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首页> 外文期刊>Journal of Intelligent & Robotic Systems: Theory & Application >Multi-Robot Patrolling with Sensing Idleness and Data Delay Objectives
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Multi-Robot Patrolling with Sensing Idleness and Data Delay Objectives

机译:多机器人巡逻,具有传感闲置和数据延迟目标

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摘要

Multi-robot patrolling represents a fundamental problem for many monitoring and surveillance applications and has gained significant interest in recent years. In patrolling, mobile robots repeatedly travel through an environment, capture sensor data at certain sensing locations and deliver this data to the base station in a way that maximizes the changes of detection. Robots move on tours, exchange data when they meet with robots on neighboring tours and so eventually deliver data to the base station. In this paper we jointly consider two important optimization criteria of multi-robot patrolling: (i) idleness, i.e. the time between consecutive visits of sensing locations, and (ii) delay, i.e. the time between capturing data at the sensing location and its arrival at the base station. We systematically investigate the effect of the robots' moving directions along their tours and the selection of meeting points for data exchange. We prove that the problem of determining the movement directions and meeting points such that the data delay is minimized is NP-hard. For this purpose, we define a structure called tour graph which models the neighborhood of the tours defined by potential meeting points. We propose two heuristics that are based on a shortest-path-search in the tour graph. We provide a simulation study which shows that the cooperative approach can outperform an uncooperative approach where every robot delivers the captured data individually to the base station. Additionally, the experiments show that the heuristic which is computational more expensive performs slightly better on average than the less expensive heuristic in the considered scenarios.
机译:多机器人巡逻代表了许多监测和监测应用的根本问题,近年来取得了重大兴趣。在巡逻时,移动机器人反复通过环境行进,在某些感应位置捕获传感器数据,并以最大化检测变化的方式将该数据传送到基站。机器人在邻近旅行中与机器人遇到时交换数据,最终将数据交付给基站。在本文中,我们共同考虑了多机器人巡逻的两个重要优化标准:(i)闲置,即传感位置的连续访问之间的时间,(ii)延迟,即捕获传感位置的数据之间的时间及其到达在基站。我们系统地调查机器人移动方向沿着他们的旅游的影响以及为数据交换的聚会点的选择。我们证明了确定移动方向和会议点,使得数据延迟最小化的问题是NP-HARD。为此目的,我们定义了一个名为Train图表的结构,其中模拟了由潜在会议点定义的巡回群岛的附近。我们提出了两个基于旅游图中最短路径搜索的启发式。我们提供了一种模拟研究,表明协作方法可以优于一个不合作的方法,其中每个机器人将捕获的数据分别传递到基站。此外,实验表明,在所考虑的场景中的较低昂贵的启发式中,计算更昂贵的启发式稍微更好地执行。

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