Performance Bounds on Spatial Coverage Tasks by Stochastic Robotic Swarms
Performance Bounds on Spatial Coverage Tasks by Stochastic Robotic Swarms
复制标题
随机机器人群空间覆盖任务的性能界限
DOI：
10.1109/tac.2017.2747769
复制
发表时间：
2018
影响因子：
6.8
通讯作者：
S. Berman
中科院分区：
文献类型：
--
作者：
Fangbo Zhang;A. Bertozzi;Karthik Elamvazhuthi;S. Berman
本文提出了一种新的程序计算参数的机器人群体，保证覆盖性能的群体在指定的误差从目标空间分布。本文的主要贡献是分析了这一误差对两个关键参数的依赖关系：群中机器人的数量和机器人的感知半径。机器人不能相互定位或通信，它们在运动和任务切换策略中表现出随机性。我们模拟种群动力学的群作为一个对流扩散反应偏微分方程（PDE）与时间相关的对流和反应条款。我们推导出严格的界限上的目标分布和覆盖范围之间的差异，实现了基于个人和PDE模型的群体。我们使用这些界限来选择将在给定误差内实现覆盖性能的群大小和将使该误差最小化的相应机器人感测半径。我们还应用了[13]中的最优控制方法来计算机器人的速度场和任务切换率。我们验证我们的程序，通过模拟的情况下，机器人群体必须达到指定的密度的授粉活动在农田。
This paper presents a novel procedure for computing parameters of a robotic swarm that guarantee coverage performance by the swarm within a specified error from a target spatial distribution. The main contribution of this paper is the analysis of the dependence of this error on two key parameters: the number of robots in the swarm and the robot sensing radius. The robots cannot localize or communicate with one another, and they exhibit stochasticity in their motion and task-switching policies. We model the population dynamics of the swarm as an advection-diffusion-reaction partial differential equation (PDE) with time-dependent advection and reaction terms. We derive rigorous bounds on the discrepancies between the target distribution and the coverage achieved by individual-based and PDE models of the swarm. We use these bounds to select the swarm size that will achieve coverage performance within a given error and the corresponding robot sensing radius that will minimize this error. We also apply the optimal control approach from our prior work in [13] to compute the robots’ velocity field and task-switching rates. We validate our procedure through simulations of a scenario, in which a robotic swarm must achieve a specified density of pollination activity over a crop field.