Optimal Periodic Multi-Agent Persistent Monitoring of a Finite Set of Targets with Uncertain States
Optimal Periodic Multi-Agent Persistent Monitoring of a Finite Set of Targets with Uncertain States
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DOI：
10.23919/acc45564.2020.9147376
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Samuel C. Pinto;S. Andersson;J. Hendrickx;C. Cassandras
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文献类型：
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作者：
Samuel C. Pinto;S. Andersson;J. Hendrickx;C. Cassandras
我们调查的问题，持续监测一组有限的目标与内部状态的演变与线性随机动态使用一组有限的移动的代理。我们从无限视野的角度来处理这个问题，寻找代理的周期性运动时间表。在线性动力学和噪声分布的一些标准假设下，最佳估计器是卡尔曼-布西滤波器。它示出，当代理被约束为只移动在一条线上，他们可以看到在最多一个目标的时间，最优的运动策略是这样的代理总是要么移动最大速度或居住在一个固定的位置。这种形式的周期轨迹承认有限的参数化，我们展示了如何计算的随机梯度估计的性能相对于使用无穷小扰动分析的轨迹定义的参数。一个梯度下降计划被用来计算局部最优参数。这种方法使我们能够处理一个非常长的持续监测地平线使用少量的参数。
We investigate the problem of persistently monitoring a finite set of targets with internal states that evolve with linear stochastic dynamics using a finite set of mobile agents. We approach the problem from the infinite-horizon perspective, looking for periodic movement schedules for the agents. Under linear dynamics and some standard assumptions on the noise distribution, the optimal estimator is a Kalman-Bucy filter. It is shown that when the agents are constrained to move only over a line and that they can see at most one target at a time, the optimal movement policy is such that the agent is always either moving with maximum speed or dwelling at a fixed position. Periodic trajectories of this form admit finite parameterization, and we show how to compute a stochastic gradient estimate of the performance with respect to the parameters that define the trajectory using Infinitesimal Perturbation Analysis. A gradient-descent scheme is used to compute locally optimal parameters. This approach allows us to deal with a very long persistent monitoring horizon using a small number of parameters.