移动多Agent持续监控的子模块滚动时域解决方案
A sub-modular receding horizon solution for mobile multi-agent persistent monitoring
摘要：
我们研究了一组异构移动代理的持续监测有限数量的互联地理节点的问题。我们为每个地理节点分配一个凹的和增加的奖励函数，在代理访问后重置为零。然后，我们设计的最优调度策略，哪些节点在什么时间访问，由什么代理，找到一个政策集，最大化的效用，被定义为在访问时间收集的总奖励。我们发现，这个优化问题是NP-难的，其计算复杂性的代理的数量和任务地平线的长度呈指数增长。通过证明效用函数是代理策略的单调递增和次模集函数，我们进而提出了一个具有已知最优性间隙的次优调度策略设计。为了降低构建可行搜索集的时间复杂度，并诱导鲁棒性的操作因素的变化，我们进行我们的次优策略设计在一个滚动的地平线时尚。然后，为了补偿的短视的后退地平线的方法超出了可行的政策的代理人在后退地平线上的奖励分配，我们添加了一个新的术语，我们的效用，它提供了一个衡量节点的重要性超出了后退地平线的视线。这个术语为策略设计提供了一种直觉，可以将代理引导到巡逻图上具有更高奖励的节点。最后，我们讨论了如何我们提出的算法可以实现在一个分散的方式。仿真研究证明了我们的结果。
英文摘要：
We study the problem of persistent monitoring of a finite number of inter-connected geographical nodes by a group of heterogeneous mobile agents. We assign to each geographical node a concave and increasing reward function that resets to zero after an agent's visit. Then, we design the optimal dispatch policy of which nodes to visit at what time and by what agent by finding a policy set that maximizes a utility that is defined as the total reward collected at visit times. We show that this optimization problem is NP-hard and its computational complexity increases exponentially with the number of the agents and the length of the mission horizon. By showing that the utility function is a monotone increasing and submodular set function of agents' policy, we proceed to propose a suboptimal dispatch policy design with a known optimality gap. To reduce the time complexity of constructing the feasible search set and also to induce robustness to changes in the operational factors, we perform our suboptimal policy design in a receding horizon fashion. Then, to compensate for the shortsightedness of the receding horizon approach for reward distribution beyond the feasible policies of the agents over the receding horizon, we add a new term to our utility, which provides a measure of nodal importance beyond the receding horizon's sight. This term gives the policy design an intuition to steer the agents towards the nodes with higher rewards on the patrolling graph. Finally, we discuss how our proposed algorithm can be implemented in a decentralized manner. A simulation study demonstrates our results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
