role in reaching a consensus value. After introducing the similarity measure among the nearest neighbours, we have compared the obtained simulation results with that of simple nearest neighbour rule. We conclude that the algorithm becomes fast after including the similarity measure among the nearest neighbours of a mobile robot.
机译:我们调查一支随机标题移动机器人团队的控制,以实现几乎肯定的渐近共识。 Vicsek的模型基于代理人及其邻居的状态。 Vicsek模型的灵感导致局部计算的最近邻居规则。在最近的邻居规则中,移动机器人传感器始终为其自身分配相同的加权因子,并且每个最近的邻居在其通信范围内。我们认为,移动机器人传感器的最近邻居的Salton的余弦相似度测量,最近的邻居并不总是同样加权。因此,一些最近的邻居移动机器人在达成共识值时变得有效的作用。在引入最近邻居之间的相似性度量之后,我们将获得的模拟结果与简单的最近邻居规则进行了比较。我们得出结论,在包括移动机器人的最近邻居之间的相似性度量之后,该算法变得快速。

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