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Vision-based multi-agent cooperative target search

机译:基于视觉的多代理合作目标搜索

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This paper addresses vision-based cooperative search for multiple mobile ground targets by a group of unmanned aerial vehicles (UAVs) with limited sensing and communication capabilities. The airborne camera on each UAV has a limited field of view and its target discriminability varies as a function of altitude. First, a general target detection probability model is built based on the physical imaging process of a camera. By dividing the whole surveillance region into cells, a probability map can be formed for each UAV indicating the probability of target existence within each cell. Then, we propose a distributed probability map updating model which includes the fusion of measurement information, information sharing among neighboring agents, information decaying and transmission due to environmental changes such as the target movement. Furthermore, we formulate the target search problem by multiple agents as a cooperative coverage control problem by optimizing the collective coverage area and the detection performance. The proposed map updating model and the cooperative control scheme are distributed, i.e., assuming that each agent only communicates with its neighbors within its communication range. Finally, the effectiveness of the proposed algorithms is illustrated by simulation.
机译:本文通过具有有限的感测和通信能力,解决了基于视觉的基于移动地面目标的合作搜索多个移动地形目标。每个UAV上的空中摄像头具有有限的视野,其目标可辨别性随着高度的函数而变化。首先,基于相机的物理成像过程构建一般目标检测概率模型。通过将整个监视区域划分为细胞,可以针对每个单元内的每个UAV形成概率图,指示每个单元内的目标存在的概率。然后,我们提出了一种分布式概率图更新模型,其包括测量信息的融合,相邻代理之间的信息共享,由于诸如目标运动的环境变化而导致的相邻代理,信息衰减和传输。此外,通过优化集体覆盖面积和检测性能,我们通过多个代理作为协作覆盖控制问题的目标搜索问题。所提出的地图更新模型和协作控制方案是分布式的,即,假设每个代理只与其通信范围内的邻居通信。最后,通过模拟说明了所提出的算法的有效性。

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