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Labor division for swarm robotic systems with arbitrary finite number of task types

机译:具有任意有限数量任务类型的群体机器人系统的劳动部

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In this paper we address the problem of modelling labor division when the number of task types is arbitrary finite integer for homogeneous robot swarms in the foraging scenario. The aim of labor division is to let the proportion of various tasks foraged by robotic swarm equal to the density of various tasks in the environment. The improved observation model is proposed to estimate transition rates in Markov process. Specifically instead of using a first-order differential equation which is only applicable for case study, we present a matrix differential equation to characterize the dynamics of generalised labor division. We use eigenvalue theory and matrix diagonalization method to derive the analytic solution of the proposed equation. And through analysing the steady state of individual dynamics the averaged global labor division of the swarm is deduced theoretically. The experiments verify that the new mathematical model shows excellent agreement with simulation results.
机译:在本文中,当任务类型的数量是辅助机器人在觅食方案中的均匀机器人群的任意有限整数时,我们解决了劳动划分的问题。 劳动部门的目的是让机器人群体的各种任务的比例等于环境中各种任务的密度。 提出了改进的观察模型来估算马尔可夫过程中的过渡率。 具体而不是使用仅适用于案例研究的一阶微分方程,我们提出了一种矩阵微分方程,以表征广义劳动划分的动态。 我们使用特征值理论和矩阵对角化方法来得出所提出的方程的分析解决方案。 通过分析个体动态的稳定状态,理论上推断了群的平均全球劳动力划分。 实验验证了新的数学模型与仿真结果显示出优异的一致性。

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