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A sub-modular receding horizon approach to persistent monitoring for a group of mobile agents over an urban area

机译：一组城市地区移动代理持续监测的子模块后退地平线方法

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We consider the problem of persistent monitoring of a finite number of interconnected geographical nodes for event detection via a group of heterogeneous mobile agents. We assume that the probability of the events occurring at the geographical points of interest follow known Poisson processes. We tie a utility function to the probability of detecting an event in each point of interest and use it to incentivize the agents to visit the geographical nodes with higher probability of event occurrence. We show that the design of an optimal monitoring policy that specifies the sequence of the geographical nodes and time of visit of those nodes for each mobile agent so that the utility of event detection over a mission horizon is maximized is an NP-hard problem. To reduce the time complexity of constructing the feasible set of the optimal approach and also to induce robustness to changes in event occurrence and other operational factors, we consider a receding horizon approach. We note that, with the number of agents growing, the cost of finding the optimal path grows exponentially even with shortened horizon. To overcome this issue, we introduce a sub-modular optimization approach that has a polynomial time complexity and also comes with a known sub-optimality lower bound. We demonstrate our results through simulations.
机译：我们考虑通过一组异构移动代理对事件检测有限数量的互联地理节点的持久监控问题。我们假设在地理兴趣点发生的事件的可能性遵循已知的泊松过程。我们将实用程序函数与检测每个兴趣点中的事件的概率绑定，并使用它来激励代理商访问具有更高概率发生的地理节点。我们展示了最佳监控策略的设计，该策略指定每个移动代理的那些节点的地理节点和访问时间的序列，以便在任务地平线上最大化事件检测的效用是一个NP-难的问题。为了减少构建可行性方法的可行性集的时间复杂性，并且还诱导事件发生和其他操作因素的变化的稳健性，我们考虑解除的地平线方法。我们注意到，随着代理商的数量不断增长，即使在缩短的地平线上，找到最佳路径的成本也会呈指数增长。为了克服这个问题，我们介绍了一种具有多项式时间复杂度的子模块化优化方法，并且还具有已知的次级最优性下限。我们通过模拟展示了我们的结果。

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