Any strategy used to distribute a robot ensemble over a set of sequential tasks is subject to inaccuracy due to robot-level uncertainties and environmental influences on the robots' behavior. We approach the problem of inaccuracy during task allocation by modeling and controlling the overall ensemble behavior. Our model represents the allocation problem as a stochastic jump process and we regulate the mean and variance of such a process. The main contributions of this paper are: Establishing a structure for the transition rates of the equivalent stochastic jump process and formally showing that this approach leads to decoupled parameters that allow us to adjust the first- and second-order moments of the ensemble distribution over tasks, which gives the flexibility to decrease the variance in the desired final distribution. This allows us to directly shape the impact of uncertainties on the group allocation over tasks. We introduce a detailed procedure to design the gains to achieve the desired mean and show how the additional parameters impact the covariance matrix, which is directly associated with the degree of task allocation precision. Our simulation and experimental results illustrate the successful control of several robot ensembles during task allocation.
翻译:用于分配一组相继任务的机器人组合的任何战略都因机器人层面的不确定性和对机器人行为的环境影响而存在不准确性。 我们在任务分配期间通过模型建模和控制整体共性行为来应对任务分配不准确问题。 我们的模式代表分配问题作为一个随机跳跃过程,我们对这种过程的平均值和差异进行调控。 本文的主要贡献是: 建立一个结构,用于对等的随机跳跃过程的过渡率,并正式表明这一方法导致分解参数,从而使我们能够对任务的共性分布的第一和第二级时间进行调和,从而使我们在所期望的最后分配中能够灵活地减少差异。 这使我们能够直接决定不确定性对任务组别分配的影响。 我们引入了详细程序来设计收益,以实现预期的平均值,并展示附加参数如何影响与任务分配精确度直接相关的交错矩阵。 我们的模拟和实验结果说明了在任务分配期间成功控制了数个机器人组合。